Context models

In this paper we show how the notion of context has been refined in order to fulfill the requirements posed by a natural language processing system. We describe the context model of the speech translation system Verbmobil which has been derived from constraints given by various system components. In Verbmobil context is stored in the dialogue memory which is incrementally constructed by the dialogue processing component. We describe our context model and give sample representations. We also relate our model to state of the art approaches in linguistics.

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches...

A particularly voluminous dataset in molecular genomics, known as whole genome alignments, has gained considerable importance over the last years. In this paper, we propose a compression modeling approach for the multiple sequence alignment (MSA) blocks, which make up most of these datasets. Our method is based on a mixture of finite-context models. Contrarily to other recent approaches, it addresses both the DNA bases and gap symbols at once, better exploring the existing correlations. For comparison with previous methods, our algorithm was tested in the multiz28way dataset. On average, it attained 0.94 bits per symbol, approximately 7% better than the previous best, for a similar computational complexity. We also tested the model in the most recent dataset, multiz46way. In this dataset, that contains alignments of 46 different species, our compression model achieved an average of 0.72 bits per MSA block symbol.

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches...

Ubiquitous environments may become a reality in a foreseeable future and research is aimed on making them more and more adapted and comfortable for users. Our work consists on applying reinforcement learning techniques in order to adapt services provided by a ubiquitous assistant to the user. The learning produces a context model, associating actions to perceived situations of the user. Associations are based on feedback given by the user as a reaction to the behavior of the assistant. Our method brings a solution to some of the problems encountered when applying reinforcement learning to systems where the user is in the loop. For instance, the behavior of the system is completely incoherent at the beginning and needs time to converge. The user does not accept to wait that long to train the system. The user's habits may change over time and the assistant needs to integrate these changes quickly. We study methods to accelerate the reinforced learning process.

Immersive Participationentails massive participatory activities in the Internetengaging people, places and objects. This ispremised on the existence of an Internet of Things infrastructure supporting applications and services with the same richness of experience as the World Wide Web. This in turn presupposes the existence of models for establishing and maintaining context relations. Where these models do exist, they impose a limited interpretation of context relations in the presence of the inherent heterogeneous and dynamic characteristics of the supporting information. In this paper we introduce an approach towards establishing context relations through the use of an improved context relational model permitting a wider, more complete range of application specific scenarios. Additionally, wederive a measure of context proximity that considers the situation, attributes, relations, accuracy and heterogeneity of both the underlying information and the vast array of requirements for metrics supporting application problem domains.

In algorithmic information theory, the Kolmogorov complexity of an object is the length of the shortest computer program that produces the object as output. It is a measure of the computational resources needed to specify the object. However, Kolmogorov complexity is non-computable as such, it can only be approximately attainable. One of the most notable approximations are data compressors, since the bitstream produced by a lossless data compression algorithm allows the reconstruction of the original data with the appropriate decoder, and therefore can be seen as an upper bound of the algorithmic complexity of the sequence. In this paper, we evaluate the usage of the Normalized Compression (NC) as the compression measure for analysing various Virus DNA sequences and evaluate how it changes when substitutions and permutations are performed on the DNA sequences. Finally, we draw conclusions regarding the nature of these sequences.

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches, on a balanced and diverse benchmark, using a competitive level of computational resources. An efficient implementation of the method is publicly available, under the GPLv3 license.

The success of software systems highly depends on user engagement. Thus, to deliver engaging systems, software has to be designed carefully taking into account Acceptance Requirements, such as "70% of users will use the system", and the psychological factors that could influence users to use the system. Analysis can then consider mechanisms that affect these factors, such as Gamification (making a game out of system use), advertising, incentives and more. We propose a Systematic Acceptance Requirements Analysis Framework based on Gamification for supporting the requirements engineer in analyzing and designing engaging software systems. Our framework, named Agon, encompasses both a methodology and a meta-model capturing acceptance and gamification knowledge. In this paper, we describe the Agon Meta-Model and provide examples from the gamification of a decisionmaking platform in the context of a European Project.

We live in the days of social software where social interactions, from simple notifications to complex business processes, are supported by software platforms such as Facebook and Twitter. But for any social software to be successful, it must be used by a sizeable portion of its intended user community. This is fundamental for social software, but also a crucial point for most of the software systems in general, and the fulfillment of such (Usage) Acceptance Requirements critically depends on psychological, behavioral and social factors which may influence intrinsic and extrinsic motivations of the user. Operationalization techniques for Acceptance Requirements largely consist of making a game out of software usage where users are rewarded depending on the degree of their participation. The game, for instance, may be competitive or non-competitive, depending on the anticipated personality traits of intended users. Making a game out of usage is often referred to Gamification. It has ...

We propose a Virtual Coach for the gamification of participatory applications in complex socio-technical systems like Smart Cities and Smart Communities. In such participatory applications, the user community is an active and essential component. Users must voluntarily take up some tasks, in order to ensure the correct operation of the application according to its requirements and goals, which, in turn, delivers collective benefits to the community. In order to facilitate users, and support their sustained engagement in a participatory application, we use a Missions metaphor to describe those volunteering tasks. Our Virtual Coach is then responsible for selecting and recommending missions to users, based on a variety of factors, including the criticality of the corresponding tasks for the application purposes, the importance of the task for the individual user that should take it up, user profile characteristics, like personal preferences and skills, and the in-game incentives the user would earn by completing the mission.

Immersive Participationentails massive participatory activities in the Internetengaging people, places and objects. This ispremised on the existence of an Internet of Things infrastructure supporting applications and services with the same richness of experience as the World Wide Web. This in turn presupposes the existence of models for establishing and maintaining context relations. Where these models do exist, they impose a limited interpretation of context relations in the presence of the inherent heterogeneous and dynamic characteristics of the supporting information. In this paper we introduce an approach towards establishing context relations through the use of an improved context relational model permitting a wider, more complete range of application specific scenarios. Additionally, wederive a measure of context proximity that considers the situation, attributes, relations, accuracy and heterogeneity of both the underlying information and the vast array of requirements for metrics supporting application problem domains.

Requirements elicitation, analysis and modeling are critical activities for software success. However, software systems are increasingly complex, harder to develop due to an ever-growing number of requirements from numerous and heterogeneous stakeholders, concerning dozens of requirements types, from functional to qualitative, including adaptation, security and privacy, ethical, acceptance and more. In such settings, requirements engineers need support concerning such increasingly complex activities, and Requirements Engineering (RE) modeling tools have been developed for this. However, such tools, although effective, are complex, time-consuming and requiring steep learning curves. The consequent lack of acceptance and abandonment in using such tools, by engineers, paves the way to the application of RE techniques in a more error-prone, low-quality way, increasing the possibility to have failures in software systems delivered. In this paper, we identify main areas of lack of accepta...

Gamification is increasingly applied to engage people in performing tool-supported collaborative tasks. From previous experiences we learned that available gamification guidelines are not sufficient, and more importantly that motivational and acceptance aspects need to be considered when designing gamified software applications. To understand them, stakeholders need to be involved in the design process. This paper aims to (i) identify key requirements for designing gamified solutions, and ii) understand if existing methods (partially fitting those requirements) can be selected and combined to provide a comprehensive gamification design method. We discuss a set of key requirements for a suitable gamification design method. We illustrate how to select and combine existing methods to define a design approach that fits those requirements using Design Thinking and the Agon framework. Furthermore, we present a first empirical evaluation of the integrated design method, with participants including both requirements analysts and end-users of the gamified software. Our evaluation offers initial ideas towards a more general, systematic approach for gamification design.

Gamification is a powerful paradigm and a set of best practices used to motivate people carrying out a variety of ICT–mediated tasks. Designing gamification solutions and applying them to a given ICT system is a complex and expensive process (in time, competences and money) as software engineers have to cope with heterogeneous stakeholder requirements on one hand, and Acceptance Requirements on the other, that together ensure effective user participation and a high level of system utilization. As such, gamification solutions require significant analysis and design as well as suitable supporting tools and techniques. In this work, we compare concepts, tools and techniques for gamification design drawn from Software Engineering and Human and Organizational Behaviors. We conduct a comparison by applying both techniques to the specific Meeting Scheduling exemplar used extensively in the Requirements Engineering literature.

—We live in the days of social software where social interactions, from simple notifications to complex business processes , are supported by software platforms such as Facebook and Twitter. But for any social software to be successful, it must be used by a sizeable portion of its intended user community. Usage requirements are usually referred to as Acceptance Requirements and they have been studied in the literature both for general technology as well as software. Operationalization techniques for such requirements often consist of making a game out of software usage where users are rewarded/penalized depending on the degree of their participation. The game may be competitive or non-competitive, depending on the anticipated personality traits of intended users. Making a game out of usage is often referred to as Gamification, and gamification has attracted huge attention in the literature for the past few years because it offers a novel approach to software technology usage. This paper proposes a generic framework for designing gamified solutions for acceptance requirements. The framework consists of a generic acceptance goal model that characterizes the problem space by capturing possible refinements for acceptance requirements, and a generic gamification model that captures possible gamified operationalizations of acceptance requirements. These models have been extracted from the literature and they are highly dependent on context (cognitive and social) elements of the intended user community. The proposed framework is illustrated with the Meeting Scheduler exemplar.

This article examines two different instances of policy defense as a means to show how a socio-cognitive approach to contexts can help develop a dialectical account of the relationship between societal processes and our communicative practices. Based on such analysis, I argue that comparative analyses within a socio-cognitive theory of context can offer new insights into how, first of all, mental models control the process of discourse production and interpretation in important ways, and second, how they are intrinsically related to ideologically based understandings of particular groups and/or situations. Such an approach allows us to account for and explain the potential effectiveness of the discursive moves that emerge from this co-constitutive relationship between contexts and communicative practices.