Data Manipulation

This paper explores a novel correlation between prime numbers and nuclear synthesis, proposing a theory where primes form the foundation of nuclear fusion and elemental abundance in the universe. By simulating fusion and fission using prime number based rules, we demonstrate a correlation between our model and observed isotopic abundances. This suggests a potential framework for understanding nuclear processes and the evolution of the cosmos.

Purpose This study investigates which dimensions of the management control system (MCS) increase the perception of organizational justice and reduce unethical behavior in the perception of managers. The purpose of this paper is to validate the theoretical model of the study of Langevin and Mendoza (2012), testing the theoretical hypotheses formulated by the authors. Design/methodology/approach A survey was performed in companies listed among the Best and Largest of Exame Magazine, and the sample is composed of 102 respondents of the research, which consists of 41 assertions. Findings The results of the structural equation modeling show that the definition of objectives increases the perception of procedural justice, but the same was not observed regarding the remuneration of the managers. Likewise, disregarding aspects that are uncontrollable by managers in performance evaluation does not lead to the perception of procedural and distributive justice. However, feedback quality leads ...

Voglio scrivere qualche riga che rimanga a testimonianza su un tema che reputo importante: la relazione tra emissione energetica solare (altrimenti chiamata in linguaggio tecnico TSI = Total Solar Irradiance) e temperatura globale del pianeta.

This PowerPoint presentation presented at the 2006 Atlantic Provinces Political Science Association Annual Conference visually shows how a key element of gerrymandering works. Instead of redrawing state legislative or congressional districts, I have redrawn state borders. They have been altered so that the outcome of the 2000 presidential election has been changed.

The redrawn states are contiguous and compact. My methodology is to reallocate contiguous counties to and from states as a technique to show how election outcomes can be altered.

In particular, I have changed the borders of five states that Bush won so that they become Gore states. They are New Hampshire, West Virginia, Florida, Missouri and Nevada.

I have also altered the borders of five states that Gore won so that they become Bush states. They are New Mexico, Wisconsin, Iowa, Oregon and Michigan.

I also use data sets which will include

• The net shift of votes (from “old” states to “new” states)
• The new margin of victory (within each “new” state)
• The new Electoral College vote count.

This shows the conceptual interactions between geographical boundaries, political power and political culture. It also shows how the urban, metropolitan and rural aspects (including population density) of a state interact with election outcomes.

This is not linguistic evolution. It is the result of colonial editing, where one letter decides which narrative you originate from: from "glory" (слава) or from "word" (слово), from a People of Farmers and Warriors or from an empire's slave, perpetually beaten and humiliated. Together with the Holodomor, this manipulation enables the upbringing of ideal slaves.
      When a Historian Becomes an Imperial Relayer, or Do I Fear the Left in Western UniSlavsversities Want to Build a Gulag?

Originally XMLw as used as as tandardp rotocolf or data exchange in computing. The evolutionofinformation technology hasopenedupnew situations in whichXML canbe used to author,m aintain, andd eliver contenta nd consequently,n ew applications of XML occurred. XMLs ervesa sadata modela nd background ford atabases of XML documents as well as fora pplications beyond today'sd atam odels (hierarchical structures,r ecursives tructures, regular expressions).X ML also playsasignificantr ole as atechnological platform forSemantic web.

This paper reports on an ongoing Knowledge Graph generation project at the industrial automation company Festo. The project aims at establishing a well defined and easily traceable extract, transform, load (ETL) process of technical data on automation products into Festo’s semantic data platform (FSP) to serve complex configuration tasks. Starting with a brief illustration of the data sources and requirements we describe a staged Knowledge Graph generation process that draws on common data extraction techniques as well as on specific semantic transformation methods such as the mapping framework R2RML or ontology reasoning. We will report on the project status as well as discuss our design decisions towards a reusable FSP Knowledge Graph data ingestion process.

Despite increasingly distributed internet information sources with diverse storage formats and access-control constraints, most of the end applications (e.g., filters and media players) that view and manipulate data from these sources operate against a traditional file-based interface. These legacy applications need to be rewritten to access remote sources, or need to rely upon ad hoc intermediary applications that aggregate the data into a passive file before executing the legacy application. This paper presents a simple, elegant, programmable method for allowing natural integration of legacy applications into distributed system infrastructures. The approach called active files, enables multiple information sources to be encapsulated as a local file that serves as their logical proxyw. This local file is accessed though a sentinel process, which automatically starts when the file is opened, aggregates data from multiple sources, and filters all access to and from the file. More importantly, the integration of active files into client applications is transparent: an active file is virtually indistinguishable from a regular file. Active files find a variety of applications in both distributed and nondistributed systems. We discuss active files, their semantics, their usage and their implementations in Windows NT.

The multicore architecture has been the norm for all computing systems in recent years as it provides the CPU-level support of parallelism. However, existing algorithms for processing XML streams do not fully take advantage of the facility since they have not been devised to run in parallel. In this article, we propose several methods to parallelize the finite state automata (FSA)-based XML stream processing technique efficiently. We transform a large collection of XPath expressions into multiple FSA-based query indexes and then process XML streams in parallel by virtue of the index-level parallelism. Each core works only with its own query index so that no synchronization issue occurs while filtering XML streams with multiple path patterns given by users. We also present an in-memory MapReduce model that enables to process a large collection of twig pattern joins over XML streams simultaneously. Twig pattern joins in our approach are performed by multiple H/W threads in a shared and balanced way. Extensive experiments show that our algorithm outperforms conventional algorithms with an 8-core CPU by up to ten times for processing 10 million XPath expressions over XML streams.

For integrating relational databases (RDBs) into semantic web applications, the W3C RDB2RDF Working Group recommended two approaches, Direct Mapping (DM) and R2RML. The DM provides a set of mapping rules according to RDB schema, while the R2RML allows users to manually define mappings according to existing target ontology. The major problem to use R2RML is the effort for creating R2RML mapping documents manually. This may lead to appearance of many mistakes in the R2RML documents and requires domain experts. In this paper, we propose and implement an approach to generate an R2RML mapping documents automatically from RDB schema. The R2RML mapping reflects the behavior of the DM specification and allows any R2RML parser to generate a set of RDF triples from relational data. The input of generating approach is DBsInfo class that automatically generated from relational schema. An experimental prototype is developed and shows the effectiveness of our approach algorithms.

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

We propose a fraud detection systems (FDS) for AMI based on anomaly detection on the energy consumption reports from smart meters. Fraud inspection is triggered when a discrepancy between the energy supplied by the grid and that reported by smart meters is detected. We use an innovative approach, where consumption reports registered shortly before and after the discrepancy detection are compared to detect a fraud. Our FDS introduces an important innovation by showing that it is possible to use only a small set of recent measures to define a consumption pattern. We call these patterns shortlived because they are expected to represent the behaviour of a consumer for a short period, only enough to detect an ongoing fraud. This approach allows the FDS to account for natural changes in the consumption behaviour of users and also helps to preserve their privacy. The FDS can be tuned, using an optimization procedure, by imposing constraints on the true or false alarm rates, or maximizing an objective function that represents the revenue of the utility.

New immersive devices (e.g., virtual or augmented reality) enable displaying large amounts of data in space to better support data analysis. Manipulating this data efficiently is crucial, but challenging because the user must be able to activate various commands or adjust various values while remaining free to move. Using the whole body offers several valuable advantages: 1) The body provides a physical support as an interactive surface, which improves accuracy and makes it less tiring to interact; 2) Using the body does not impair mobility and avoids handling devices; 3) Proprioception makes it possible to interact eyes-free, including for choosing values in a range; 4) By leveraging spatial memory, the body helps memorizing commands, thus interacting in expert mode (i.e., perform quick actions without visual feedback). In this position paper, we analyze various ways of interacting with the body and discuss their advantages and challenges for immersive analytics.

Figure 1: (a): A line graph representing heart rate over six minutes and including 300 data points; (b): we simplify the line graph in (a) using the Space-Filling Line Graph (SF-LG) technique; (c) & (d): the available space created around the line graph can be used to fill auxiliary, interlinked information. In our approach, we can provide the algorithm a priority to position the information. The labels "1", "2", "3" represent the priority assigned to those components which are given the maximal space; (e) & (f): embedding auxiliary and interrelated graphs that augment the primary line graph becomes possible based on the reorganization of the available space

Figure 1: (a): A line graph representing heart rate over six minutes and including 300 data points; (b): we simplify the line graph in (a) using the Space-Filling Line Graph (SF-LG) technique; (c) & (d): the available space created around the line graph can be used to fill auxiliary, interlinked information. In our approach, we can provide the algorithm a priority to position the information. The labels "1", "2", "3" represent the priority assigned to those components which are given the maximal space; (e) & (f): embedding auxiliary and interrelated graphs that augment the primary line graph becomes possible based on the reorganization of the available space

In this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions.

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The article deals with a design of a system that provides tools for creation of automatically provisioned embedded communication system and its components. As the key feature of the BEESIP platform (Bright Efficient Embedded Solution for IP Telephony) a unique building and provisioning system of the network devices has been developed allowing the administrators to fully control the firmware and configuration of the devices even in the remote and inaccessible locations. The process of custom firmware building and device provisioning eases the mass deployment of the BEESIP based hardware to cover the needs of small to medium business in the vast range of services.

Teknoloji kullanımın her geçen gün artmasıyla birlikte sosyal medya ve internet kullanımı hızla yaygınlaşmaktadır. Özellikle sosyal medya kullananların sayısının artmasıyla insanlar duygu ve düşüncelerini, gündemle ilgili görüş ve fikirlerini sosyal medya araçlarını kullanarak paylaşmaktadır. En çok kullanılan sosyal medya araçlarından biri olan Twitter ve Facebook gibi sosyal ağ uygulamalarında paylaşılan içerikler yapısal olmayan veri türleridir. Bunun gibi yapısal olmayan metin içerikli verilerin yapısal hale dönüştürülmesinde ve analiz edilmesinde metin madenciliği yöntemleri kullanılmaktadır. Bu çalışmada, 24 Haziran Cumhurbaşkanlık ve Milletvekili Seçimlerinde 'Tamam' ve 'Devam' etiketleriyle atılan tweetler metin madenciliği yöntemleri kullanılarak analiz edilmiştir. Metin ön işlem aşamasında kelimelerin köklerini bulmak için Zemberek kök bulma yöntemi ve ağırlıklandırma yöntemi olarak ise terim frekansı (TF) ağırlıklandırma yöntemi uygulanmıştır. Oluşturulan sınıflar arasındaki ilişkiyi ölçmek amacıyla korelasyon analizi yapılmıştır. Kruskal Wallis Testi ile frekans terim ağırlıklarının sınıflara göre farklılığı test edilmiştir. Elde edilen özelliklerin terim frekansı (TF) değerlerine göre kelime bulutları oluşturulmuştur.

Recent state-of-the-art approaches and technologies for generating RDF graphs from non-RDF data, use languages designed for specifying transformations or mappings to data of various kinds of format. This paper presents a new approach for the generation of ontology-annotated RDF graphs, linking data from multiple heterogeneous streaming and archival data sources, with high throughput and low latency. To support this, and in contrast to existing approaches, we propose embedding in the RDF generation process a close-to-sources data processing and linkage stage, supporting the fast template-driven generation of triples in a subsequent stage. This approach, called RDF-Gen, has been implemented as a SPARQLbased RDF generation approach. RDF-Gen is evaluated against the latest related work of RML and SPARQL-Generate, using real world datasets.

A well-functioning procurement system is an essential requirement for a developing country like Kenya. In order to promote development, proper management of scarce resources is vital. A system that ensures transparency, efficiency, cost effectiveness, easy to control and monitor can accelerate development to great heights. It will encourage fair competition and freedom of information amongst trading partners in a regional block. This can be achieved by using an electronic government procurement system realized as a web based internet portal. This paper explores the design methodology applicable to the design and implementation of an electronic Government Procurement System (e-GP). The paper further shows how Object Oriented technology can be applied to model the business artifacts of the system through the Unified Modeling Language (UML). The internet provides a lot of information through the web that makes better decisions to be made quickly.

İnternet kullanımındaki artışa paralel olarak dünyanın farklı noktalarındaki insanlar farklı konulardaki duygu ve düşüncelerini sosyal medyalar aracılığıyla kolay bir şekilde aktarabilmektedirler. Bu sosyal medyalar içerisinde önemli bir yere sahip olan Twitter vasıtasıyla küresel ölçekte her gün çeşitli konularda milyonlarca mesaj yazılmakta ve okunmaktadır. Firmaların rekabet gücünü artırmak açısından tüketici davranışlarını anlamak önemli bir konu iken Twitter gibi büyük veri kaynakları, davranışların analiz edilebilme yöntemlerini çok yönlü ele almaktadır. Aynı zamanda gelişmiş ülkeler güç sahibi olmak için veri madenciliği projelerine önemli kaynaklar ayırmaktadır. Çalışmada veri madenciliği algoritmaları kullanılarak mobilya tercihindeki eğilimleri belirlemek için bir sosyal medya ağı olan Twitter'da yapılan ilgili paylaşımlar değerlendirmeye alınmıştır. Bu kapsamda Rapidminer ve doğal dil işleme yazılımları kullanılarak içinde mobilya geçen popüler tweetler Mayıs 2018-Şubat 2019 tarihleri arasında on ay boyunca toplanmış ve doğal dil işleme yazılımları sayesinde tweetlerin duygu durumları (pozitif ve negatif) belirlenmiştir. Daha sonra pozitif ve negatif tweetlerde geçen anahtar kelimelerin morfolojik analizleri gerçekleştirilmiştir. Son olarak veri madenciliğinde kullanılan karar ağacı ve birliktelik algoritmalarından faydalanarak anlamlı bilgiler elde edilmiştir. Karar ağacı algoritmasına göre pozitif veya negatif duyguların oluşumunda itiraz, kampanya, keşfetmek ve fikir gibi kelimelerin baskın olduğu belirlenmiştir. Birliktelik algoritması sonucunda ise en pozitif duyguları uyandıran kelimelerin sipariş ile yapılmış, fırsat ve ahşap gibi ifadeler olduğu tespit edilmiştir. Aynı algoritmada en negatif duyguları uyandıran kelimeler ise kasvet, keyifsiz, rahatsız ve kumaş olarak sıralanmıştır.

With the rapid advancement of networking technologies, security system has become increasingly important to academics from several sectors. Intrusion detection (ID) provides a valuable protection by reducing the human resources required to keep an eye on intruders, improving the efficiency of detecting the various attacks in networks. Machine learning and deep learning are two key areas that have recently received a lot of attention, with a focus on improving the precision of detection classifiers. Using defense anvance research project agency (DARPA"98) datasets, a number of academics and research have developed intrusion detection systems. This paper discusses various approaches developed by different researchers, including scale-hybrid-IDS-AlertNet (SHIA), forward feature selection algorithm (FFSA), modified-mutual information feature selection (MMIFS), deep neural network (DNN), and the holes that remain to be filled, highlighting areas where these procedures can be improved, also are addressed and the proposed approach improved deep convolutional neural network (IDCNN) is compared with existing approach.

User-maintained mode is often adopted in facilitating a bimanual interaction using multitouch devices, in which a thumb of a non-dominant hand holding the device can press a part of the screen and keep pressed in order to activate and maintain a temporary mode that is required only for a short period of time, until the user releases the thumb. In this paper, we characterise this particular interactivity and propose novel locking mechanism that supports easy swapping between primary and secondary modes enabling further useful bimanual interaction mechanism that we call reversed mode relationship. We design three prototypes that employ this interactivity and conduct a usability testing with 27 participants. Findings suggest that this interaction works better in applications where there are frequent but short and non-intensive sub-tasks that interrupt the main workflow. We use this insight to further explore more advanced ways to support such interaction. User interfaces, interaction design, user-maintained mode, mode locking, mode reversing, moded design.

Recent advances in linked data generation through mapping such as RML (RDF mapping language) allows for providing large-scale RDF data in a more automatic way.However, considerable amount of data in open data portals remain inaccessible as linked data.This is due to the nature of data portals having large number of small-size dataset which makes writing mapping description becomes tedious and error-prone. Moreover, these data sources requires additional preprocessing before To solve this challenge, We introduce extensions to RML to support required tasks and developed RMLx, a visual web-interface to create RML mappings.Using this interface, the process of creating mapping description can become faster and less error-prone.Furthermore, the process of linked data generation can be wrapped as to enable integration with other data in a linked data exploration environment. We explore on four different use cases to identify the requirements followed by describing how these are solved.

Traditionally, manipulation has been considered as an act that necessarily takes place somewhere in the background, in secret, "backstage", in the dark, so to speak, in the "unconscious" part of our social actions. Such an understanding of manipulation thus suggests, psychoanalytically, that manipulation is fundamentally constituted by a logic of the unconscious, which must be suppressed, concealed, and camouflaged, something that resists being easily uncovered. However, in the post-communication era manipulation has taken a step further. Encouraged by big data technologies, pseudocommunication strategies, digital factories of fake news and lies, pseudo-journalism, industries of viral mystification, fabricating and disinforming media, and by related complex systems of deceiving, disguising, blurring, simulating, falsifying, distorting, diverting, mispackaging, deforming, misrepresenting and misusing the reality that have colonised all spheres of social life, from politics, business, media, mass communications industry, public sphere to interpersonal communication, manipulation has recently taken on a new form: that of deep manipulation. This term aims at the increasing, intense and omnipresent naturalisation of manipulation, which has brutally invaded the territories of communication between people at both individual and collective levels, moulding it into its tool. In such a world of perverted communication, the goal of using communication is not "plain communication" but the constant production of manipulation by performing it as our "new communication". But this is not the end of the story of deep manipulation operating both in depth and at the capillary level, both

The injection of interface faults through API parameter corruption is a technique commonly used in experimental dependability evaluation. Although the interface faults injected by this approach can be considered as a possible consequence of actual software faults in real applications, the question of whether the typical exceptional inputs and invalid parameters used in these techniques do represent the consequences of software bugs is largely an open issue. This question may not be an issue in the context of robustness testing aimed at the identification of weaknesses in software components. However, the use of interface faults by API parameter corruption as a general approach for dependability evaluation in component-based systems requires an in depth study of interface faults and a close observation of the way internal component faults propagate to the component interfaces. In this work we present the results of experimental evaluation of realistic component-based applications developed in Java and C using the injection of interface faults by API parameter corruption and the injection of software faults inside the components by modification of the target code. The faults injected inside software components emulate typical programming errors and are based on an extensive field data study previously published. The results show the consequences of internal component faults in several operational scenarios and provide empirical evidences that interface faults and software component faults cause different impact in the system.

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a data set, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse data sets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitation...

In this paper the behavior of assertion-based error detection mechanisms is characterized under faults injected according to a quite general fault model. Assertions based on the knowledge of the application can be very effective at detecting corruption of critical data caused by hardware faults. The main drawbacks of that approach are identified as being the lack of protection of data outside the section covered by assertions, namely during input and output, and the possible incorrect execution of the assertions. To handle those weak-points the Robust Assertions technique is proposed, whose effectiveness is shown by extensive fault injection experiments. With this technique a system follows a new failure model, that is called Fail-Bounded, where with high probability all results produced are either correct or, if wrong, they are within a certain bound of the correct value, whose exact distance depends on the output assertions used. Any kind of assertions can be considered, from simple likelihood tests to high coverage assertions such as those used in the Algorithm Based Fault Tolerance paradigm. We claim that this failure model is very useful to describe the behavior of many low-cost fault-tolerant systems, that have low hardware and software redundancy, like embedded systems, were cost is a severe restriction, yet full availability is expected.

Large vertical displays are increasingly widespread, and content sharing between them and personal mobile devices is central to many collaborative usage scenarios. In this chapter we present FlowTransfer, bidirectional transfer techniques which make use of the mobile phone's position and orientation. We focus on three main aspects: multi-item transfer and layout, the dichotomy of casual versus precise interaction, and support for physical navigation. Our five techniques explore these aspects in addition to being contributions in their own right. They leverage physical navigation, allowing seamless transitions between different distances to the display, while also supporting arranging content and copying entire layouts within the transfer process. This is enabled by a novel distance-dependent pointing cursor that supports coarse pointing from distance as well as precise positioning at close range. We fully implemented all techniques and conducted a qualitative study documenting their benefits. Finally, based on a literature review and our holistic approach in designing the techniques, we also contribute an analysis of the underlying design space.

The Navy's Advanced Avionics Subsystem Technology (AAST) Fault Tolerant program is clarifyin the Navy's fault tolerant avionics specifications mekods and acceptance tests. The goal of the program will be clarify the Specification and Statement of Work language needed in future procurements and to demonstrate fault tolerant validation tools on an avionics design. A set of tool features will then be developed that spans the needs of fault tolerant computer system design from early concept studies to full scale production and operational support, both hardware and software. The paper will give an overview of the AAST Fault Tolerant Demonstration and focus on two tools that are being used in the demonstration: FERRARI a software fault injector that will be used to validate the fault tolerance of the Common Integrated Processor (CIP), the F-22 Mission Processor and GRIND a concept evaluation tool that will be used to evaluate the overall CIP architecture.

The recent surge of popularity has established Mashups as an important category of Web 2.0 applications. Mashups are essentially Web services that are often created by end-users. They aggregate and manipulate data from sources around the World Wide Web. Surprisingly, there are very few studies on the scalability and performance of mashups. In this paper, we study caching as a vehicle for enhancing the scalability and the efficiency of mashups. Although caching has long been used to improve the performance of Web services, mashups pose some unique challenges that necessitate a more dynamic approach to caching. Towards this end, we present MACE-a cache specifically designed for mashups. In designing the MACE framework this paper makes three technical contributions. First, we present a model for representing mashups and analyzing their performance. Second, we propose an indexing scheme that enables efficient reuse of cached data for newly created mashups. Finally, this paper also describes a novel caching policy that analyzes the costs and benefits of caching data at various stages of different mashups and selectively stores data that is most effective in improving system scalability. We report experiments studying the performance of the MACE system.

Recently, mashups are gaining tremendous popularity as an important Web 2.0 application. Mashups provide end-users with an opportunity to create personalized Web services which aggregate and manipulate data from multiple diverse sources distributed across the Web. However, this increase in personalization also results in new scalability and performance challenges. Surprisingly, there are very few studies on the performance aspect of mashups. In this paper, we propose two novel techniques to enhance the scalability and performance of mashup platforms. The first is an efficient mashup merging scheme that avoids duplicate computations and unnecessary data retrievals by detecting common operator sequences in different mashups and executing them together. Second, we propose a canonical form-based mashup reordering scheme that not only transforms individual mashups to their most efficient forms but also increases the effectiveness of mashup merging. This paper also reports a number of experiments studying the benefits and costs of the proposed techniques.

Baby-foot, a popular 2v2 table soccer game, has become an essential part of employee leisure and team building in various companies, including Hays Brussels where I work since January 2021. The game has found its way into corporate environments to integrate new employees and foster team bonding.
The original ranking system used at Hays Brussels was based on Excel, calculating ranking based on points won per game. This method had significant limitations in terms of data management and scalability. Also, the point-based calculation method was inherently unfair and did not reward players who took risks in uneven matches. As a result, some players refused to team up with weaker opponents for fear of losing points. This behavior contradicted the game’s original spirit and made it difficult to integrate new players, who are usually new to baby-foot.
Player rejection and Excel’s limitations inspired the creation of an entirely new system, which is the subject of this thesis. It presents a comprehensive approach to developing an improved Elo ranking system for baby-foot games, encapsulated in a web application that enhances the player experience. In addition, this thesis aimed to create an all-in-one platform that allows players to fully engage in their gaming experience by providing a comprehensive view of their performance by leveraging the power of data visualization.
Considering the importance of data management, this thesis delves into cleaning, organizing, and processing data into a more efficient and structured database model. The Elo ranking system, used in various sports, was studied and adapted for baby-foot games. As a result, a new ranking algorithm created a fairer environment and ranked players based on their true performance.
This new algorithm adapts the traditional Elo system by incorporating new variables to adjust the probability of winning and the rating calculations. This approach proposes a new ranking system that evaluates players based on more parameters than just their final score. It also considers the strength of opponents and teammates, the difference in level, the number of games played, and the final score to give a fair representation of each player’s performance. To lay the groundwork for a more sustainable system, an automated data processing strategy using Python was proposed to process, store, and apply the new algorithm to the collected data.
To ensure the sustainability of the project, a web application has been developed on top of the data processing foundation. This application allows users to enter game results, access real-time rankings, and view relevant statistics. The application also provides real- time data visualization to help players better understand track and compare their performance.
The thesis concludes with a methodological reflection that discusses the successes and limitations of the project, and possible future directions. Possible improvements include implementing a team-based approach to calculating winning probabilities, a personalized recommendation system for balanced team compositions, and a more sophisticated predictive analysis based on player performance.
In conclusion, this thesis shows how data-driven solutions and data management can create an equal and more inclusive environment for baby-foot games at Hays Brussels

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a data set, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse data sets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitation...

A continuación, transcribimos una carta muy interesante que apareció en el número 24 de la revista Cultura Boliviana. Fue escrita por Pedro Domingo Murillo luego de la Revolución de La Paz, en el marco de la Guerra Separatista de América, también conocida como Revoluciones del Reino de Indias o Guerras de Independencia Latinoamericanas. En la carta, se revela lo que parece ser un arrepentimiento del caudillo revolucionario Pedro Domingo Murillo, quien manifiesta lealtad a la monarquía hispánica y fidelidad a las autoridades realistas, o en este caso concreto, el brigadier arequipeño José Manuel de Goyeneche.

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of "manually" creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload.

Modeling temporal database over relational database<br> using 1NF model is considered the most popular approach. This<br> is because of the easy implementation as well as the modeling and<br> querying power of 1NF model. In this paper, we compare a new<br> approach for representing valid-time temporal database (in<br> terms of structure and performance) to the main models in<br> literature with attribute and tuple timestamping. The<br> measurement of the performance is represented by the<br> processing time to get the required temporal data as well as the<br> size of the whole stored temporal data. A test has been performed<br> by running sample queries for the same data in the represented<br> models. Based on the tests, we have found that the new proposed<br> model required less time and used less disk space. Therefore, it is<br> more appropriate for modeling 1NF with interval-based<br> timestamping...

In this article, we investigate the problem of electricity theft attacks on smart meters when malicious customers (i.e., adversaries) claim injecting more generated energy into the grid to get more profits from utility companies. These attacks can be applied by accessing the smart meters monitoring renewablebased distributed generation (DG), and manipulating the reading. In this article, we propose approaches that rely on data sources with only a single generator (i.e., solar only) and multifuel type; and address the crucial effects of slight perturbations that the attacker can add, which can deceive the detector. In particular, this article introduces an efficient multitask deep-learning-based detector that offers a higher detection rate, copes with different fuel types, and uses only single data sources. The proposed detector incorporates months and days as two additional features to boost the performance and properly guide the model to successful detection. The proposed method is then extended to consider small perturbations that attackers may use to launch successful attacks. We conduct extensive simulations for two different detectors, one for solar DG and the other for multiple fuel types (i.e., solar and wind). Using a realistic dataset, the results reveal that the proposed recurrent neural network-based detectors identify adversaries at a higher rate than the existing solutions, even with minimal perturbations and different fuel types. INDEX TERMS Cyberattacks on smart grid, electricity theft, generation domain, deep-learning (DL)-based detector, small perturbations.

The controllability principle, which stipulates that managers should only be evaluated based on elements that they can control, has been widely studied in the management control literature, both from theoretical and empirical perspectives. It is based on the idea that the principle leads to better satisfaction of the managers through the neutralization of uncontrollable items in their performance assessment, because it brings fairness in the evaluation. The paper focuses on the managers' opinions concerning the relevancy of these neutralization processes, as regard three types of uncontrollable factors. It develops a conceptual framework to support hypotheses that managers wish the principle to be applied and conducts a survey of 265 French managers. Both quantitative and qualitative results show a significant difference according to whether uncontrollable factors are external to the company or internal (interdependencies and hierarchical decisions), offering new insight as concerns the rationale for the controllability principle.