The rapid expansion of large datasets, encompassing text, images, audio, and video, presents substantial challenges for data labeling, a crucial step in machine learning and data science workflows. Large Language Models (LLMs) provide a promising approach for automating and improving the accuracy of data labeling across various modalities. However, their application in this area introduces specific challenges, such as managing diverse data types, maintaining high-quality annotations, handling computational complexity, and, critically, addressing and mitigating biases associated with automated methods. This vision paper examines the potential of LLMs to enable fair data labeling and validation in crowdsourcing environments, discussing the current landscape, and existing challenges, providing potential future research directions that can help ensure their successful integration.

The digitization of all processes and the expansion of IoT devices have fostered the emergence of a new form of crime: cybercrime. This term covers a range of malicious acts, the majority of which are now carried out using social engineering strategies-a phenomenon that combines the exploitation of both «human» vulnerabilities and digital tools. The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, making them the «weak link» in cybersecurity. These attacks have increased in both intensity and frequency, causing significant emotional and financial damage to public institutions, businesses of all sizes, and individuals. This research provides an overview of social engineering attacks, existing detection techniques, and the limited effectiveness of current countermeasures, whether due to technological or human factors. Even a robust security system can be easily bypassed by a simple social engineering attack. As current deployment policies prove insufficient, it is necessary to focus on upstream steps: learning how to anticipate attacks, identifying weak signals and outliers, detecting threats early, and reacting quickly to cybercrime. These are priority issues that require a prevention-focused and cooperative approach.

Understanding software's user behavior is key to personalizing and enriching the user experience and improving the quality of the software. In this paper, we consider the use of user navigation graphs issued from user activity traces. The aim of our study is to do node classification over the user graph navigation in order to understand better the composition of the software and to offer a better experience to the users. Traditional baseline methods has shown good performance in the node classification task, but can't be applied for tasks as link prediction. Graph Neural Network on the contrary can satisfy both node classification and link prediction. However, GNN produce significant results when the features on the nodes are numerous enough. This is not always the case in real-world problems, because too many features implies too much data, storage issues, affect the performances of apps, etc. Indeed, due to the origin of the data and their uncontrolled generation, the resulting graphs contain few or no features (AKA non-attributed graphs). In addition, in industrial fields, some external requirements particularly legal may limit the collection and the use of data. In this article, we show that graphs issued from real-world data also have such limitations, and we propose the generation of artificial features on the nodes as a solution to this problem.

International audience

Intelligent transport systems (ITS) play a pivotal role in enhancing safety, efficiency, and sustainability in modern transportation. Deep learning, a subfield of machine learning, has emerged as a powerful tool for tackling complex problems in intelligent transport applications. In this paper, we first provide a comprehensive overview of the advancements and future prospects of deep learning in intelligent transport systems. It explores various deep learning techniques, their applications, challenges, and potential solutions, contributing to the development of efficient and intelligent transportation networks. We created a hybrid prediction model based on LSTM with a Random forest algorithm to inform drivers' real-time road situations. To evaluate our proposed model, we developed a web application in which the user should subscribe to access the different services and know the real-time situation of his current used road. In the final, we showed the performance of our model by calculating the different metrics of evaluation, which are accuracy and F1_Score.

An important increase in the number of smart things, connected objects, smartphones, and sensors are founded every day, creating a huge amount of data called Big Data. Because of the dynamism of things and the limited capability of their resources and services to process and store this huge data, Cloud computing became an efficient, accessible, and reasonable solution. IoT in combination with Cloud functionalities provides a new phenomenon called the Cloud of Things (CoT) where many new possibilities are enabled. With the heterogeneity of different objects, the dynamism of their context, their distribution and the infinite cloud services, a middleware is a key solution. The main aim of this paper is to study middleware for self-adaptation of the Cloud of things environment into the context of objects. Toward this end, we first present some related features. Next, we compare some middlewares according to their characteristics. Then, we present some middlewares for static and dynamic adaptation and finally, we conclude this paper with a list of current challenges and issues in the design of a new middleware able to adapt dynamically and in execution time any cloud service into the context of the current object.

Nowadays, special attention is directed to trust issues in the Decentralized Online Social Network (DOSN). In a distributed system for social networking, interactions and collaborations can be unreliable because some users resort to malicious behaviors in order to increase their trust values in the network to be chosen later by others, and launch trust-related attacks. In this unreliable situation, users will not be able to estimate the trustworthiness of the received social services’ list of recommendations. Hence, a trust management model becomes a necessity in order to overcome its trust-related attacks and to recommend trustworthy social services. In this respect, we propose a new trust management model that helps prevent trust-related attacks in order to ensure a reliable environment. Towards this end, our suggested model implements a new technology, called blockchain. Based on the studied trust-related attacks, we intend to add logical security to blockchain since this technology takes into account only the physical security. Evaluation values show the effectiveness of our model.

Abstract Herein, the problem of vehicle re‐identification using distance comparison of images in CNN latent spaces is addressed. First, the impact of the distance metrics, comparing performances obtained with different metrics is studied: the minimal Euclidean distance ( MED ), the minimal cosine distance ( MCD ) and the residue of the sparse coding reconstruction ( RSCR ). These metrics are applied using features extracted from five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201. We use the specific vehicle re‐identification dataset VeRi to fine‐tune these CNNs and evaluate results. Overall, independently of the CNN used, MCD outperforms MED , commonly used in the literature. These results are confirmed on other vehicle retrieval datasets. Second, the state‐of‐the‐art image‐to‐track process (I2TP) is extended to a track‐to‐track process (T2TP). The three distance metrics are extended to measure distance between tracks, enabling T2TP. T2TP and I2TP are compared using the same CNN models. Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining DenseNet201 and MCD ‐based metrics exhibits the best performances, outperforming the state‐of‐the‐art I2TP‐based models. Finally, experiments highlight two main results: i) the impact of metric choice in vehicle re‐identification, and ii) T2TP improves the performances compared with I2TP, especially when coupled with MCD ‐based metrics.

This paper presents our work on forensic video analysis that aimed to assist videosurveillance operators by reducing the volume of video to analyze during the search for post-evidence in videos. This work is conducted in collaboration with the French National Police and is based on requirements defined in a project related to videos analysis in the context of investigations. Due to the constant increasing volume of video generated by CCTV cameras, one of the investigators' goals is to reduce video analysis time. For this purpose, we propose a negative filtering approach based on quality and usability/utility metadata, enabling to eliminate video sequences that do not satisfy requirements for their analysis through automatic processing. Our approach involves a data model which is able to integrate different levels of video metadata, and an associated query mechanism. Experiments performed using the developed framework demonstrate the utility of our approach in a real-world case. Results show that our approach helps CCTV operators to significantly reduce video analysis times.

The miniaturization of computers, coupled with a constant increase in computing power, led to the emergence of new sources of context information. We are facing a new paradigm, the Internet of Things (IoT). Today, this latter improves the quality of life in multiple areas. However, the heterogeneity of objects used in such environments makes their interoperability difficult. In addition, the observations produced by context providers (connected objects) are generated with different vocabularies and data formats. This heterogeneity of technologies in the IoT world makes it necessary to adopt generic solutions. Therefore, it is important to transform the raw data from these context producers into knowledge and information based on ontologies. The use of ontologies solves the challenges of heterogeneity and interoperability of IoT systems. In this paper, we propose a semantic IoT model that aims to overcome the semantic interoperability challenges introduced by the variety of objects potentially used in IoT systems. Furthermore, we enhanced this ontology with quality of context meta-data. These meta-data helps in dealing with imperfection and inconsistency of the collected IoT data.

Visual non-verbal behavior analysis (VNBA) methods mainly depend on extracting an important and essential social cue, called eye contact, for performing a wide range of analysis such as dominant person detection. Besides the major need for an automated eye-contact detection method, existing state-of-the-art methods require intrusive devices for detecting any contacts at the eye-level. Also, such methods are completely dependent on supervised learning approaches to produce eye-contact classification models, raising the need for ground truth datasets. To overcome the limitations of existing techniques, we propose a novel geometrical method to detect eye contact in natural multi-person interactions without the need of any intrusive eye-tracking device. We have experimented our method on 10 social videos, each 20 minutes long. Experiments demonstrate highly competitive efficiency with regards to classification performance, compared to the classical existing supervised eye contact detection methods.

With the constant evolution of CCTV cameras deployed in major cities to ensure the citizens' security, CCTV operators have to watch a huge amount of video when they are searching for scenes, objects, or target persons. Watching or processing some video sequences can be useless for several reasons: content is unsuitable for operators' needs, unusable shooting conditions, etc. Filtering useless content can be an efficient way for operators to save time. In this paper we propose an approach for CCTV contents filtering based on contextual information in order to provide CCTV operators with video sequences of interest. The proposed approach takes into account many sources of contextual information such as: open data, social media, mobility, geolocation, and crowdsourcing. We provide an analysis of contextual information relevant for this approach. Since interoperability is one of the main problems of context-based approaches, we propose a generic data model of contextual information used in our approach in order to tackle this issue. Based on this data, we propose a framework architecture for relevant video segments recommendation.

System specifications are generally organized according to several documents hierarchies levels linked in order to represent the traceability information. Requirements engineering experts verify manually the links between each specification which allows to generate a traceability matrix. The purpose of this paper is to automatize the generation of the traceability matrix since it is a time consuming and costly task. We propose an artificial intelligence based approach to deal with this problem through a clustering approach. This latter is an unsupervised algorithm that doesn't need any prior knowledge on the language neither the domain of the specifications. Our approach generates duplicates and clusters containing linked requirements. We experiment our approach in an aeronautic domain and a space domain. We obtain better results for high level specifications especially with a pre-processing.

In surveillance applications, humans and vehicles are the most important common elements studied. In consequence, detecting and matching a person or a car that appears on several videos is a key problem. Many algorithms have been introduced and nowadays, a major relative problem is to evaluate precisely and to compare these algorithms, in reference to a common ground-truth. In this paper, our goal is to introduce a new dataset for evaluating multi-view based methods. This dataset aims at paving the way for multidisciplinary approaches and applications such as 4D-scene reconstruction, object identification/tracking, audio event detection and multi-source meta-data modeling and querying. Consequently, we provide two sets of 25 synchronized videos with audio tracks, all depicting the same scene from multiple viewpoints, each set of videos following a detailed scenario consisting in comings and goings of people and cars. Every video was annotated by regularly drawing bounding boxes on every moving object with a flag indicating whether the object is fully visible or occluded, specifying its category (human or vehicle), providing visual details (for example clothes types or colors), and timestamps of its apparitions and disappearances. Audio events are also annotated by a category and timestamps.

The use of mobile devices and the development of geo-positioning technologies make applications that use location-based services very attractive and useful. These applications are composed of sensors that generate various and heterogeneous spatio-temporal data. Exploiting this spatio-temporal data to support video surveillance systems remains a relevant purpose for video content filtering. Since the data processed in such a context are heterogeneous (indoor and outdoor environment, various position types and reference systems, various data format), interoperability and management of these data remains a problem to be solved.

The following topics are dealt with: formal specification; social networking (online); Internet of Things; data analysis; business data processing; human factors; Internet; data mining; learning (artificial intelligence); and decision making.

Requirements are usually "hand-written" and suffers from several problems like redundancy and inconsistency. The problems of redundancy and inconsistency between requirements or sets of requirements impact negatively the success of final products. Manually processing these issues requires too much time and it is very costly. The main contribution of this paper is the use of k-means algorithm for a redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a filtering approach to eliminate "noisy" requirements and a preprocessing step based on the Natural Language Processing (NLP) technique to see the impact of this latter on the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking to detect technical business terms associated to the requirements documents that we analyze. We experiment this approach on real industrial datasets. The results show the efficiency of the k-means clustering algorithm, especially with the filtering and preprocessing steps. Our approach is using the software SEMIOS and will be integrated as a new functionality.

Online social networks (OSNs) have become an important source of information for a tremendous range of applications and researches such as search engines, and summarization systems. However, the high usability and accessibility of OSNs have exposed many information quality (IQ) problems which consequently decrease the performance of the OSNs dependent applications. Social spammers are a particular kind of ill-intentioned users who degrade the quality of OSNs information through misusing all possible services provided by OSNs. Social spammers spread many intensive posts/tweets to lure legitimate users to malicious or commercial sites containing malware downloads, phishing, and drug sales. Given the fact that Twitter is not immune towards the social spam problem, different researchers have designed various detection methods which inspect individual tweets or accounts for the existence of spam contents. However, although of the high detection rates of the account-based spam detection methods, these methods are not suitable for filtering tweets in the real-time detection because of the need for information from Twitter’s servers. At tweet spam detection level, many light features have been proposed for real-time filtering; however, the existing classification models separately classify a tweet without considering the state of previous handled tweets associated with a topic. Also, these models periodically require retraining using a ground-truth data to make them up-to-date. Hence, in this paper, we formalize a Hidden Markov Model (HMM) as a time-dependent model for real-time topical spam tweets filtering. More precisely, our method only leverages the available and accessible meta-data in the tweet object to detect spam tweets exiting in a stream of tweets related to a topic (e.g., #Trump), with considering the state of previously handled tweets associated to the same topic. Compared to the classical time-independent classification methods such as Random Forest, the experimental evaluation demonstrates the efficiency of increasing the quality of topics in terms of precision, recall, and F-measure performance metrics.

Research has shown the effectiveness of inferring user interests from social neighbors, also called "social profiling". However, the evolution in the social profile is not widely taken into consideration. To overcome this drawback, we propose a time-aware social profiling method that considers the temporal factors of the information and the relationships between the user and his/her social neighbors. This method aims at weighting user interests in the social profile, by applying a time decay function. The temporal score of a given interest is computed by combining the temporal score of information used to extract the interests with the temporal score of individuals who share the information in the network. The experiments conducted on a co-authorship network, DBLP showed that the time-aware social profiling process applying our proposed time-aware method outperforms the existing time-agnostic social profiling process. The combinatorial analysis and the parametric study led us to observe that in the context of co-authorship network, the individual temporal score has more influence than the information temporal score. As this kind of network does not exhibit a rapid evolution of information and relationships, to obtain a relevant social profile, the information should be damped slowly.

The integration of social networks with mobile communication has led to the rise of a new paradigm, the mobile social network (MSN). Recently, MSN has emerged as a new hot spot of research attracting much interest from both academia and industrial sectors. For instance, MSN opens new horizon for information diffusion-based applications such as viral marketing. Thus, it is a fundamental issue to select an efficient subset of seed-nodes (i.e. initial sources) in a MSN such that targeting them initially will maximize the information diffusion to interested nodes. This paper studies the problem of identifying the best seeds through whom the information can be diffused in the network in order to maximize the content utility (i.e. a quantitative metric that determines how satisfied are the users). A multi- layer model that combines the social relationships and the mobile network in order to design an efficient information diffusion is proposed. Based on this multi-layer model, different seed selection approaches are proposed for information diffusion environment (e.g. mobile advertising) where users have heterogeneous interests for the different information generated in the network. Simulation results show the effectiveness of multi-layer based seed selection approaches comparing to a classical approach.

Incorporating user interests evolution over time is a crucial problem in user profiling. We particularly focus on social profiling process that uses information shared on user social network to extract his/her interests. In this work, we apply our existing time-aware social profiling method on Twitter. The aim of this study is to measure the effectiveness of our approach on this kind of social network platform, which has different characteristics from those of other social networking sites. Although the improvement compared to the time-agnostic baseline method is still low, the experiments using a parametric study showed us the benefit of applying a time-aware social profiling process on Twitter. We also found that our method performs well on sparse networks and that the information dynamic influences more the quality of our proposed time-aware method than the relationships dynamic while building the social profile on Twitter. This observation will lead us to a more complex study to find out meaningful factors to incorporate user interests evolution on social profiling process in such a network.

This paper presents our work about assisting video-surveillance agents in the search for particular video scenes of interest in transit network. This work has been developed based on requirements defined within different projects with the French National Police in a forensic goal. The video-surveillance agent inputs a query in the form of a hybrid trajectory (date, time, locations expressed with regards to different reference systems) and potentially some visual descriptions of the scene. The query processing starts with the interpretation of the hybrid trajectory and continues with a selection of a set of cameras likely to have filmed the spatial trajectory. The main contributions of this paper are: (1) a definition of the hybrid trajectory query concept, trajectory that is constituted of geometrical and symbolic segments represented with regards to different reference systems (e.g., Geodesic system, road network), (2) a spatio-temporal filtering framework based on a spatio-temporal modeling of the transit network and associated cameras.

Improving the egocentric network-based user's profile building process by taking into account the dynamic characteristics of social networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's egocentric network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic egocentric network-based user profiling process.

This paper represents a study along the cutting edge of the current analysis of online social network in relation with the contents communicated among users. Twitter data is carefully selected around a fixed hash-tag in order to study the specified content in relation with other contents that users bring to connection. A separate network of hash-tags related (in tweets) is constructed for different days; the networks are analyzed within advanced Gephi package, providing several measures —degree, betweenness centrality, communities, as well as the longest path, by which the evolution of communication around specified concepts is quantified. Our study is absolutely in the current trend of analysis of online social networks that, going beyond mere topology, reveals relevant linguistic and social categories and their dynamics.

In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become “outdated” through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same “important” resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

The paper presents a solution to the problem of capitalizing in different contexts and by different stakeholders the time-stamped new documents produced by social Web sites (including news, blog entries, and uploaded documents). The solution core includes an ontology-based method to express the interest topics and to automatically classify them. For such textual content obtained in real-time, we propose an unsupervised text classification system based on general YAGO ontology, graph algorithms and a custom scoring method. The system shows good performance using only ontology information and the ontology structure itself. We compare our system against a SVM-based (Support Vector Machine) classic text classification approach. For determining the relevance of a specific document for a specific topic, our approach develops and compares the ontology sub graphs corresponding to the query and to the document. It leads to a high flexibility in terms of capitalizing the already classified documents when refining and changing the interest topic: a graph-based matching of the already obtained ontology-based document representation against the new query representation is enough to assess the document relevance.

Nowadays, social networks are more and more widely used as a solution for enriching users' profiles in systems such as recommender systems or personalized systems. For an unknown user's interest, the user's social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user's social network, and strongly depend on each author's objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the user's social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted user's interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g. centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individuals based techniques, and the influence of structural measures (related to communities) on the quality of derived profiles. The results also raise the problem of users' privacy in platforms such as online social networks. To enable users to better protect their privacy, these platforms should provide their users with a way to also make their friendlist private.

Existing works about user profile modeling always model the user as an independent entity. However, in social sciences, many works show the user's behavior as strongly influenced by his social ties and/or social interactions. These results were diffi

The present paper proposes a new approach for developing a generic design process, taking the aircraft design as use case. Our solution extends the multi-agent approach into a competence-based human resources and agent allocation. The solution consists into an iterative design technique that considers the reality of the design process as an evolutional process, where the new constraints are determined and take shape at each step. Alongside with the high-level aircraft requirements, each step demands the compliance and the setting up of some specific aircraft characteristics, which our solution identifies with the competences required for the involved human resources and software agents. The eventual re-definition of the current step specification involves a resources re-allocation as well.

In this paper, we are interested in the context of the French government's new efforts to put into practice the concept of the "personal health record (PHR)". We propose an ontology-based solution to improve the access to PHR and to extend it with the annotations that the patient can add and that will constitute the Annotated PHR. We consider that each user can manage the annotations access policies. We also develop an ontology-based solution for granting medical agents with useful, authorized and relevant information, which adopts XACML query rewriting mechanisms. The paper illustrates how the proposed framework could assist patients during their travels, when an unexpected health disorder takes place and the suitable specialist is not available.

The current paper expose a technique for developing a solution of personalized recommendations for e-learning systems adopting an ontology-based modeling of user profiles and document models. Because the solution is situated at the interference of three domains (e-learning, semantic Web and adaptive hypermedia systems), the methodological aspects considered in developing such a solution are discussed with respect to the existing techniques in these domains. As well, some evaluation criteria of such solution are discussed, while considering some existing systems that have similar characteristics to the proposed solution.

In this article we are presenting a solution for the problem of combining various indexation algorithms in order to acquire a semantic multimedia indexation and to provide responses to the user complex queries. The challenge of this problem concerns the big heterogeneity of the multimedia indexation algorithms and the weak semantic aspect they address. Our solution considers a generic interface for the indexation algorithms, an implementation as Web services, as well as a semantic description in terms of WSMO (Web Service Modeling Ontology) of their functionality and orchestration. Original contribution of the article concerns the idea of organizing the various multimedia metadata types into a generic structure,used to express the user queries, the algorithms' generic interface, as well as the algorithms' WSMO metadata. This approach facilitates the definition of algorithm combination rules, and enables the reduction of the multimedia retrieval task to a metadata matching process.

The diversity of terminals used by the user to access resources (Personal Digital Assistant, mobile phone, etc.) using several types of networks (wireless, local, etc.) generates a growing need to adapt services dynamically to the user's context. In this article, we present our architecture that aims at adapting content and presentation of services to the user's context. We realize the content adaptation based on a new data modeling methodology. This methodology aims to take into account the different structures associated with the same data allowing a data representation in several viewpoints. The presentation adaptation is based on an automatic generation process of the interfaces service code. The context in our architecture is presented by a generic model for the user and the service. The adaptation process is detailed based on an e-learning scenario.

In the age of mobility, many times a certain teacher, a student or a document face difficulties to be integrated in different e-learning systems. Focusing on the computer science field, we present a service-oriented solution for annotating and retrieving the documents and persons involved in this domain, enhancing their mobility inside pervasive e-learning systems. The solution is based on the ACM classification system, which is used in order to develop user competence profiles, to annotate materials, and to retrieve them. The ontology-based retrieval mechanism will investigate not only the matches with the concepts involved in a query, but also with their related concepts inside the ACM classification system; the ranking algorithm will consider the documents content as well as the user competence.

At the present time, the remote sensing community will have to deal with new data type; very high spatial resolution and IKONOS and Quickbird data give an excellent reference of them. For some topics that are directly implied like environment or urban areas analysis, these new data will be very important. Indeed, the arrival of these images enables a new capability and the study of a range of non-observable objects until now. Using high resolution imagery should make it possible to detect man-made features such as buildings, rivers or roads in an easier way than conventional data. This research presents and proposes an automatic system of cartographic elements extraction from space images, using very high spatial resolution images. This system can be adapted to other types of remote sensing images. This research work is focussed on the extraction of four types of cartographic elements: water areas, urban areas, wooded areas and linear features such as roads or railways. Each type of cartographic element is extracted detecting its own characteristics, using image analysis, applying a segmentation process and knowledge extraction.

The increasing number of requirements for Web information systems and the increasing need for making their components interoperable ask for a new vision for the architecture of such Systems. We focus on two of these requirements: usability and interoperability. We present, in this paper, our contribution that aims at proposing a new architecture of Web information systems, supporting adaptation to the userpsilas context and providing the user with a list of Web services adapted to his context. This architecture is based on an extension of AHA! architecture through an adaptation layer containing various components dedicated to the context adaptation. So, our aim is to build an adaptive Web information system based on Web Services. Finally, we present an algorithm to build a user contextual profile ldquoProfile-Context-User (UCP)rdquo containing only the userpsilas preferences that can be satisfied with the context.

Nowadays, with the great diffusion of mobile technology, and ubiquitous systems, the context has become the ear and the eye of information systems. These systems are more and more based on the usage of Web services. The classical architecture of these services that allows an interoperable interaction between service users and providers does not take in account context adaptation. In this article, we aim to integrate context adaptation within the classical architecture of Web services, adding to it dedicated components that would return to nomadic users a list of Web services that are adapted not only to his profile but also to his context.

This article provides a general discussion about how flexible querying can be applied to semistructured data (SSD). We adapt flexible querying ideas, already used for classically structured databases, to XQuery-like querying of SSD for managing users' priority and preferences, but also for tackling with the variability of SSD underlying structures. Indeed flexible querying seems to be still more useful for SSD than for classical databases, because of the potential structural heterogeneity of the former. Fuzzy sets are useful for expressing flexible requirements on attribute values and for estimating the degree of similarity of tags, or attribute labels, with elements present in the request. Priorities are introduced in the request for specifying the relative importance of elementary requirements in terms of their semantic contents, but also preferences about the location of information in the structure. The evaluation of the queries uses a qualitative scale with a finite number of levels, and retrieved pieces of SSD are rank-ordered using a lexicographic vector procedure. Illustrative examples are provided. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 723–737, 2007.

Queries to a database can be made more powerful by allowing flexibility in the specification of what has to be retrieved, and by referring to cases either for expressing the request, or for computing the answer. In this paper, we present an implemented information system (applied to a database describing houses to let), based on an approach developed in the fuzzy set and possibility theory setting. This provides a unified framework for expressing users' preferences about what they are looking for, for weighting the importance of requirements, for referring to examples that they like and/or counter-examples that they dislike, and for making case-based predictions. Thus information querying goes beyond the retrieving of items from a database, and involves associated tools which help the user to figure out the actual contents of the database.

Queries to a database can be made more powerful and user friendly by referring to cases, either for expressing the request, or for computing the answer. This requires some similarity-based reasoning facilities. In this paper, we present an implemented information system (applied to a database describing house renting), based on an approach developed in the fuzzy set and possibility theory setting. This provides a unified framework for: expressing user's preferences about what he is looking for; weighting the importance of requirements; expressing similarity relations; referring to examples that he/she likes and/or counter-examples that he/she dislikes; and for making case-based predictions.

Fuzzy logic is known for providing a convenient tool for interfacing linguistic categories with numerical data and for expressing user's preference in a gradual and qualitative way. Fuzzy set methods have been already applied to the representation of flexible queries and to the modeling of uncertain pieces of information in databases systems, as well as in information retrieval. This methodology seems to be even more promising in multimedia databases which have a complex structure and from which documents have to be retrieved and selected not only from their contents, but also from "the idea" the user has of their appearance, through queries specified in terms of user's criteria. This paper provides a preliminary investigation of the potential applications of fuzzy logic in multimedia databases. The problem of comparing semistructured documents is first discussed. Querying issues are then more particularly emphasized. We distinguish two types of request, namely, those which can be handled within some extended version of an SQL-like language and those for which one has to elicit user's preference through examples.