Semantic Matchmaking

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Semantic matchmaking is the process of matching data or services based on their meaning and context rather than mere syntactic similarity. It utilizes semantic web technologies and ontologies to enhance the discovery and integration of information, enabling more accurate and relevant results in various applications such as information retrieval and service composition.

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Key research themes

1. How can semantic matchmaking improve personalized service recommendation in contextual and dynamic environments?

This research theme focuses on leveraging semantic description and hybrid matchmaking methods to provide personalized, context-aware recommendations in dynamic domains such as Ambient Assisted Living (AAL) and personalized media consumption. It addresses the challenge of effectively matching user profiles—encompassing behavioral, factual, and contextual data—with relevant services or content by integrating rule-based reasoning with statistical methods. The goal is to enhance user satisfaction, safety, and autonomy by recommending the most appropriate services while reducing information overload, especially for vulnerable populations such as cognitively impaired elderly users.

A hybrid matchmaking approach in the ambient assisted living domain

by Konstantinos Votis

2022, Universal Access in the Information Society

Key finding: Presents a hybrid matchmaking system combining semantic rule-based inference with statistical analysis of user profiles and preferences to propose personalized ambient assisted living services. Directly contributes to the... Read more

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Maximum and Sorted Cache Occupation Using Array Padding

by OSCAR ALBERTO ESPINOZA PLATA

2023, 2007 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation

Key finding: Shows that traditional syntactic personalization methods are limited and proposes semantic reasoning approaches based on ontology annotations and spreading activation techniques to infer deeper semantic associations between... Read more

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Unique stable matchings

by Gregory Gutin

2023, Games and Economic Behavior

Key finding: Though focused on stable matching theory, formalizes the concept of acyclicity in preference relations after iterated deletion of unattractive alternatives, which underlies the uniqueness of stable matchings. This theoretical... Read more

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A Flexible Framework for Adaptive Knowledge Retrieval and Fusion for Kiosk Systems and Mobile Clients

by Simon Bergweiler

2016

Key finding: Introduces a middleware framework enabling flexible, semantic integration and fusion of heterogeneous knowledge sources through unified queries, harmonizing varied data to serve context-aware client applications. This... Read more

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2. What inference mechanisms and logical frameworks optimize semantic matchmaking performance in constrained or resource-limited environments?

This research area investigates computationally efficient, logic-based, and non-standard inference techniques tailored to semantic matchmaking, especially for applications such as the Semantic Web of Things (SWoT). It emphasizes reasoning over moderately expressive Description Logics and the development of lightweight matchmakers and reasoners to perform quick, context-aware matchmaking on devices with limited resources. The focus is on balancing expressive power and computational feasibility to deliver timely and accurate matchmaking results in pervasive environments.

Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

by agnese pinto

2022, Innovations, Developments, and Applications of Semantic Web and Information Systems

Key finding: Develops Mini-ME, a compact reasoning engine supporting ALN Description Logic, that efficiently executes both standard and advanced inference services for semantic matchmaking on resource-constrained mobile platforms.... Read more

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Semantic interactive ontology matching: synergistic combination of techniques to improve the set of candidate correspondences

by Jomar Silva

2022

Key finding: Proposes ALINSyn, an interactive ontology matching approach that employs semantic and structural techniques to refine candidate correspondences for expert validation, significantly improving alignment quality and convergence... Read more

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OpenKnowledge Deliverable 3.1.: Dynamic ontology matching: a survey

by Fausto Giunchiglia

2023

Key finding: Surveys dynamic ontology matching methods emphasizing run-time matching requirements in open, decentralized applications, highlighting approximate, interactive, and community-driven approaches to achieve timely, adaptable... Read more

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3. How can semantic matchmaking frameworks address large-scale query result challenges, including plethoric answers, by identifying and managing failure-inducing query components?

This trending research addresses the practical problem in semantic matchmaking and information retrieval where queries yield excessively large, often unmanageable, result sets (‘plethoric answers’). The focus is on developing cooperative methods that identify minimal failing subqueries (MFIS) and maximal succeeding subqueries (XSS), isolating query parts responsible for excessive output. Such techniques aim to aid both users and automated systems in refining queries, thus improving retrieval accuracy, result relevance, and overall user experience in semantic matchmaking contexts.

A cooperative treatment of the plethoric answers problem in RDF

by Allel HADJALI

2024, Knowledge and Information Systems

Key finding: Introduces the concepts of Minimal Failure Inducing Subqueries (MFIS) and Maximal Succeeding Subqueries (XSS) to pinpoint causes of plethoric query answers and provide interactive and automated query reformulation strategies.... Read more

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Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

by amel Boustil

2024

Key finding: Presents a possibilistic logic framework to express and process qualitative user preferences as symbolic weights in SPARQL queries, allowing more flexible ranking and filtering of semantic query results. This approach... Read more

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Online dating recommendations: matching markets and learning preferences

by David Jensen

2016

Key finding: Develops a two-sided matching market model combining preference learning via Latent Dirichlet Allocation (LDA) with maximization of successful matches, significantly improving recommendation precision and reply rates.... Read more

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All papers in Semantic Matchmaking

Jotsna Molly Rajan et al. / International Journal on Computer Science and Engineering (IJCSE) Ontology-based Semantic Search Engine for Healthcare Services

by Jotsna Rajan

2025

— With the development of Web Services, the retrieval of relevant services has become a challenge. The keyword-based discovery mechanism using UDDI and WSDL is insufficient due to the retrieval of a large amount of irrelevant information.... more

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Estimación de la similitud semántica de tareas entre procesos de negocio de telecomunicaciones

by Juan Diego Riggioni Corrales

2023, Ingeniería y Ciencia

Como una medida para mejorar la competitividad en el mercado de las telecomunicaciones, actualmente las empresas del sector crean nuevos servicios de valor agregado, con el fin de ampliar su portafolio de servicios y garantizar bien sea... more

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An Approach for Indexation, Classification and Retrieval of Knowledge Sources in Collaborative Environments

by Ruben Costa

2023

This work introduces a conceptual framework and its current implementation to support the semantic enrichment of knowledge sources. It improves the ability for indexing and searching of knowledge sources, enabled by a reference ontology... more

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Multi-dimensional Ontology Model to Support Context-aware Systems

by José Huber Herrera Rodríguez

2023

Computing has evolved in such a way that has left behind the limitation of being static and local, promoting the emergence of the new era of computing called Ubiquitous Computing. To maximize the use of this new computing platform, it is... more

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An ontology-based Method for Discovering Specific Concepts from texts via Knowledge Completion

by Brigitte Safar

2023

Heterogeneity in user's queries and data sources can easily cause problems in perceiving sufficient information to form correct answers. In this paper, we address this issue when data sources are unstructured short texts describing... more

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Porto, Portugal SEMAPRO 2013 Editors SEMAPRO 2013 Committee SEMAPRO Advisory Chairs

by Abolfazl Mir

2022

a series of events highlighting the most recent advances in ontology, web services, semantic social media, semantic web, deep semantic web, semantic networking and semantic reasoning. The inaugural International Conference on Advances in... more

Fig. 2. IGUSMON Project System Architecture [22] The research is in its infancy and the following architecture and design described is the conceptualisation of our algorithm for intelligence gathering using semantic metadata. Figure 2 illustrates the conceptual design of the Information Gathering tool, which demonstrates how the web spiders will act as a mechanism for gathering the raw data, before sending the extracted semantic metadata back to the database for validation with the predefined ontologies. Once the extracted data is verified, a data-mining [34] algorithm structures the data into information before returning it as a knowledgeable answer to the Query Management System.

Figure 1. Domain decomposition based parallelisation of the Random Indexing algorithm.

Figure 3. Performance results for decomposed datasets.

RISO-TT is a rules-based system and since it was conceived to become extensible and flexible it uses a configuration file which determines the connection from the internal logic to the rules-base. To insert a new standard (or rule) in the rules base, the new standard is simply added to the configuration files. For this change to be realized, it just needs that RISO-TT be run again with a document that contains, in its content, the corresponding expressions. Figure | presents the RISO-TT architecture. Figure 1. RISO-TT architecture.

Fig. 1. Ecosystem for Statistics-Driven Ontology Modeling Support The approach chosen aims at being generic, in order to cater the needs of different use cases that expose a strong need for dynamic adaptation and the demand for a deep analysis of interactions with the models. Figure | depicts the influences relevant for the considerations made in this approach:

Fig. 3. Relations between different wildcard concepts and their temporal likelihood to occur before/after each other Figure 3 shows how temporal relationships are discovered levaring the depicted Markov chain.

Figure 1. Taxonomy of Ontology. Moreover, we identify properties and relationships of each one of the concepts. Then, we obtain the taxonomy and classes shown in Fig. 1.

The query is received and the processing starts: the que is analyzed and contextualized through the exploration of t ontology. Next, the rules of the context are applied (market analysis constraints). This results in the parameters that define a geographic query in market domain. Then, t ontology is explored to find a matching between query elements and the concepts stored into ontology. Each element of a query is searched within the ontology (e.g., label names). When a concept is found into the ontology, then its context is extracted. A context is formed by t ry he ne by ne neighboring concepts of a matched concept and its semantic relations are extracted and stored into a vector. The process works according to the following algorithm depicted pseudo code (see Fig. 2): in Figure 2. Ontology exploration algorithm.

Qi, = “find a business location in Mexico City with market competition in a radius of 10 km located in corner, or into the street, rent or buy". The result is the list of properties that meet the criteria, one of which is shown in Fig. 4. Now, if the query Q; is modified on the relevance of the parameters: parking and roads near. Then, the new Q;, query is generated. The result is now a new list of locations.

Figure 3. Input parameters (interface Web system).

Figure 6. Results for new generated query Qua.

Figure 7. Best commercial locations for query Q3. Figure 5. The best location for commercial business from Qi.

Figure 8. Rated building or property for query Quy. Another test conducted for the query Q,: "Find location for a stationer’ shop, with a low floating population, located to 3 km from a zone influenced commercially." The results obtained show 10 possible locations, the most relevant being located in: Calle 15 No 100. This is shown in Fig. 8.

Figure 9. Zone of influence for the best location of query Q4. Fig. 9 shows a polygon (in orange) that represents the commercial zone that influences the locations found and evaluated as good candidates for query Qy.

Fig. 3. Implementation of Swarming Intelligence Behaviour with the IGUSMON Project System Architecture signal the release of the web spiders from the spider deployment module via the database and a swarming agent. Collected information will be verified for relevant inte ligence within the Validation Module via ontology checks. However, before the semantic metadata reaches the Vali wor dation Module, a final check will be conducted via a ker agent against the Irrelevant Data module, where discarded information from previous extractions, that did not prod uce positive intelligence results relating to a query, are stored.

Figure 1. Schema of Social Communication [24] (German) Figure 1 shows Aufermann’s [24] model for social communication in which two parties are involved. The sender encodes the statement he intends to submit in a message. Therefore, he uses his own character set to encode the message. The message is sent via a medium to the recipient whereby spatial and temporal distance is overcome. When receiving the message the recipient will use his own character set for the decoding of the message.

Figure 2. Knowledge Transfer through four layers The four layers that influence the transfer of a message from one person to another are code, syntactic, semantic and pragmatic layer. The concept of a knowledge transfer through different layers was influenced by the OSI Reference Model [27]. Figure 2 illustrates the transfer of a message from the sender to the receiver passing through the four layers. At the lowest level of the layer for transfer is the code. The code consists of symbols or signs that represent the smallest unit, which forms the basis of the higher layers. In the case of written language, which is the focus here, the smallest elements are the characters, o, taken from an alphabet X. In the case of spoken language it would be phonemes, or in sign language gestures.

Knowledge about the receiver is an important requirement for a successful and lossless transfer of a message. The better the sender knows the receiver, the easier he can encode the message. A proper encoding of t message can be done by using elements that exist identica in the personal knowledge base of the sender as well as in t personal knowledge base of the receiver. If the receiver unknown, only assumptions can be made to support t selection. The other way around it is easier for the receiver decode the message if he knows the sender of the message very well. Figure 3 visualizes the overlapping of t knowledge in different layers. ne ly ne iS ne to ne

Besides a few pre-defined primitive types UML allows the definition of additional datatypes in class diagrams. These can be primitive types, complex datatypes, and enumerations. In UML, datatypes—similar to classes—can have owned at- tributes (as well as operations which are not discussed here). Therefore, they can be used to describe structure. Figure 1 shows examples for the three kind of datatypes. Fig. 1. Examples for datatypes in UML. Left: user-defined datatype witt two components. Center: user-defined primitive datatype. Right: Enumeratior with three allowed values.

Fig. 2. Extract from the UML meta model, showing the difference between classes and datatypes. In contrast to instances of classes “any instances of that data type with the same value are considered to be equal instances.’[9, p. 63] Although the graphical representations of datatypes in general (instances of DataType) as well as primitive types (instances of PrimitiveType and enumerations (instances of Enumeration) in particular look similar to the representation of classes (instances of Class) they are different elements of the meta model as shown in Figure 2.

Fig. 3. Extract relevant for datatypes from the OWL-2 meta model. An OWL-2 datatype is defined by assigning an Inter- nationalized Resource Identifier (RI) to a DataRange us- ing a DatatypeDefinition axiom. According to the OWL-2 DL specification this IRI must have been declared as the name of a datatype.

Fig. 6. Example for the transformation of a generalization relation between datatypes. no internal strcuture (e.g., enumerations) a transformation is possible: While the generalization of UML classes can be transformed into an OWL-2 ObjectUnionOf class expres- sion this is not possible for datatypes. As the name suggest, an ObjectUnionOf can only be used for classes. Instead an instance of DataUnionOf is used. The sub-datatypes combined in the DataUnionOf constitute a new data range. Using a DatatypeDefinition axiom a name is assigned to this set of datatypes. This name is the name of the super- datatype from UML. Figure 6 shows an example for such a transformation.

Fig. 7. Example of a QVT-Relations rule from the OWL-2 — UML transformation that maps OWL-2 Literals to UML EnumerationLiterals.

Fig. 3: Class hierarchy of the food domain ontology

Fig. 6: Location and Food Ontology Relations Fig. 5: Lasagna individual of MealCourse class that we described in Section 2.

Fig. 9: General view of the architecture

By defining ontologies based on a particular domain [26], the algorithm [27][28] within the Intelligence Gathering tool will facilitate the return of intelligence in a structured manner and only for information predefined within our ontologies for BI. Figure 1 presents a breakdown of the thinking required behind ontology design and will form the foundations for developing our BI ontologies. Fig. 1. Visualisation of layers for Ontology Creation [2][29]

benelits of this focus are anticipated to ensure vast datasets can be quickly referenced and utilised for extraction. The direct integration of the semantic metadata with the ontologies will ensure that relevant knowledge can be extracted. An obvious limitation to this method will be determined by how much of the relevant data is attributed with semantic metadata. Even though semantic web methods have been proposed for over a decade now, data does exist that was created before and after, which seldom or minimally focuses on semantics. However this does not mean that semantic metadata is limited; with the technological growth and vast amounts of growing data, this limitation is becoming less finite. The other limitation that will impact our research will be the reach of the algorithm. When the conceptualisation of the algorithm is developed, the testing will focus on a finite number of websites for extraction, due to available computing power and time constraints. As mentioned, the focus of the IGUSMON project is currently immersed within this area and development is in progression; some of the design elements proposed may change as the modules are created and tested for feasibility. Fig. 4. Mathematical Theory of Communication [5][22]

Figure 1. The result of Positive MAP in the various topics of TREC, using the two approaches: with categorization "MAP-AC" and without categorization "MAP-SC".

Figure 2. The result of Negative MAP in the various topics of TREC, using the two approaches: with categorization "MAP-AC" and without categorization "MAP-SC".

Figure 1. Pay-As-You-Go Framework for OWL ontology management in the cloud

Figure 2. Pay-As-You-Go Architecture of the proposed system

TABLE II. PERFORMANCE CHARACTERISTICS GROUPED BY DATASET AND NUMBER OF COMPUTING NODES. TABLE III. PERFORMANCE CHARACTERISTICS FOR FRAGMENTED DATASETS, THE NUMBER OF FRAGMENTS CORRESPONDS TO THE NUMBER OF COMPUTING NODES.

TABLE IV. PERFORMANCE RESULTS FOR PARALLEL AIRHEAD SEARCH ALGORITHMWITH VARYING JVM SETTING ON WIKII DATASET.

We notice that the p-value obtained from the Heideltime data characterizes a non-normalized sample. However, evaluating the data, we noticed that they concern three samples which, in the document marking process, had not a good result. The format in question is for dates with three characters (e.g., 200 AD). This format was not recognized by Heideltime and, so, due to the non- Right after tests, the first task carried out was the evaluation of normality of the resulting data. For this activity, we ran the Shapiro-Wilk test, obtaining the results displayed in Table I.

Based on the BoxPlot presented in Figure 2, it is possible to conclude that we cannot state whether there is superiority of one of the tools Heideltime and SUTime, since the trust intervals intersect each other. This occurs maybe due to the fact that both tools use the same marking standard and are based only on the knowledge based available in the standard. However, it is possible to state that there is a statistically proved superiority of the results of RISO-TT, compared to the others.

ABLE III. ACCURACY, COVERAGE AND F-MEASURE RESULTS

TABLE II. SEMANTIC VALUES FOR MARKET PARAMETERS In Table 2, the possible values or range of values for each parameter is showed. For example, when a business requires a form of transportation, the evaluation is done in two ways: fast, in terms of time and distance, considering the traffic during the transportation. Table 2 shows the possible semantic values for some parameters in a market context.

Many methodologies identify the domain expert as a crucial participant in the ontology engineering process. For example, Methodology 6 discusses that “... an interorganizational ontology needs to be modeled not by external knowledge engineers, but by domain experts themselves. Only they have the tacit knowledge about the domain and can sufficiently assess the real impact of the conceptualizations and derived collaborative services on their organization. ...” However, it is interesting to observe that only Methodology 3, Methodology 4, and Methodology 6 offer explicit support for domain experts to model the respective ontologies, or at least parts of it, autonomously.

favie Vit EXPERIMENTS ON THE POLARITY WITHOUT CLASSIFICATION OF TOPICS

One reason why a significant improvement is obtained in these classes may be due to the number of topics in the training data sets. Topics number of class "Issue" and class "Person" are, respectively, 22 and 21. [HE RESULT OF FEW TOPIC (TREC) FOR POSITIVE WORDS

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WEB 2013 The First International Conference on Building and Exploring Web Based Environments WEB 2013 Committee WEB Advisory Chairs

by Roula Karam

2022

WEB 2013 Foreword The First International Conference on Building and Exploring Web Based Environments [WEB 2013], held between January 27th-February 1st, 2013 in Seville, Spain, was the inaugural conference on web-related theoretical and... more

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Temporal RDF System for Power Utilities

by christian langheit

2022

Temporal data is a critical component in many applications. This is especially true in analytical applications for the smart grid. The analytical process often requires uncovering and analysing data and complex relationships from... more

Where C; is the set of change operations with overlapped triples in all stored delta. Instead of executing all the in-between deltas in sequence, the aggregated delta can create a logical version directly by storing all of the possible deltas in advance. In other words, given a sequential delta Aj i41, Aj+1i+2,..., Aj—1,; between V, and V;,1, an aggregated delta is defined as follow:

Figure 1 shows the number of triples required to store power transformers for each RDF versioning approach. The number of triples of each scheme includes the total number of triples in all the versions and, if any, all the deltas schema. In Sequential Delta and Aggregated Delta, we consider the first version as the original version. With the All Snapshots approach, as shown in Figure 1, the number of triples increases linearly as the number of versions increases, since this scheme stores all the version snapshots in the triple store. In contrast, the Sequential Delta and Aggregated Delta approaches require less triples than the All Snapshots, because they store only the original version and the deltas. When we compare the Sequential Delta and the Aggregated Delta to each other, we notice in Figure 2 that the Aggregated Delta requires more triples than the Sequential Delta. This is because there are duplicated triples stored by the aggregated delta procedure. In terms of number of triples, the Sequential Delta procedure requires less storage space than the Aggregated Delta. Figure 1. Number of triples for the version management Figure 3 shows the construction time of versions in the Sequential Delta and the Aggregated Delta. The y-axis rep- resents the construction time of versions in seconds, and the x-axis denotes the specific versions to be constructed. In order to generate versions which are not stored physically, we need to construct logical versions on the fly from the original version. As shown in Figure 3, while the construction time in the Sequential Delta is proportional to the number of versions we need to trace backwards, the Aggregated Delta can compute any specific version at almost a constant time. This is because

Figure 3. Construction time for the Delta-Based versioning approaches the Aggregated Delta recreates any version by applying only a corresponding aggregated delta to the original version. Since the relevant version needs to be constructed on the fly for a given query and it occurs very frequently, the Sequential Delta performance is very critical. We also evaluated the query performance of various version storage schemes using the queries in Table II. All the queries are in SPARQL. The SPARQL Query Language is a language for querying the RDF data [13]. Basically, the queries in Table II simply count the number of power transformers for a specific version. Figure 4 shows the average response time of ten executions of the queries of Table II. We notice that the All Snapshots approach is superior to both Delta-Based methods for these queries except for the first version. This can be easily explained by the fact that the sample queries used here require the computation in a specific version and the All Snapshots approach physically stores all the versions. With the Delta- Based approaches, we first need to construct the version on the fly and then query against it.

Figure 2. Number of triples for the Sequential Delta vs the Aggregated Delta

Figure 4. Computation time for the version management. TABLE II. SAMPLE QUERY

Figure 7. Voltage outliers detection Figure 6. RapidMiner extension shaping customer usage patterns, preventing outages, optimiz- ing assets and more is extremely valuable. Transforming a high volume of data into valuable decisions becomes a reality for any business that intends to succeed.

TABLE III. QUERY EXECUTION TRACES We embedded our application with RapidMiner [16], a code free modern analytics platform that includes machine learning, data mining, text mining, predictive analytics and business analytics. According to the 15th annual KDnuggets Software Poll [17], released in 2014, RapidMiner remains the most-used free dataming tool. Data mining is the process of analyzing and turning large collections of data into useful knowledge. It can be seen as a natural evolution of informa- tion technology, where huge volumes of data accumulated in databases are analyzed, classified and characterized over time. Final Step 8: The Java code converts the received values into an RDF ontology and merges it with the resulting ontology of Step 2. The original user query is applied to the newly merged ontology and the result is returned to the user.

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Estimación de la similitud semántica de tareas entre procesos de negocio de telecomunicaciones

by adriana narvaez

2022, Ingeniería y Ciencia

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Application of the Tensor-Based Recommendation Engine to Semantic Service Matchmaking

by Andrzej Szwabe

2022

The paper presents a novel approach to semantic Web service matchmaking, which involves a use of multilinear data representation and processing. The proposed solution involves the use of a novel tensor data filtering method based on a set... more

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Semantic Retrieval and Distribution of Relevant Medical Knowledge

by Altaf Rahman

2022

In the fast growing world of information, the amount of medical knowledge is growing at an exponential level. It has now become a very difficult task for a regular person to keep up with all the new discoveries and updates in this domain.... more

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Service Discovery with SWE-ET and DIANE - A Comparative Evaluation by Means of Solutions to a Common Scenario

by Emanuele Della Valle

2022

Semantic service discovery and matchmaking has received increased attention within the last years. Various approaches have been proposed but agreed upon criteria or common use and test cases to objectively evaluate such approaches are... more

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Cross-Device Interaction Definition, Taxonomy and Applications

by Jörg Cassens

2022

This contribution proposes a definition and taxonomy of the often used term cross-device interaction. Despite of technical progress, systems and interfaces that integrate into the environment are still the subject of intensive research.... more

Figure 1. Taxonomy for XDI devices Within this definition, there are various possible scenarios of XDI.

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SEMAPRO 2010 Committee SEMAPRO Advisory Chairs SEMAPRO 2010 Industry Liaison Chairs

by Petr Kroha

2022

the complexity of understanding and processing information. Semantic processing considers contextual dependencies and adds to the individually acquired knowledge emergent properties and understanding. Hardware and software support and... more

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WEB 2013 The First International Conference on Building and Exploring Web Based Environments WEB 2013 Committee WEB Advisory Chairs

by Carlos Bobed

2022

Fig. |. | Technology made communication evolve from a one-to-one to a many-to-many model. The future could bring human-machine intercommunication.

Figure 15. Relational Database Schema search result The mapping test between the Relational Database Schema (Figure 2) and RDF Schema (Figure 13 & Figure 14) is evaluated using meta-data search comparisons as shown by the sample screenshots in Figure 15 & Figure 16 below.

Figure 1. Logical architecture of WebODRA

Figure 2. A sample forum developed using WebODRA framework

capability to OSGi in order for OSGi to be more applicable to enterprise systems. Toward this goal, in our previous paper [13] we proposed an approach, where distribution is enabled by exposing OSGi services as Web services, which is done by creating a “middleware” bundle that adapts OSGi services to become Web services. Figure | gives an overview of our approach. Figure 1. Architecture of our approach [13]. (System B is an external system.)

Figure 19. GUI of airline interface. The airline interface lets airlines to change information about scheduled flights. For example, an airline can change the aircraft type to a smaller one if the currently scheduled plane cannot be filled or the airline can change the departure time if the currently scheduled plane is delayed due to weather conditions. All these changes are broadcasted to the flight list display system and the mobile client by the management system. Figure 19 shows GUI of the airline interface. The flight list display system sends updated flight information (including current departure time, gate, aircraft type and flight status: on-time, delayed or cancelled) to flight list displays, which are spread over the airport. Figure 20 shows GUI of the flight list display system.

To prove the feasibility of our approach, we implemented a flight information system (FIS) using our approach. The purpose of the FIS is to inform passengers waiting at the airport about flight changes made by airlines. Figure 18 gives an overview of the FIS. Figure 18. Architecture of flight information system.

Figure 21. GUI of mobile client. In addition, the mobile client lets passengers receive updated flight information from the comfort of their mobile phones. Figure 21 shows GUI of the mobile client.

Figure 1. Relational to Semantic Mapping Model With Relational data on one side and RDF repository data on the other, the mapping algorithms have involved domain specific heuristic formulation to satisfy the accurate implementation of knowledge transfer. Different reasoning logic and rules are part of the mapping algorithms.

Figure 2. Relational Database Schema Overview

Figure 13. RDF Schema view (Protége) The implementation of the algorithms is based on the Space Project Management scenario. An interactive Java application is used to execute the mapping procedures and the resulting Semantic Web repository loaded in Protégé (a free open-source Java tool providing an _ extensible architecture for the creation of customized knowledge-based applications) OWL editor is shown on the figure below. The Ontology diagram modeled above is also serialisable using OWL file format as in Figure 14 below.

RECALL OF PREDICATE SEARCH BY EACH METHODS.

Figure 16. RDF Triples search result We have compared our attempt to some of the existing approaches (Direct Mapping, eD2R, R20, Relational.OWL, Virtuso, D2RQ, Triplify, R2RML, R3M). We used the comparison criteria specified in the seminal paper [6]. The results of this comparison can be summarized as shown in Table I and Table II.

Figure 1. A Basic Service Oriented Architecture [11]

Figure 3. The proposed Architecture with the Protocol service

the Protocol service executes the target service and ends the process. Alternatively, if the request involves two-way operations (synchronous), the Protocol service executes the target service and the result is eventually returned to the consumer. Figure 4. The outline of the Transformation method

Figure 2. Top 10 reviews sites by numbers of review

Figure 3. Top 10 internet countries in Europe. The best way to get information about the holidays destinations is to take recommendations from friends, colleagues or relatives. “Sharing” is a key word of new way intending tourism; in fact reviews and social networks are connected with principal sources chosen by travelers for get information on travels. At the second place we can see Internet websites, at the third the personal experience. Other sources for reaching information are: travel/tourist agencies and free catalogues and brochures are important. Less important than the other are: newspapers, radio and TV, paid-for guidebooks and magazines and social media sites (see Fig.4). [13]. Fig. 5 shows the most important methods used for arranging the holidays. These are the results of several inquests, with multiple responses, of the document "Attitudes of European Towards Tourism", published by the European Commission, where analyzes the behavior of European tourists in 2012 [13]. As we can see, the main method used is Internet with a 53%, almost the double of the second method most used. These data can bring us to reflect about the importance of Internet and the experience (ours experience or people who we know) in comparison with other sources. From these results, it is easy to see the importance of the ICTs and web services in the tourist sector.

Figure 4. Information sources considered to make a decision about travel plans (multiple responses)

In 2012 we can see in which products the Spanish people decide to use internet as a commerce vehicle use by 27% of

Figure 6. Ten activities with the biggest turnover in the global electronic commerce in the first 2012 trimester

Figure 7. Italian e-commerce turnover at the end of 2011

Figure 9. Percentage of Internet Users in function of the total population

This sector is used mainly by men (5,7%) instead women (2,7%) [26]. In 2012, the 56% of internet users (in France) used internet in order to travel. In other countries as Germany or U.K, this value is higher than the 64%, with a value of 71%, for U.K. Figure 10. Main information sources to make decision about travel destination.

Figure 1. General Overview of Query and Matching APIs Searching sub-graphs: First, we search the API registry about any API that has all the output parameters of the query (we call “last nodes”), and any API that has at least one of the input parameters of nodes”). After this searching it i sets are obtained for the first and the query (we call “first s assumed that non empty last nodes. The next is to create n-ary trees for every last node by visiting all the nodes connected to a particular last node. Such tree is constructed by recursively includ the directed similarity graph unti We use the BFS algorithm to so ing nodes and edges from 1 we reach the first nodes. lve this problem. Now we can find all the possible composition candidates from the trees. Figure 1 shows a general overview of the query and the matching APIs before constructing the overall composi- tion plans.

Figure 3. Directed Similarity Graph To experiment with the data mashup tool we extracted a collection of REST and SOAP APIs from Programmable- Web. To avoid potential bias, we chose different APIs from different domains. We first collected a subset which associ- ated REST APIs for three domains: weather, travel, and mapping. This set contains 63 APIs. Next, we collected a subset containing 17 SOAP APIs from three domains: zip- code, location, and search. In Figure 3, we show a directed similarity graph which obtained from our experimental dataset. The graph consists of 80 nodes and 123 edges. A possible query for the Web API composition is given as follows: Q.1 = {zipcode}, Q.0 = {city, latitude, longitude}. The composition result is exemplified by part of the directed similarity graph as shown in Figure 4. From the registry our engine has discovered 8 last nodes (dark grey circles) and 7 first nodes (light grey circles). We call the BFS algorithm and create an n-ary tree for each last node. This is repeated until all the last nodes are reached.

visually, and iteratively refine their goals until the final results satisfying. The ontology learning method automati- cally builds semantic ontologies from Web API descriptions

ded results in Table 4 have desired or relevant goals. Although the 3rd ranking result turns out to be invalid as it does not satisfy the user requirement, top 2 ranking results have desired composition plans. These results have shown that our algorithm can generate most user desired outputs. TABLE IV. LIST OF RANKED COMPOSITION CANDIDATES

Fig. 1. X3D interfaces and metadata nodes In the example in Listing 1, the shape is described by three elements: geometry, appearance and metadata. The whole semantics is enclosed in the Met adat aSet and includes both simple (subject, creator, description—lines 9-10) as well as complex (Mass, 11) nested nodes. Every node is assigned a name and a reference to the name definition (a web document). The containerField associates the node with the appropriate field of its parent node.

Figure 1. Classes and instances of Labour Market ontology All those terms derive from the occupation descriptions and belong to the group of specific terms with the highest TF-IDF values (see point 3 in the previous Section B). Ontology classes and relationships have to be refined from the initial classification taxonomy of candidate terms. By taking as main references the ILO and O*Net resources it is possible to specify some ontology parts, e.g., Sector class with its sub-classes and instances as represented in Figure 1.

Figure 1. Competency Questions Translation Approach

Previously [4] we have introduced a model of stigmergy including the concept of a stigmergy grand purpose and the core components of stigmergy: the agent, the environment, and the sign. The model is illustrated in Figure 1. The model as illustrated in Figure 1. ties together the core components of stigmergy, an inner band representing tl attributes of the com the dynamics acting outer band dynamics or defining the inter feedback (contributi on those attributes. Fur are either internal to eac ons) and negative feed illustrating how the agent, where the negative feedback is ap environment. The paper where we presented t focused on a holistic model of stigmergy which applies for the world of entomology, the human world and the virtual world. This paper is focused specifically on how the varieties of stigmergy manifest as Web environment UI elements. In context to this paper, these three components correlate to the users of Web environments, contributions that the ponents, and an outer band representing thermore, t Our mod back (deca positive feedback is contributed by t plied by t users make. he model w Nn component, face between components. describes the dynamics of equilibrium between positive ne ne el y) he ne as and the

Figure 2. Example of a Facebook LIKE mechanism.

Figure 3. Example of a Facebook SEEN-BY mechanism.. Another variety of stigmergy describes the development of unintentional trails within the environment. An example of this is best shown by people wearing a path into a lawn when a short-cut is taken across the lawn. This unintentional trail is similar to another type of UI mechanism found within Facebook (see Figure 3. ).

We conducted an experiment within the university research community on the impact of usability methodology of personas and scenarios that led to the design of the ERaUI interface as an intelligent and portable widget that we deployed on the left hand side of NaCTeM web interface as shown in Figure 1.

Figure 3. ERaUI Click & Mouse Movement Heat Map. ERaUI use click / mouse movement heat maps techniques to tracks the user interaction with the host website as shown in Figure 3.

Figure 4. User Records and brpwsing patterns

Figure 9. ERaUI live help box As shown in Figure 9, ERaUI interface also provides live communication facilities through ‘live help' box enabling the user to seek support and guidance from the NaCTeM host web site administrator.

Figure 7. Panes of similar pages from collaborative filtering

Following the completion of a project, the materials contained in some of these resources are considered for archival purposes so that they remain available despite the end of funding and dispersal of teams. This is an increasingly familiar situation as funders and institutions seek to develop repositories to increase the impact and availability of their work. Such archives or repositories of web resources enable users to search for information in the collection using basic keyword searching which proves to be ineffective simply because they retrieve web pages, datasets, and research papers merely on the basis of incidental mentions of a term in users’ queries. Such a searching strategy misses the context, relationship, historical details and other attributes of a particular resource which would have enabled users to extend their exploration to other related records in the archive. One such web resources repository is the ReStore repository [6] which will be used as a test-bed for the experimentation and search performance evaluation described here. Figure 1: An overview of web resource, creation, development and its archival into a web resource archive, e.g., ReStore repository

Figure 3: ReStore synthetic ontology architecture to be integrated into the KIM’s bootstrap ontology KIMO to aid in (a) annotating domain-specific (Social Science Research Methods) resources in ReStore repository (b) searching relevant information in integrated ontologies. In Figure 3, we have put together very basic terms and concepts (classes, properties) in a container with a view to mapping and extending them to other ontologies and storing the resulting annotation metadata (instances of classes or KB together in the form of integrated ontologies. The KB and mapped network of ontologies are assumed to be searched at a time to produce relevant and accurate results with a view to evolving it iteratively as new terms are added. GATE will process structured/semi-structured content in documents and OWLIM represents RDF triple store containing inference, querying engine for information retrieval.

Figure 1. Events that are similar by vocabulary but different. edge represents the relation between two minimal events. In order to establish EGM, we first define two types of a minimal event which can not be divided on a sentence in the given article: an event noun phrase and an event predicate-argument structure. An event noun phrase is a noun phrase which can represent an event by itself. If we divide the noun phrase into several elements, then no elements can represent an event. Therefore, it is adequate that we regard an event noun phrase as a minimal event. A predicate-argument structure in a sentence, abbreviated to PAS, is called an event predicate-argument structure, abbreviated to EPAS, if its predicate represents an action related to an event. For example, sentence “Date Muratoki revived the economy of his feudal domain” contains an EPAS because its predicate “revived” represents an action in the economic revival; on the other hand, the predicate of sentence “Tokugawa Ieyasu had been told as a descendant of Nitta Yoshishige” is not regarded as an action of a historical event. If a sentence has two predicates representing actions of events, then we regard the sentence contains two EPAS. We then introduce the following eight kinds of a relation between minimal events: deployment, illustration, parataxis, causality, paradox, progress, means, and situation. In contrast to models dealing with the causality relation between events [1], our eight kinds of relations are expected to find similar events more accurately. We also present an idea how to determine which events have a relation by utilizing the centering theory proposed by Grosz et al. [5], [6]. Figure 2 illustrates an example of composite events in

Figure 2. Examples of minimal and composite events.

One usage location that was pointed out by Reviewer-5 was marked as Not in use. The framework was not used there since it required a modification to the framework that would change its semantics, a task that the team preferred not to do at the time that that specific location in the application was implemented.

BLE I. SUMMARY TABLE OF RDB-TO-RDF MAPPING LANGAGE COMPARISON [6] WITH RD2SW

RDB-TO-RDF MAPPING LANGUAGES COMPARISON LEGEND [6]

RELATIONSHIPS BETWEEN TERMS its pattern rule, the matching logic will return a matching score because these two parameters are closely related (perhaps using the rules “CityName propertyOf City” and “codeOfCityv propertvOf City’). The semantic matching technique estimates the similarity of the input and output by considering the underlying concepts the input/output parameters cover. Formally, we describe the input as a vector 1 =< p;,C; > (similarly, t O =< py, Cy he output can be represented in the form >), where pj; is the set of input parameters and C; is the concept that is associated with p;. Then, the simila- rity of the in put can be found using the following two steps (the output can be processed in a similar fashion); (1) we split pj; into a set of terms, we then find synonyms for these terms, and (2) we replace each term with its corresponding concepts, and then compute a similarity score.

TABLE II. EXAMPLE FOR DISCOVERY PROBLEM We describe an automatic Web API discovery algorithm similar to the one in [5]. An API matches a query when an API is sufficiently similar to the query. This means that we need to allow the agent to perform matches that recognize the degree of similarity between APIs and the query. We define the matching criteria as follows: matches the query requirement is the Web API discovery problem. For example, we are looking for an API to search a hotel. Table 2 shows the input/output parameters of a query and an API. In this example a Web API W satisfy the query Q. Q requires HotelName as the output and W produces HotelName and ConfirmNumber. The extra output produ- ced can be ignored. W requires CountryCode and Name- OfCity as the input and Q provides CountryID, State- Name, and CityName as the input. An API parameter can be matched with the other parameter only if there is a semantic relationship between them. Here, although CountryCode and CountryID are different forms, they have the same semantics since they are referred to the same concept. Also NameOfCity and CityName have the same semantics since they are properties of the same object (i.e., City). Therefore the agent is able to infer that Q and W input parameters have semantically the same classes.

Fig. 2. Mapping between RDFa attributes and X3D metadata nodes

In Table 1 some candidate terms are classified according to the Labour Market ontology classes.

TABLE III. HERO COMPETENCY QUESTIONS’ ANSWERS (EXCERPT)

TABLE II. SOME EXAMPLES OF HERO CQS ACCORDING TO THEIR RESPECTIVE CATEGORIES

We are conscious that our work encompasses several limitations, such as:

The Cronbach’s alpha was calculated for each of the factors obtained in order to estimate the reliability of the results. Factor 1 obtained a Cronbach alpha value of 0.874, Factor 2 obtained a Cronbach alpha value of 0.907 and Factor 3 obtained a Cronbach alpha value of 0.862. This signifies that the values obtained are good, and that the results are therefore reliable.

RELATIONSHIP BETWEEN DEMOGRAPHIC ASPECTS AND CLUSTERS

E VI. SUMMARY OF THE RELATIONSHIP BETWEEN DEMOGRAPHIC ASPECTS AND CLUSTER Limitations of the study. This work has been carried out in a systematic manner. Nevertheless, we are conscious that it has certain limitations. The first concerns the quality model used since we have limited the research to DQ characteristics in the Contextual category of the SPDQM model.

EXAMPLES OF A PREDICATE STRUCTURE OF EVENTS. Table I

Table III RESULTS OF APPLING OUR RULES OF PREDICATES. THE NUMBER OF RELATIONS BETWEEN MINIMAL EVENTS. Table II

PRECISION OF PREDICATE SEARCH BY EACH METHODS.

a priest and renamed; (b) He succeed to a house because of retirement of his father; (c) He is helped to the lord, and returned to secular life; (d) He killed his men and he lost trust. We use three simple methods for search of these queries: AND search of two predicates; search wit! the information of predicates and the edge of the predicates in our event graph model; search with the information of predicates, the edge and its label in our event grap n n model. For this experiment, we use 500 Wikipedia articles of Japanese feudal lords. We also use the Japanese Wordnet [9] for comparison of two predicates.

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SEMAPRO 2010 Committee SEMAPRO Advisory Chairs SEMAPRO 2010 Industry Liaison Chairs

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Ontologies as a knowledge representation method are already being applied in various areas. Therefore, this method is introduced to new developers constantly. The paper investigates possibility for overlooking ontology features that can... more

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Healthcare systems around the globe are changing their information systems in order to be able to share and reuse patients' information not only in a department where the information is produced, but also between the departments of an... more

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Adequate information management requires more than persistently storing data. Owl-M ea i N in G (to read either owl-mining or owl-meaning) is an expandable 'Business Intelligence 2.0' Enterprise Resource Planning (ERP) prototype,... more

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To deliver on their promises, Physical-Cyber-Social systems must semantically integrate a wide range of heteroge- neous multimodal observations, including physical sensor mea- surements, human self-observations, social observations, and... more

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This paper will present an approach and methodology for the development, deployment and maintenance of web-service Service Oriented Architecture (SOA) platforms based on the concept of a Community of Exchange (CoEx). It will present the... more

The SOA platform has been built around the concept of the Community of Exchange (CoEx). The CoEx serves as formal construct with cognizant authorities, collaborative parties and control of the underlying business process. The CoEx concept is implemented within the SOA platform and effectively controls the use of tools within the SOA context. Securboration uses the term Community of Exchange to describe the focused areas of modeling within a domain and/or problem space. CoEx accurately conveys the interest The general architecture for the SOA platform 1s shown in Figure 1 and is represented as a layering of the shared vocabulary, the discovery/policy enforcement and grounding, the Web Service Layer and the Information/Data Layer. allowing the users to discover data and tools across domains that can be utilized in new ways to enhance existing warfighter processes. The presentation of common data in the semantic “agreed upon” terms cuts out the uncertainty associated with determining if a piece of data referenced for example as “Hazardous Plan Material” in one domain is semantically the same as a piece of data referenced as “Dangerous Plan Material” in another domain.

since they express information directly applicable to the environment. However, SGM provides a “base” set of verbs that domain specific CoEx developers may choose when expressing a capability or dependency. This base set of verbs encompasses a wide range of expressions to provide various meanings. Verbs are categorized to indicate what type of capability or dependency is being expressed (2. Physical nodes may have capabilities expressed as “presentation” if they are data files). However, the SGM verbs can be extended to create more domain specific verbs when creating the domain dependent CoEx. Nouns and Qualifiers are domain specific properties and will usually map to objects and object characteristics specific to the community. The matching of artifacts through the search of capabilities and dependencies is an effective way to share the semantic meanings of the data. The nodes, capabilities and dependencies provided by the SGM allow the CoEx developer to create the Semantic Knowledge Definitions.

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UBICOMM 2015, The Ninth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies

by Carlos Westphall and

2015

The Ninth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM 2015), held between July 19-24, 2015 in Nice, France, was a multi-track event covering a large spectrum of topics related to developments that operate in the intersection of mobile and ubiquitous technologies, on one hand, and educational settings in open, distance and corporate learning on the other, including learning theories, applications, and systems.
The rapid advances in ubiquitous technologies make fruition of more than 35 years of research in distributed computing systems, and more than two decades of mobile computing. The ubiquity vision is becoming a reality. Hardware and software components evolved to deliver functionality under failure-prone environments with limited resources. The advent of web services and the progress on wearable devices, ambient components, user-generated content, mobile communications, and new business models generated new applications and services. The conference made a bridge between issues with software and hardware challenges through mobile communications.
The goal of UBICOMM 2015 was to bring together researchers from the academia and practitioners from the industry in order to address fundamentals of ubiquitous systems and the new applications related to them. The conference provided a forum where researchers were able to present recent research results and new research problems and directions related to them.
Advances in web services technologies along with their integration into mobility, online and new business models provide a technical infrastructure that enables the progress of mobile services and applications. These include dynamic and on-demand service, context-aware services, and mobile web services. While driving new business models and new online services, particular techniques must be developed for web service composition, web service-driven system design methodology, creation of web services, and on-demand web services.
As mobile and ubiquitous computing becomes a reality, more formal and informal learning will take pace out of the confines of the traditional classroom. Two trends converge to make this possible; increasingly powerful cell phones and PDAs, and improved access to wireless broadband. At the same time, due to the increasing complexity, modern learners will need tools that operate in an intuitive manner and are flexibly integrated in the surrounding learning environment.
Educational services will become more customized and personalized, and more frequently subjected to changes. Learning and teaching are now becoming less tied to physical locations, co-located members of a group, and co-presence in time. Learning and teaching increasingly take place in fluid combinations of virtual and "real" contexts, and fluid combinations of presence in time, space and participation in community. To the learner full access and abundance in communicative opportunities and information retrieval represents
new challenges and affordances. Consequently, the educational challenges are numerous in the intersection of technology development, curriculum development, content development and educational infrastructure.
The conference had the following tracks:
 Information ubiquity
 Ubiquitous multimedia systems and processing
 Ubiquitous mobile services and protocols
 Ubiquitous software and security
 Users, applications, and business models
 Ubiquitous networks
 Fundamentals
Similar to previous editions, this event attracted excellent contributions and active participation from all over the world. We were very pleased to receive top quality contributions.
We take here the opportunity to warmly thank all the members of the UBICOMM 2015 technical program committee, as well as the numerous reviewers. The creation of such a high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and effort to contribute to UBICOMM 2015. We truly believe that, thanks to all these efforts, the final conference program consisted of top quality contributions.
Also, this event could not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the UBICOMM 2015 organizing committee for their help in handling the logistics and for their work that made this professional meeting a success.
We hope that UBICOMM 2015 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in the area of Mobile Ubiquitous Computing, Systems, Services and Technologies. We also hope that Nice, France, provided a pleasant environment during the conference and everyone saved some time to enjoy the charm of the city.

Figure 2. Expected loss of fingerprints allocation 9f? for each router setup (out of 84 candidates) with Nzo+ = 15 total budget, N = 5 subregions, kK = 83 routers, as selected by APEC Exhaust, APEC Greedy (with different batch sizes B), and random approach (black error bar showing means standard deviation over 20 test runs). E]

Figure 1. Map of the toy example showing the access points locations and fingerprints allocations (radius of the green circle) determined by APEC Exhaust (also APEC Greedy since they agree). We design a simple problem, as shown in Figure 1, with M = 9 possible router locations and N = 5 subregions, where the location priority is color coded, and the frequency is identical for all 5 subregions. The total number of finger- prints N54 is restricted so that APEC Exhaust is tractable. A random approach is also implemented where the fingerprints are distributed randomly.

Figure 3. Expected loss vs. router setup indexed by the optimal costs given by solving the fully coupled problem (APEC Exhaust). The plot also shows the loss estimated by random selection (green, mean + | standard deviation) and heuristic router selection (red). Another possible heuristic, random selection, is to treat each setup uniformly, where they have identical distribution of expected loss, as shown in Figure 3. We might end up in a region, i.e., the right half of the graph, where even with the optimized fingerprints of the incurred loss is significantly higher then the optimal, since unlike the decoupling heuristic, the ranking information is lost.

Figure 4. Plots of RSSI vs. log distance color coded by devices. A linear regression line is fitted on the data: RSSI = —8.31 - log(d) — 53.4. Figure 4 shows the RSSI with respect to the log distance from the fingerprint to the access point. Generally, a linear relation is observed (with correlation score .77), though the variance can be reduced by accounting for walls as suggested by Bahl and Padmanabhan, [1]. It can be seen that the dependency is stable for all devices (Android, iPad, iPhone), which ensures cross-platform performance of fingerprints con- figuration.

Figure 5. Plots of the actual cost (12) vs. the expected cost (11), color coded by the number of routers deployed. A quadratic curve is fitted to the data: L=0.37- Ef — 2.34-L- + 4.46, where L and Le are actual and expected losses given in (12) and (11) respectively.

Figure 7. Visualization of IPS configuration in CREST for experiment A (left) and B (right), where 5 or 7 access points are deployed according to APEC Greedy. Location priority map is color coded as illustrated in the legend. Location frequency map is not shown, which is HIGH for cubicles, MED for shared spaces, and LOW for corridors. The radius of the green circle indicates the number of fingerprints at the spot. Figure 6. Actual cost distribution in Experiment A (left) and B (right) for all devices color coded by the fingerprinting methods, i.e., random selection, uniform selection, and APEC Greedy. The box goes from the 1°* quantile to the 3"@ quantile, with the black line indicating the mean. As a side note, Android device incurs less cost in both experiments as a result of its stability of the RSSI signal.

Figure 1: Customization based on semantic technologies [22].

Figure 2: Production scenario. different production lines, which are coupled via a workpiece carrier (WPC). The WPC accompanies the product through the milling, assembling and processing cycle and carries the product physically. The WPC is also equipped with a CPS- enabled ADOMe, that couples the physical product part with its virtual counterpart, which represents all product-specific data. Within this interconnected infrastructure, the WPC has access to all information of the product, to provide relevant and necessary data at the respective part of the industrial plant. The WPC communicates with the ADOMe of the respective object, to provide information for the next production step. Thus, produced objects can be registered early in the process flow.

Figure 2. Example of high frequency use in advertising information requests

To illustrate this principle, we take the example of the network represented in Figure 1. This network consists of 12 nodes numbered from 0 to 11. At the instant t, these nodes are arranged as described in Fig. 1. If we suppose that node 0 is the source and node 11 is the destination, the set of possible paths between the source and the destination at the instant t are described in Figure 1. Indeed, the possible paths are the following:

Figure 3: Interaction of the individual components of the framework. The approach can be subdivided into three processing areas that need to interact with each other. Figure 3 shows the actual products and field device level, represented by each CPS and the associated ADOMe, furthermore, the supply level, where services, snippets and ADOMes are hosted as cloud- based networked solutions, and the assistance level for decision support and knowledge acquisition of the CPPS. Decision making is based on the dedicated processing steps and the context-adaptive provision of information of field devices and manufactured products stored in their ADOMes. Each product or field device has both a local and a global ADOMe. The local ADOM¢e is stored directly on the CPS with limited memory, and the global ADOMe, for storage-intensive data types, is stored by a central server, the OMS.

Figure 4: Contextual management based on a situational model.

Figure 5: OMS creates ADOMe in production. information sharing, and quality management. Within the cloud resources, such as processing power, memory or software, in the form of little scripts, are provided dynamically and appropriately over a network. The Object Memory Server (OMS) is the mair infrastructure component that stores ADOMes, according tc the model of an application server to serve a large number of users. Via a RESTful Web service interface the OMS permits access to process data of each manufacturer and provides the functions to create, store, replace, and modify the datz structures in a uniform and consistent manner. Figure 5 shows the interaction of the CPS’ client layer with the OMS. The

Figure 6: Worker performing a maintenance task with a mobile device.

Figure 5. The ROC curves of the OR fusion Figure 3. The histograms of sensor measurements

Figure 7. The impact of number of cooperative sensors for OR fusion

Figure 6. The impact of number of cooperative sensors for AND fusion

Figure 2. Algorithm of paths construction in the case of periodic movements The instants of topology change:

P: Temporary variable containing the path which we are discovering. CP: Intermediate variable containing the current content o Figure 3. Algorithm of paths construction in the case of non-periodic movements

-a- Variation of the maximum transmission delay in the simulations according to the moving radius of the nodes

-b- Comparison between the simulation transmission delays and the analytic transmission delays according to the moving radius of the nodes Bimire & Vanatnon af the waret craca tranemiceinn dalayv accaminna tn the analytic transmission delays according to the moving radius of the nodes Figure 5. Variation of the worst case transmission delay according to the moving radius of the nodes

Figure 4. Variation of the transmission delays over time

Figure 2. (a) CO sensor up close. The platform is integrated with the main module to sense COz concentration, and a local data storage solution with SD card. (b) The testbed is a conference room located at the Center for Research in Energy Systems Trasnformation (CREST) in Cory Hall on the UC Berkeley campus. The space is of size 14 x 10 x 9 ft?, equipped with a full ventilation system including an air return vent and air supply vent, as illustrated in Figure 1.

Figure 5. Occupants experiment F. Field measurements during project discussion. Top: Proxy measurements. Bottom: Corresponding occupancy. The following shows results from several such experiments, which substantially cover the usage of the conference room, and can be easily extended to other areas in the building. The field measurements are shown in Figure 5 and 8 (right), and the strictly controlled experiment is illustrated in Figure 8 (left). Note that to avoid significant overlap between graphs of this section and those of the simulation section, we arbitrarily decide which graphs show the blackboard measurement and the others show the simulated return, as long as the evidence is sufficient to for the argument.

Figure 4. CO2 pump experiment B. Short term excitation with period of 1 hour. Measurements at return (red), supply (green) vents, and blackboard (blue) are shown. Figure 3. CO2 pump experiment D. The measured CO2 concentration from different locations for a 5-hour CO2 release are shown.

Though the system is responsive to the change of occu- pancy, the time it takes to accumulate or deplete CO, to the corresponding stationary value is fairly long. From vacancy to a high level occupancy, the measurement slowly sweeps across several intermediate levels. The difficulty of most distribution- based classification methods is illustrated in Figure 6, where the significant overlapping of regions and misplacement of modes corresponding to different levels of occupancy will lead to confusion for standard machine learning algorithms. By modeling the temporal and spatial dynamics of the system, as we demonstrate in the subsequent sections, we can develop an inference method that is both robust to noise and responsive to change of occupancy. Figure 6. Empirical distribution of CO2 concentration for all occupants experiments corresponding to different occupancy (color coded). B. Simulation with Proxy Link Model

Figure 7. Proxy model simulation with COz pump experiment A with 30 minutes (left) and experiment B with | hour (right) periodic excitation. Measurements at supply (green), return (blue), and simulated return (red) vents are shown.

Figure 8. Proxy link model simulation with occupants experiment E (left) and G (right). The proxy measurements at return (blue), supply (green), and simulated return (red) vents are demonstrated.

Figure 10. Sensing the latent CO2 emission rate by proxy for CO2 pump experiment A (left) and experiment C (right). The estimated emission rate (blue), median filtered rate (green), and ground truth (red) are given.

Figure 9. Spatial and temporal dynamics of CO2 concentration as represented as the states in the proxy link model.

Figure 11. Occupancy detection by “sensing by proxy” (Algorithm 1) for experiment E (left) and G (right). The response times from vacancy to occupancy and vice versa are about 10 seconds and 5 to 10 minutes respectively, since the method detects the dynamics of the system rather than static concentration. The estimations (green) are within 1 occupant of the ground truth (red).

Figure 12. Visualization of confusion matrix for Bayes Net (left) and sensing by proxy (right), where the position of circles represents the true number ¢ occupants (x-axis) and estimated number of occupants (y-axis), and the size indicates the percentage normalized for each column.

Figure 1. Analysis of papers by publication year. In this study, we identified usability evaluation approaches for mobile apps published from 2004 to 2014 (when the study was run).

Figure 3. Analysis of mobile application category Finally, evaluations in hybrid apps are starting to emerge, because it represents a new trend of development of mobile apps, justifying the small number of research in the area. All authors who proposed usability evaluation approaches for hybrid apps highlighted that this category is emerging and needs more research, not only for evaluation of usability, but also for application development.

Figure 2. Analysis by evaluation technique category We observed the category of usability evaluation techniques most frequent are dynamic approaches (77/101 for mobile apps; 25/28 for ubiquitous mobile apps). This result is justified due to the need of assessing the app on a scenario closer to reality, possibly by using dynamic approaches, making the evaluation more efficient.

Figure 4. Analysis by type of experiment.

Figure 6. Analysis of Usability Factors Yet to answer the question Q3 addressed in section III.A, an analysis by usability factors was done, as shown in Figure 6. Analyzing the results, we observed that the factors uset (60/108) and tasks (66/108) are more frequent in the selected papers. In general, most of the papers when dealing with one of these factors also deal with the second one. The evaluation of the factor context of use was observed only in approaches that deal with context awareness requirements. In this analysis, a paper could address one or more factors, justifying that the sum of the numbers distributed among the factors is greater than the number of identified papers. Furthermore, this also shows us that ubiquitous mobile applications can be evaluated recitals three factors as context of use with 21 identified papers, the user featuring 14 papers and 17 papers task with a total of 28.

Figure 5. Usability Attributes Analysis In order to answer the question Q3 addressed in Section IlI.A, an analysis by usability attributes was performed, as shown in Figure 5. We observed that user satisfaction is the most investigated attribute in the identified approaches (67/101 papers) on mobile apps, and it is the second more investigated in ubiquitous mobile apps (15/28 papers). In this analysis, a paper could address one or more attributes, justifying that the sum of the numbers distributed among the attributes is greater than the number of identified papers.

After that, the execution of scripts was implemented using the functions created, as described in Section III. An example of automation can be seen in Figure 2 that validates the behavior of Waze app. In short, the script defines the expected behavior through image (line 2 and 3) for both location ON and OFF, modifies the status of the location property of the phone (to ON), opens the app and verifies whether Waze has adapted to the new context or not. The final steps were to run the scripts for the remainder of the apps and compare the results after execution.

It used the Windows Phone emulator called Project My Screen App [8] to project the application screen to be possible run the scripts. This is the last step.

The high-level architecture of CardiaCare service is pre- sented in Figure 1.

Commonly used optimizations can be described with the following procedure illustrated in (Figure 3).

Figure 5. Separate tables for attributes of different types Figure 4. One table for all attribute values

Figure 6. Hirarchical dictionaries in CardiaCare data model

Figure 7. Attribute representation in CardiaCare data model

Figure 1. Previous Projects Developed in Urban Computing. UrbanContext was designed from the result of previous projects and experiments developed in controlled and open environments of urban interaction. Our previous experiences have been performed gradually and were restricted to three major projects [14][15][16] (See Figure 1).

Figure 4. UrbanContext Android Application.

Figure 1. Work flow of advertising method via smart device using high frequencies. First, the smart TV pictured on the left in Figure 1 starts sending the first high frequency signal to the smart device (@), and the smart device verifies the presence of the first high frequency using Fast Fourier Transform (FFT) (@). Then, if the smart device detects the first high frequency consistently, it checks whether the number of first high frequency signals is & times over (@)). Once this has been

Figure 1 illustrates the architecture of adaptive streaming for a virtualization service. In this architecture, HTTP 2.0 is used for server push. The proposed system performs segment quality control and HTTP request control algorithm to The rest of this paper is organized as follows: in Section II, we describe the concepts and algorithms introduced in the proposed scheme. In Section III, we show the simulation results. Conclusions and future works are presented in Section IV. To reduce the latency of DASH, the server push based streaming scheme has been proposed. The server push mechanism pushes a resource directly to the client without the client request. However, server push is not suitable for DASH as the DASH adjusts the bitrate of contents based on the client request. In this paper, we propose a DASH based adaptive streaming scheme to improve the QoS of virtualization service. The proposed scheme provides seamless display by adjusting the quality of the segment based on the segment throughput and the buffer status. It also reduces latency by using the server push mechanism.

To evaluate the performance, we have implemented the proposed scheme in a DASH reference player developed by DASH Industry Forum (IF). We compared the amount of playback buffer among the proposed scheme, RAHS, ASAC, and default adaptation algorithm of DASH IF player. Figure 3 shows that the proposed scheme stably maintains the buffer level without buffer underflow because the quality is adjusted on the basis of the segment throughput and buffer status. Figure 3. Comparison of the amount of playback buffer

Figure 2. Behavior of the proposed adaptive streaming sceme Figure 2 depicts the behavior of the proposed adaptive streaming scheme. At the beginning of the service, the client requests virtualized content which has the minimum bitrate because there is no information about the network status and the playback buffer is empty. Then, the server pushes the segments until the client requests another bitrate of virtualized content. Based on this approach, the proposed scheme is better able to reduce latency and prevent playback discontinuity.

Figure 3. Screenshot of the advertising application: (a) Main screen featuring a graph of the receiving high frequencies; (b) Real advertising information screen by advertisement signal. This section introduces the advertising application capable of supplying the advertising information to the smart device user by utilizing high frequencies as the proposed method. In addition, we explain the experiments and results used for the performance evaluation of the proposed method. We developed the advertising application based on iOS 6 and created it using Xcode 5. Figure 3 is a screenshot of the main advertising application.

The physical representation of our model is illustrated in Figure 1. The convection of air from air supply to the air return vent near the ceiling is represented by the PDE part. The diffusive term is intentionally omitted since it plays a relatively minor role in dispersing indoor pollutants as suggested in [10]. Another design consideration involved is the modeling of the COz concentration near the ceiling since this is where we see most effect from human-generated CO. One explanation is that the warm breath from a human occupant acts as a “bubble” of gas that rises to the ceiling, since it is more buoyant than the ambient, cooler air. Thus, the air coming from lower in the room is modeled as a source term on the PDE across its entire path. The fact that this bubble of air does not immediately rise to the ceiling but only gradually (as observed in the response of the CO, concentration in the room due to changes of the human’s COz2 input shown in Figures 5 and 8, during the occupant experiments) is captured by the ODE part of the model, which behaves as a filter between the unknown CO, emission rate of humans and the CO. concentration in the room. Figure 1. The physical representation of the model. Fresh air with CO2 concentration U(t) enters the room from the supply vent, and exits the room after convection and mixing with human breath, V(t), which rises to the ceilings, and the measured COz2 concentration at the return vent is u(1, t).

We compared the latency between the proposed scheme with server push and one without server push mechanism. Figure 4 shows that the latency is evidently decreased when the server push mechanism is deployed. DASH can improve the QoS of streaming-based virtualization service by adjusting the quality of content according to the network condition. However, HTTP request/response procedure of DASH introduces additional latency which degrades the quality of virtualization service. In this paper, we propose an adaptive streaming scheme for DASH to improve the QoS of streaming-based virtualization service. The simulation results show that the proposed scheme provides seamless playback and low latency by adjusting the quality of content based on the server push mechanism of HTTP 2.0. In the future work, we will enhance the proposed adaptive streaming scheme to reduce the frequent quality change due to the varying network condition.

Figure 1. High-Level Block Diagram of the system parts. The first part is a traditional online social network that enables users to communicate through the use of comments. Furthermore, this social network enables users to create con- nections with other individuals, most importantly, those users that are within their vicinity. The second part of this social network is another website, which we refer to as the Interactive Wall. This website is responsible for extracting a stream of comments posted by the users of our system. This stream of comments is then displayed onto a big screen which is placed in a public space. Apart from displaying the stream of comments, the Interactive Wall will also try to play an active role in the discussions by displaying articles relevant to the current discussion on the wall (which are selected from news sources using artificial intelligence techniques).

of previous discussions. The vast majority of the comments posted (94%) were text-only comments, while the remaining 6% included images. Figure 2 shows the usage of our system over a period of time. This includes a day before the actual start of the experiment, and a period of three days after it had ended. Figure 2. Usage of the system over time

Figure 2. Multimodal localization system fusing WiFi signals and images. Positioning range is easily broaden by simply adding more APs (Access Point) in the environment. (3) Computational expense is much lower for WiFi-based than vision-based positioning system. (4) The positioning accuracy is provided in accordance with the application-specific demands. Although the camera- based system brings more impressed positioning results, but not every where in building needs high localizing accuracy. (5) Sampling frequency is improved for the WiFi-based system, because it has lower sampling rate (about one signal measure per second) than vision-based system (approximately 15 fps). (6) The information for person Re-ID becomes richer. One object can be identified simultaneously by both WiFi and camera systems. These ID cues can be used in the way of supporting one another in multi-model object localization system. For example, at a certain time, one object is localized and identified by WiFi system, with the position of Pyy;p; and the identity of /Dw:r; (the MAC address of mobile device) respectively. At the same time, this object is also determined by Peam and ID-am from camera system. However, Py ir; is not as accurate as P.gm, whilst [Dw ;pr; is clearer than [Deam. Therefore, by using both of these systems, the object can be localized by P.a, and identified by [Dy ;7;.

Figure 1. Flowchart of object localization system. Object localization is known as a problem of determining the object position in the environment. For each user in multi- user localization system, two problems of positioning (where the user is) and identifying (who the user is) must be solved simultaneously. A general diagram for object localization is illustrated in Figure 1. In this figure, the input cues can come from different sensors, such as optical, radio frequency, ultra sound, inertia, DC Electromagnetic sensors, etc. From the input cues, localization and Re-ID are executed simultaneously to give the output for object position and ID. Multimodal object localization is defined as a problem of multi-cue combination for input or fusion of different positioning methods. As proven by Vinyals et al. [17] and Dao et al. [18], compounding of different models gives better positioning results than applying a single model. Teixeira et al. [19] proposed to use the motion signature taken from wearable accelerometer for identifying people in camera network.

Figure 3. Surveillance region with WiFi range and disjoint cameras’ FOVs. In the surveillance region, two problems of person local- ization and Re-ID will be solved concurrently by combining visual and WiFi information. Figure 3 demonstrates a surveil- lance region which contains WiFi range and camera FOVs. In this region, the WiFi range covers some visual ranges (the camera FOVs: FOV of C;, FOV of Co,.., FOV of C,,). This means the user always move within WiFi range but switch from one camera FOV to others.

Figure 5. The basic idea of representation based on kernel methods. where G is defined by: G'G= Kap and Kpp is D x D matrix with {Kee}i; = k(vi,v;). kp is a D x 1 vector with {ke}; = k(x, uj).

Figure 7 shows the 8th floor plan of our office building. It is set as the testing environment for our combined person localization system. At the entrance, people hold smart phones or tablets go one by one through the check-in gate, then move inside the surveillance area, and finish their routes by going out check-out gate.

Figure 6. Illustration of size-adaptive patches (a, c) and size-fixed patches (a, b) which is mentioned in [1].

ihe dataset for Numan WKe-1V includes 2) people with different routes in the testing environment. Each person spends from 3 to 5 minutes for his route. An approximation of 800 values of RSSIs are scanned, about 2000 frames are captured for each camera in the surveillance area. All cap- tured frames are processed as real Re-ID scenario of mul- timodal pedestrian localization system. Firstly, the images of person body at different poses are extracted from video sequence of the entrance camera. They are later used for training phase of appearance-based person identification. In the surveillance area, each frame from the sequences of four cameras is processed for human detection, so each of 25 pedestrians will have the appearance images (the bounding boxes of each person) at different views. The image file- name identifies the video sequence to which it belongs, the camera ID, frame number, time (hour, minute, second), and the person ID: VVV_WW_XXXXX_YYYYYY_Z22Z.jpg (E.g., 025_01_01260_153702_012.4pg means the appearance image belongs to video sequence 25; it is captured from the camera 01; frame number 1260; the time is 15h:37m:02s; the person ID is 12). These images are utilized for testing phase of person Re-ID system. An example in the dataset for person Re-ID in camera network is shown in Figure 8. The images on the top are used for training phase of appearance-based person identification. They are captured by a camera at the check-in/check-out region. The images for testing phase are shown at the bottom. They come from four different cameras in the surveillance region. Figure 8. The instances in dataset for vision-based person Re-ID.

Figure 9. The results of proposed method against AHPE [23], SDALF [24] and KDES [1] on (a) CAVIAR4REID dataset and (b) iLIDS dataset. We compare the results of our proposed method with orig- inal KDES [1] and other state-of-the-art approaches on multi- shot datasets of CAVIAR4REID and iLIDs. The experimental settings are kept the same as in [23], with modified version of iLIDS dataset is selected (including only 69 individuals, with at least 4 images for each) and 72 pedestrians for CAVIAR4REID dataset, in which 50 different individuals are captured under both views. Each view has 10 images for each pedestrian. The outperforming results of the proposed method are shown in Figure 9. For CAVIAR4REID dataset, rank-1 recognition rate of AHPE (Asymmetry-based Histogram Plus Epitome) [23] is much lower than our method. It is only about 8 %, compared with 67.76 % of the original KDES [1] and 73.81 % for our method. However, both KDES and our method gain nearly the same figures from rank-13 and backward. For iLIDS dataset, the gap between rank-1 recognition rates of the proposed method with AHPE [23] or SDALF (Symmetry- Driven Accumulation of Local Features) [24] is approximately 20 %, and about 7 % with the original KDES [1]. This gap is slightly decreased for KDES but significantly reduced for AHPE and SDALEF after rank-15.

Figure 10. The comparative results with (a) reported methods in [25] and (b the results are tested on our dataset.

Figure 4. SUMMIT mobile app workflow Prior to the game, users can check out different routes and rewards associated with each of the routes. They can then select a route that provides the rewards they desire and suits their constraints in terms of time and distance. This flexibility enables users to customise their gaming experience based on their needs at any particular time. Fig. 4 shows the workflow of the mobile app. When users select a route, the route information will be downloaded onto their phone assuming Internet connection is available. By pre- loading the routes, the issue of unreliable 3G signal is avoided as the route information is now locally stored, hence will always be available to users when en route. During the hike, only GPS signal is required to track users’ position. Since each checkpoint covers an area of 50-metre radius, a short lost of GPS signals will not affect the performance of the app. These approaches give users the Virtual “Always- On” Connectivity impression allowing them to have an undisrupted interaction experience. The problem of draining the battery power is also minimised as the phone is not constantly connected to the network. Synchronisation with the server occurs the next time network connectivity is available and activated by the user when all logged data on the mobile device is uploaded. To help users locate the rewards, a map that shows the locations of the different checkpoints is provided as illustrated in Figure 5(a). Fig. 5(b) shows a rewards selection dialog box.

Figure 5. (a) Map with checkpoints, (b) Rewards selection dialog box

Figure 7. Average rating comparison between younger and olderusers. (* denotes variables with significant differences, a = 0.05)

Figure 6. Overall average rating of all 24 participants.

Figure 3. Star graph example: Hotel Negresco and recommendations on its historical surrounding.

Figure 2. Hotel Negresco: information extraction from different sources Figure 1. Sample semantic network: historical relations built around Hotel Negresco.

Figure 4. Multi-agent architecture: historical data from various sources and other information are semantically related and analyzed in the smart space. Popular architectural styles for e-Tourism recommender systems are web-based, agent-based, and mobile [1][2]. Smart

Figure 5. Many KPs interact in a smart space to construct a service.

Figure 1. Middleware taxonomy, based on [3]. The possibilities of mobile applications increase with the advent of faster hardware, bigger screens and more stable broadband connections. This ongoing evolution of mobile computing leads to larger applications and increases the need for methods reducing software complexity [1]. While reducing complexity has played a central role in software engineering right from the beginning, there is still no silver bullet; com- plexity can only be coped with abstraction [2]. Several types of middleware can aid software developers by providing some of the abstractions that are needed to create nowadays programs.

Figure 3. Control flow during application startup and activity change.

Figure 4. Interaction on application startup.

Figure 5. Universal service handles binding of client services. The binding process is shown in Figure 5. In step (1), a binding is requested by a client fragment. The request, containing the classname of the targeted service, is han- dled by the universal service. In step (2), the service is instantiated and its life cycle is executed until onBind() is called, returning a binder object in step (3). The binder object is passed to the corresponding client fragment by calling onServiceConnected() in step (4), finally establishing the connection between client fragment and client service.

Figure 6. Overview of the middleware integration architecture. Figure 6 shows an overview of the architecture containing the elements described above. Green dots represent connection endpoints, embodied on Android by implementations of the classes ServiceConnection and Binder.

Figure 1. Path from point 0g to point 01. 0g — 03 — 00 — 01 indicates that the pattern is started from og, then moved to 03, then to 09 and then finally ended at 01.

Figure 3. Perceived strength of patterns by users. It is mostly consistent with computed strength class for patterns.

Figure 2. Choice of different proposed patterns (based on pattern strengths) We used a within-subjects design and asked (“How strong is the following pattern?”’) the participants to rate the strength of six patterns as shown in Figure 2 on a 5-point Likert scale. We chose patterns from three different security levels based on their strength, weak, medium, and strong, as is defined in section IIJ-D. Patterns 2(a) and 2(b) are weak patterns, patterns 2(c) and 2(d) are of medium security level, and patterns 2(e) and 2(f) are strong patterns (refer to Figure 2). To avoid biases, we randomized the order that the patterns were shown to the participants.

Figure 4. Sequence showing the change in colors for a single pattern, as user draw it strength of the pattern, the color of the pattern gets updated (see Section II-D for details on pattern strength). Note that change in the color usually goes from red to yellow to green, however in certain cases it can sometimes goes the other way around, i.e., from green to red. For example, a half-drawn Z (09 — 01 — 02 — 04 — 0g — 07) iS a More secure pattern than a full drawn Z (09 — 01 — 02 — 04 — 06 — O7 — Og).

Figure 5. Suggesting a start point by blinking In a pilot study, we examined a number of techniques to suggest a starting point to the users without hampering the usability of Android Patterns. Based on our observations and users suggestions, we concluded that a) suggestion by blinking a point is very effective, and b) users need to be told what is expected be done with the suggested point without hampering the usability of the system. Our final design uses a blinking point (see Figure 5) and similar to EPSM it pops-up a recommendation message stating “Jt is recommended to start your pattern with the blinking point but NOT MANDATORY”, as it helps user to understand what to do and how to proceed with the blinking point.

Figure 7. Strength level of the patterns created by users of NORMAL, BLINK and EPSM. Red, yellow, and green bars indicate weak, moderate, and strong patterns respectively.

Figure 6. Distribution of starting point of the patterns in the NORMAL, BLINK and EPSM. BLINK removes the bias of starting points. Figure 6(a) shows the percentage of patterns starting from each of the nine points in all three schemes. As expected in NORMAL and EPSM, we can observe that the starting point probabilities of the top two corner points (52.1% and 35.7%) are much higher than the other points. This is because there was no recommendation provided for eliminating the starting point bias in NORMAL and EPSM.

Figure 9. Recall accuracy (in terms of trials before success)

Figure 8. Guessing attack against NORMAL, BLINK and EPSM. Guessing model is built over the NORMAL version. needs 3344 and 1918 guesses to guess 50% of the patterns in EPSM and BLINK, respectively. In terms of partial entropy of patterns, this translates to 9.79 bits of entropy for the patterns used by 50% of NORMAL, and 11.7 and 10.9 bits of entropy for the same proportion of users in EPSM and BLINK.

Figure 1. A typical RPL DODAG with one root and six other nodes.

Figure 2. System flowchart After calculating the position of the object, the drone will compute a new position for it to fly to (if the object has moved) so that it will continue to have the object in its view. This process can be repeated as often as necessary. Figure 2 gives a flowchart of the system. B. Object detection

Our system is not restricted to work on only one class of objects. It can be extended to work on any class of objects, but we need to have a reliable object detection algorithm for it. In this paper, we demonstrate our system on pedestrians, and we make use of the Histogram of Oriented Gradients (HOG) pedestrian descriptor from the OpenCV 2.4.10 library (“peopledetect.cpp”). Figure 3 shows an example of the pedestrian detection algorithm in OpenCV, which displays a bounding box over the detected pedestrian. Figure 3. Example of pedestrian detection from the drone

Figure 1 shows an overview of our system. Our system consists of a drone mounted with a single camera at position (xa, ya). Using the algorithm described later in this section, the drone will compute the distance Z and angle 6,of an object (¢.g., pedestrian) relative to itself, and then use this distance to compute the position of the object (x, y) in global coordinates. We assume that the position of the drone is always known (e.g., through GPS, or WiFi positioning, or from Inertial Navigation System (INS), if available).

Figure 4. Classical disparity formula diagram In order to obtain an accurate estimation of the depth of an object, the camera needs to be calibrated. The basic concept of the classical structure-from-motion algorithm is from the geometry shown in Figure 4.

Figure 5. Geometry of view angle calibration

Figure 6. Images of an object taken at various positions We can obtain this value by placing an object at various positions in the drone’s field of view (Figure 6) and measuring the image offset p and the angle subtended @

Figure 7. Classical stereo vision method for the stationary pedestrian We will first instruct the drone to takeoff, and then hover for a few seconds for it to stabilize itself. Then, we will call the object detector function to find any objects within its view. For the ease of discussion, let us use the example of pedestrian detection. After the drone is stabilized, we call the HOG pedestrian detector from the OpenCV library. If more than one pedestrian is found, we need to choose which pedestrian is the one that we are trying to localize. After we have found the pedestrian, we rotate the drone (yaw) so that the pedestrian (i.e., the centroid of the bounding box) is in the center of the image (see Figure 2 for an overview of the system).

The baseline between any two images is simply the sum of the values between them. For example, the baseline between image 0 and image 5 is 47.50451.75+75.75491.25 = 266.25(cm). ER SIO MEETS errr EI EE IEEE ERE ES We repeated ‘the experiment three times, and averaged the results of the four experiments to produce the baselines shown in Table II. To measure the length of the baselines, we placed numerous markers on a wall, and made the drone fly parallel to the wall, taking images as mentioned above. From the images, we can determine the positions of the drone where the images were taken. We observe that the first image (i.e., at position 0) was exactly the same as the image at hovering (i.e., it was taken before the drone has even started to rotate (roll)), and the second image (i.e., at position 1) was taken just after the drone has completed its rotation (roll) to the left (but before any horizontal translation takes place — so no change in its position). The rest of the images (at positions 2 to 10) were of increasing distances between each other, which is due to the inertial and the acceleration of the drone, which needed some time to accelerate to the desired speed.

After we have obtained the depth Z and angle 6 we can use them together with the drone position to calculate the global coordinates of the pedestrian. Figure 10 shows the geometry for calculating the coordinate transformation for each of the 3 cases. In Figure 10, (%qg2,Va2,@q) and (%a1,Va1,9q) refers the positions of the drone (which is assumed to be known). (Xs,¥s), (Xp, Mp), (Xv, Vy) are the positions of the pedestrian in each of the three cases of stationary, moving parallel, or moving perpendicular (vertical) to the baseline, respectively Note that the initial coordinate of the drone (x41, Yai, 9a) is vital which is used as boundary condition for coordinate transformation. From this geometry, we can directly

Figure 9. Geometry for pedestrian moving perpendicular to the baseline

Figure 8. Geometry for pedestrian moving parallel to the baseline The key idea here is that we now need three images, at position 0, 1 and n (the last image where the pedestrian can still be seen) to compute the depth Z. Due to the fact that at position 1 the drone has just completed its rotation (roll) to the left, the drone has not moved yet but the pedestrian had moved by a distance C. This gives us the very important image offset p: that allows us to calculate @. At the nth position, at angle &, we know that the pedestrian has moved by an additional distance of Cx (n—1). So from the geometry, we can generate the following relation:

Figure 12. Examples of Image Sequence and pedestrian detection (5.5m, stationary pedestrian) stationary) are shown in Figure 12. Notice that between the first and second image, there is only a rotation of the drone and there is no horizontal movement.

The drone used in this study is the Parrot AR.Drone2 GPS edition [10]. It has a forward-looking 720p HD camera and a vertical QVGA camera. The drone is controlled from an Intel i5 laptop running Windows 8.1 with 4GB of RAM. The software we used to control the drone is the “CV Drone” package which is available from Github [11]. The image processing routines were from the OpenCV 2.4.10 library, and the entire system was developed in Microsoft Visual Studio 2013.

calculate the position of a pedestrian’s position using one of! the three following equations.

In the second scenario, we have two or more images. The first image may be generated by a person with a digital camera or mobile phone camera, or by another intelligent CCTV camera (CCTV1). The second image may be taken by a drone (i.e., UAV) or by another CCTV camera. If the drone is the source of the second image, it can offer not only the top view but also many views in different angles because it is a flying camera. These images are then sent to CCTV2 where it will generate a new view of the car that matches its own viewpoint of the road. In the first scenario, we have only one image of the car. The image may be generated by a person with a digital camera or mobile phone camera, or even by another intelligent CCTV camera (CCTV1). This image is then sent to CCTV2 where it will generate a new view of the car that matches its own viewpoint of the road.

We identify that CCTV1 must provide at least one image of the car (with no restriction to its viewpoint) to CCTV2. Since CCTV2 knows its own position, elevation and camera inclination, it must generate a view of the car that matches its viewpoint of the road when the car begins to come into its field of view. Figures 2 and 3 illustrate two scenarios where the source image(s) can be obtained.

Figure 7 shows another Opel Corsa that has a different pattern from Figure 6. This Opel Corsa has a black plate with a green stripe mark running from the hood to the trunk. In this case, there are also two images of the same car. The second image (Figure 7(b)) represents an image taken by a drone or from a CCTV camera that is mounted at the top of a building. If this image was not available, we will face the same problem as the Opel Corsa with Flame patterns. The white points in Figure 7(a) and (b) are the points of the polygon where the user has to identify and enter into the system. If both images (Figure 6(a) and (b)) are available then we can completely reconstruct the 3D model of the Opel Corsa. The white circles in Figure 6 are the points of the polygon where the user has to identify and enter into the system.

the hood and driver-side door patterns and we will not know that there are also patterns on the trunk and the passenger-side door. This may lead to an incorrect 3D model reconstruction. To increase our chances for obtaining a correct 3D model, we can make certain assumptions (e.g., that the flame mark exists on the doors on both side of the car) and generate several candidate models for recognition.

Figure 5. Audi Q7 with strips on the hood Figure 5 shows an Audi Q7 that has stripes running down the hood. The four points in red (on the hood near to the stripes) in Figure 5 are the four points that the human user needs to enter into the system.

Figure 10. Opel Corsa with warped image of green stripes

To perform the texture mapping, we need to attach the warped image to the UVW map of the car. Here, the human user has to enter another 4 points on the UVW map to show where the warped image will go. Figure 11(b) shows the 4 points that the human user have entered. After we have downloaded the corresponding car model, we need to perform a UVW unwrap operation (from any 3D modeling software such as 3DSmax) to obtain the UVW map of the model. The UVW map allows us to perform texture mapping onto the 3D car model. An example of the UVW map of the Audi Q7 is showed in Figure 11(a).

Figure 9. Opel Corsa with warped image of flame pattern Figure 8(a) is original car image of Audi Q7 which is not modified at all. The Figure 8(b) is the warped image of extracted black stripe mark from Figure 8(a).

Figure 12. Completed UVW map of the Audi Q7 ATter entering the 4 points, the warped Image Wl! be automatically resized and then “pasted” onto the UVW map. In addition, the human user needs to specify one more point in the image (that has the color of the body of the car) so that all the panels in the UVW can be filled with this color. This completes the texture mapping process on the UVW map and we are ready to reconstruct the 3D car. Figures 12, 13 and 14 show the completed UVW maps of all the three cars in our experiment.

With the information on the car position and the camera position, we can generate the expected view of the car from CCTV 2 using the gluLookat function in the OpenGL library. By rotating the 3D reconstructed model, we can generate many arbitrary views of the car at different angles.

Figure 16 shows the results of reconstructing the 3D model of the three cars to the same view as the original images so that we can verify that the result is correct.

Figure 14. Completed UVW map of the Opel Corsa with Green Stripes Figures 13(a), 13(b) and 13(c) are completed UVW map of Opel Corsa with flame pattern. Each of them is generated by attaching warped images of part D to the region of UVW which is chosen by user. Figure 9(b) is used for Figure 14(a), Figures 9(c) and 9(e) are used for Figure 14(b), and Figure 9(f) is used for Figure 13(c). Figures 14(a), 14(b) and 14(c) are the completed UVW map of the Opel Corsa with green stripes. The resized Figures 10(b), 10(d), and 10(e) are attached to corresponding position of UVW map of Opel Corsa.

Figure 13. Completed UVW map of the Opel Corsa with Flame pattern each other, the algorithm performs resizing process before pasting.

Figure 18. Merging with CCTV background image We can see that the car images blend in very well to the CCTV background image and the resulting image looks quite realistic. We also showed a zoomed-in image of the Opel Corsa with green stripes to show that the

Figures 16(a), 16(c), 16(e), 16(g) and 16(i) are the raw car images of Audi Q7 and Opel Corsa which are same with Figures 8(a), 9(d), 9(a), 10(a) and 10(c) each. Figures 16(b), 16(d), 16(f), 16(h) and 16(j) are the reconstructed 3D model of each car.

Figure 17 shows the results of generating new arbitrary views of the three cars. Figures 17(a) and 17(b) are the arbitrary view of Audi Q7. Figures 17(c), 17(d), 17(e) and 17(f) are different view of Opel Corsa with flame pattern and green stripe. We can see that all the three cars in our experiment look realistic.

Figure 1. A conceptual view of a Disaster Warning System The main goal of a disaster warning system is preventing loss of properties by providing suitable information to people who can be affected by disaster events [2][7]. To achieve this goal, the system should analyze the impact of disaster events in detail, pick up all relevant people to the disaster, prepare proper messages and send the messages reliably in timely manner.

Figure 2. The architecture of the proposed disaster warnding system disaster warning system. Finally, the failure-resilient geo- aware dissemination module is related to the dissemination component of a disaster warning system.

Figure 4. Propagation process with different channel preferences When the message propagation is considered, we can consider the multi-modal channels for the message propagation, because users usually use various channels to propagate messages through social connections [6]. In an emergency situation, the message propagation may enhance the possibility that a message reaches to the target user within a given time limit.

Figure 3. Performance of predicting user attitute (E: Extraverstion and I: Introversion) in MBTI (Myer-Briggs Type Indicator). SVM (Support Vector Machine) is used as a classifier and each location clustering technique is used to extract classification features.

Figure 5. Reliability of message dissemination under large failures In a disaster event, there is a high probability that the network devices are ruined. In this situation, how to provide a reliable sending method is also important. For example, a mobile phone is a reliable method to send the message to user in the normal case. But in a disaster, the mobile phone can be ruined or the user cannot get the message. In this case, we need to choose another way to send the message to the user. Also, it is possible that the network devices such as switches or routers are destroyed. To send message to the user, we might have to reroute the message to another router.

the location misclassification rate weighted by the priority and visiting frequency is minimized. Intuitively, the more fingerprints a) collected the better we can estimate the mean at that lovation Aj. and the less the misclassification rate (refer Section II.B for the relations). By the maximum likelihood method, our estimates of Ay. is the sample mean of fingerprints ao), which is normally distributed with mean Aj. and covariance 1 Mis where n,; is the number of fingerprints collected. By Assumption 1, %, is the same for all locations i € [N]. Consider m and I to be neighboring two TeBIONS, then by applying the results in (7) and (9) with U,, = a and yy) = oH d (dis diagonal with entries c? by Assumption 1),

is based on the HBSM (line 5, see Section II) and weighted misclassification cost (line 8, see Section III).

Figure 4. Results of the test using each advertisement signal. Next, we evaluated the performance of the proposed method. We added an advertisement signal to the sound component of the TV content, which was a K-pop music video. We used 19kHz, 20kHz, and 21kHz as the first high frequency and 18kHz as the second high frequency. Each advertisement signal was generated twice at two-minute intervals. Thus, we did the receiving test 50 times for each advertisement signal. Because the advertising application checks high frequencies of FFT per 10ms, we set each threshold value in such a way that k was 12 times (60% of the first high frequency length) and i was six times (60% of the second frequency length). The distance between the smart TV and the smart device was 3m and the volume level of the smart TV was 70dB. The test space measured 5m x 4m, and 40dB was maintained as the silence space. Figure 4 below shows the result of the test using each advertisement signal. In Figure 4, when the first high frequency was 19kHz, the smart device received the advertisement signal 50 times. Using 20kHz as the first high frequency caused the signal to be received 49 times, 21kHz caused it to be received 47 times, and so on. Thus, the total success rate averaged 97%. This shows that transmission errors increase in line with the rising value of the first high frequency. We believe the reason for this is that the bin number of the higher frequency detected was lower than the bin number of the lower frequency over the same distance and with the same number of decibels. The next task was to test performance according to distance. The distance between the smart TV and the smart device was from 1m to 5m in Im intervals, and we did the receiving test 50 times with each advertisement signal (19kHz, 20kHz, and 21kHz) and at each distance. The test space and every threshold value were the same. Figure 5

Figure 5. Result of advertisement transmission test according to distance. below shows the results of the advertisement transmission test according to distance.

Figure 4. Overall error rates for the Skeys-MAP, MsWord, and iSpell algorithms, when considering all error types and only substitution errors, respectively In Table II, we show the initial error distribution and how many errors of each type were not corrected by the algorithm. It can be noticed that the correction method is less efficient for deletions, when a word has been split, and for combined error types. In the case of deletions, the typed

Figure 2. Chording keyboard prototype used during the typing study

Figure 3. The interface of the Java application used during the study A Java application was designed to display the text to be typed and to monitor the pressed keys. A screenshot of the application is shown in Figure 3. The top-left window contains the text to be typed and the bottom-left window represents the typing area. The help image is displayed on the right. Because we wanted to evaluate an error correction mecha- nism, we instructed the participants not to correct their mis- takes (however, this was not enforced and they could delete typed text). As a reward for the time commitment during the experiment, they received a fixed monetary compensation for the first nine typing sessions. To provide additional motivation, for the last session, the reward was proportional to the number of typed words and to the typing accuracy.

The proposed work is justified by the importance of the teacher to follow, throughout the course, the implementation process, avoiding a posteriori analysis, i.e., with the results, the teacher is able to give special attention to students with difficulties in constant learning, directly, without requiring more than a tool for analysis. Introdugao a Integragao de Midias na Educagao

Figure 1. Schematic representation of the 4 main artifacts of map matching applied to dense traces. For each case the artifact is represented to the left and contrasted with the ground truth trajectory, to the right.

Figure 3. Projected trace points plotted on top of the satellite map showing examples of Case-1: (a) before and (b) after match enhancement

Figure 5. Projected trace points plotted on top of the satellite map showing examples of Case-3: (a) before and (b) after match enhancement. Figure 4. Projected trace points plotted on top of the satellite map showing examples of Case-2: (a) before and (b) after match enhancemen

Figure 6. Projected trace points plotted on top of the satellite map showing examples of Case-4: (a) before and (b) after match enhancement.

Figure 7. Projected trace points plotted on top of the satellite map showing examples of Case-4: (a) before and (b) after match enhancement.

Figure 1. INERTIA framework computational capacity) that are driven simultaneously to cooperate with each other as in Figure 1.

Figure 5. Personal user interface d) HISTORICAL view: user monitors the historical data about consumption at different time; c) CALENDAR view: user submits to INERTIA world his habits and commitments such as for example “surgey in operating room from 11:00 am to 3:00 pm” or “training every Monday from 6:00 pm to 9:00 pm in the workout room”; All the components of the user interface architecture must communicate with each other. The connection between Presenter and View uses a socket connection. A socket is an end-to-end link over a single TCP connection, having the following features:

TABLE IV. COMPARISON OF ROOT MEAN-SQUARED ERROR OF ESTIMATION IN OCCUPANTS EXPERIMENTS to yield different outputs due to different training, sensing by proxy will output the same value given the chosen parameters, which is desirable since it is less susceptible to training noise.

TABLE II. EXAMPLE OF CONTENT GENERATION to resemble the process of a human using the Web to find other information relevant to these POIs. Finally, augmented reality is used in combination with facet selection to present this POI-related information to an active tourist on her/his mobile device. Similarly to our scenario, this application aims at dy- namic provision of semantically-enriched information in favor of a classical travel guide. In contrast, our scenario introduces both nearby and faraway POIs, which are semantically related within a variety of historical objects, including common histor- ical persons and events. Our scenario supports automation of semantic crawling with POI ranking; the ranks are then used for visual representation of POI recommendations to the user.

TABLE I. FEATURE OVERVIEW. Legend: +: supported, o: partly supported, -: not supported.

TABLE I. GROUP DEMOGRAPHICS. We recruited a total of 270 US-based workers to participate in our experiment. Note, that these are different participants than the participants described in Section III-A. We also con- fined our user study to those who are familiar with the Android Patterns. Demographics of the participants and number of participants are given in Table I. The user study procedure involved two main steps a) Registration - Participants were assigned randomly to one of the groups, i.e, NORMAL, EPSM or BLINK. After that, they were asked to choose a pattern and then to verify it immediately. All participants were instructed to imagine that they have received a new phone and would like to set an Android Pattern on it. They were asked to choose a pattern of a minimum length four. b) Survey - After creating a pattern, participants were asked to complete a survey. We paid $0.40 to each participant upon the completion of our user study.

Table I shows the data measured from Figure 6. While the Op ratio is not constant throughout the entire image, the variation was found to be quite small (0.001°/pixel) between the two cases where the object is in the image center and at the image edge.

TABLE HI. STATIONARY PEDESTRIAN “ABLE IV. PERPENDICULARLY MOVING PEDESTRIAN

TABLE I. NOTATIONS AND SHORTHANDS REFERENCE. where X®SS!(q) in Decibel (dB) is the Received Signal Strength Indicator (RSS) at distance d, dp is the reference distance, 7 is the path loss exponent (PLE), and «, ~ (0,07) is a random variable reflecting the attenuation caused by flat fading [14].

TABLE I. Measurement statistic characteristics of a node at (1031.713. 2778.091)

BLE I. ASSIGNED FREQUENCIES FOR EACH HEXADECIMAL DIGIT Another researcher, Chung, proposed a near wireless control technology using high frequencies [12][13]. In this method, he defined high frequencies as base signals and low latency as a control signal. Base signals comprise more than two high frequencies and note the existence of the control signal to the smart device. Then, because low latency accesses various control data by frequency value, low latency is generated by the Central Processing Unit(CPU) of the smart device. The distance of the control signal in this method is greater than in Bihler’s method and the data transmission time is faster than in Lee’s method. Thus, Chung’s method can be used as a trigger signal for the effective transmission of advertising information.

Figure 1. Environmental progress and block.

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Extending a Middleware for Pervasive Computing to Programmable Task Management in an Environment of Personalized Clinical Activities

by Alencar Machado

2015

Abstract: Currently, Pervasive Computing has focused on the development of programmable and interactive environments, which are intended to help the user in daily activities. The health system of the future envisages the use of Pervasive... more

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Estimación de la similitud semántica de tareas entre procesos de negocio de telecomunicaciones

by Revista Ingeniería y Ciencia and

2014

"Como una medida para mejorar la competitividad en el mercado de las telecomunicaciones, actualmente las empresas del sector crean nuevos servicios de valor agregado, con el fin de ampliar su portafolio de servicios y garantizar bien sea... more

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An offer evaluation system based on buyers' interests.

by Samira Sadaoui

2014, Proceedings of the 2011 ACM Symposium on Applied Computing (SAC)

Matchmaking systems failed to provide the best matched offer to each individual. In this paper, we develop an interest-based offer evaluation system for semantic matchmakers. Our system returns the best offer by sorting the... more

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An Aspect-Based Resource Recommendation System for Smart Hotels

by Aitor Almeida

2013

Abstract: The number of resources (services, data, multimedia content, etc) available in Smart Spaces can ver overwhelming. Finding the desired resource can be a tedious and difficult task. In order to solve this problem, Smart Spaces... more

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Extending a Middleware for Pervasive Computing to Programmable Task Management in an Environment of Personalized Clinical Activities

by Fernando Silva

2013

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A data-intensive lightweight semantic wrapper approach to aid information integration

by Nigel Shadbolt

2013

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Continuous evaluation in the process of ontology development

by Dejan Lavbič

2013

Abstract: Due to complexity of existing methodologies for ontology development we propose facilitating ontology development with continuous evaluation of steps in the process of ontology development. The approach is called Rapid Ontology... more

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Semantic Matchmaking

Key research themes

1. How can semantic matchmaking improve personalized service recommendation in contextual and dynamic environments?

2. What inference mechanisms and logical frameworks optimize semantic matchmaking performance in constrained or resource-limited environments?

3. How can semantic matchmaking frameworks address large-scale query result challenges, including plethoric answers, by identifying and managing failure-inducing query components?

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