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Integrating the Internet of Things (IoT) with Social Networks (SN) has given rise to a new paradigm called Social IoT, which allows users and objects to establish social relationships. Nonetheless, trust issues such as attacks have emerged. These attacks can influence service discovery results. A trust management mechanism has become a major challenge in the Social IoT to prevent these attacks and ensure qualified services. A few studies have addressed trust management issues, especially those that prevent trust attacks in Social IoT environments. However, most studies have been dedicated to detect offline attacks with or without specifying the type of attack performed. These works will not be able to prevent attacks by aborting transactions between users because their primary purpose is to detect an offline attack. In addition, they do not consider security properties. This research paper aims to provide a detailed survey on trust management mechanism to handle trust attacks in Social IoT. In this research paper, we compared the techniques and technologies whose common point is attack prevention and demonstrated that blockchain technology can play a key role in developing a trust management mechanism that can prevent trust attacks while maintaining security properties. Then, we proposed combining the Apache Spark Framework with blockchain technology to provide real-time attack prevention. This combination can assist in creating upgraded trust management mechanisms in Social IoT environments. These mechanisms aim to prevent attacks in real-time through considering the security properties. Lack of survey papers in the area of trust attack prevention in real-time stands for an important motivational factor for writing this paper. The current research paper highlights the potential of the blockchain technology and Apache Spark in terms of developing an upgraded trust management able to prevent trust attacks in real-time.This paper provides a comprehensive survey on trust management mechanisms and approaches to handle trust attacks in Social IoT. Lack of such papers increases the significance of this paper. It also offers potential future research directions in terms of real-time trust attack prevention. [ABSTRACT FROM AUTHOR]

With the advent of data management and analytics in many everyday applications, recent trends in various activities related to "computerised restaurant" are emerging, as a joint cooperation between computer science and cooking or food-oriented activities. Among these activities, observing a scene, for instance a diner, and interactions among the participants tend to be relevant. Analysing such social interactions relies on interpreting human-to-human interaction, from a spatial and visual point of view. Identifying behaviour depends on various modalities: speaking, sightseeing, moving, walking, etc. Various inputs can be considered as postural congruence (left space and mutual distance), choice of the seat, etc. Furthermore, voice and sound (silence) are relevant to this analysis too: voice level, silences, onomatopoeia, etc. For example, voice can bring semantics by analysing the detected keywords using voice transcription. Such observations are nowadays enabled by advanced applications that embody more and more intelligence and the corresponding interconnected, smart, embedded devices. For example, sensors and cameras allow non-intrusive recording. Our aim is to survey how an observatory can be designed and managed in order to give context to research investigations. Specifically, how analysing, mining, eliciting data as well as crowdsourcing, video, social networks etc can help us to observe new insights on our eating habits and choices. As we need "in vivo" capture and observation, the observatory must be able to offer various settings to avoid any predefined scenario. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM. [ABSTRACT FROM AUTHOR]

We propose a comprehensive analysis of existing concepts of AI coming from different disciplines: Psychology and engineering tackle the notion of intelligence, while ethics and law intend to regulate AI innovations. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems. Relevant concepts are integrated into a matrix intended to help defining more precisely when and how computing tools (programs or devices) may be qualified as AI while highlighting critical features to serve a specific technical, ethical and legal assessment of challenges in AI development. Some adaptations of existing notions of AI characteristics are proposed. The matrix is a risk-based conceptual model designed to allow an empirical, flexible and scalable qualification of AI technologies in the perspective of benefit-risk assessment practices, technological monitoring and regulatory compliance: it offers a structured reflection tool for stakeholders in AI development that are engaged in responsible research and innovation. [ABSTRACT FROM AUTHOR]

Purpose: Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user's interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users' profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach: This approach handles conflicts by detecting the reliability of neighbors' profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings: The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications: Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner's interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value: This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach. [ABSTRACT FROM AUTHOR]

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Virtual social networks have led to a new way of communication that is different from the oral one, where the restriction of time and space generates new linguistic practices. Twitter, a medium for political and social discussion, can be analyzed to understand new ways of communication and to explore sociosemiotics aspects that come with the use of the hashtags and their relationship with other elements. This paper presents a quantitative study of tweets, around a fixed hashtag, in relation with other contents that users bring to connection. By calculating the frequency of terms, a table of nodes and edges is created to visualize tweets like graphs. Our study applies social network analysis that, going beyond mere topology, reveals relevant sociosemantic communities providing insights for the comparison of social and political movements. [ABSTRACT FROM AUTHOR]

Virtual social networks have led to a new way of communication that is different from the oral one, where the restriction of time and space generates new linguistic practices. Twitter, a medium for political and social discussion, can be analyzed to understand new ways of communication and to explore sociosemiotics aspects that come with the use of the hashtags and their relationship with other elements. This paper presents a quantitative study of tweets, around a fixed hashtag, in relation with other contents that users bring to connection. By calculating the frequency of terms, a table of nodes and edges is created to visualize tweets like graphs. Our study applies social network analysis that, going beyond mere topology, reveals relevant sociosemantic communities providing insights for the comparison of social and political movements. [ABSTRACT FROM AUTHOR]

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 difficult to integrate in user profile modeling because of the problem of capturing social data about users in infor-mation systems. With the advent of the social web for example, this barrier can be broken on the Web, and we can reasonably think about enriching users' profiles from social data. In this paper we propose a technique to develop users' profiles with social data. We built profiles from textual users' activities data, and we show through visualization of temporal graphs the relevance of social ties on built profiles (with an experimentation carried out on 7, 081 Facebook profiles). These results motivated the potential implementation of new profiling techniques based on users' social networks, to enrich users' profiles and solve pending problems such as 'cold start problem' in personalized or recommender systems. [ABSTRACT FROM AUTHOR]

The authors propose an automatically constructed metadata resume to facilitate the retrieval of desired media information about content distributed across several servers. [ABSTRACT FROM AUTHOR]

Personalization of user interfaces for browsing content is a key concept to ensure content accessibility. In this direction, we introduce concepts that result in the generation of personalized multimodal user interfaces for browsing XML content. User requirements concerning the browsing of a specific content type can be specified by means of user-friendly description languages. According to these specifications, transformation rules are generated and applied to content to produce personalized user interfaces for browsing the content. With the emergence of the semantic Web and connected XML applications, such customized multimodal user interfaces can be helpful to many kinds of users, especially to individuals with various types of impairment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]