This paper investigates the relationship between the perception of usefulness and perceived game elements, and their subsequent influence on learning performance. Un experiment was conducted with fifty-six undergraduate students who have used a gamified learning environment to learn for one semester. After that students’ data regarding perception and learning performance were collected and analyzed using PLS software. The results showed that students' perceptions of usefulness in a gamified environment are linked to specific game elements. Notably, the study reveals that students who perceive the gamified environment as highly useful also tend to hold positive perceptions of certain game elements, such as the progress bar and chat features. Moreover, results showed that students' perception of game elements can significantly impact their learning performance. These insights not only advance our understanding of human-computer interaction but also offer valuable guidance for both researchers and practitioners in designing effective gamified learning environments. By recognizing the interplay between perceived usefulness, game elements, and learning outcomes, educators and developers can tailor interventions and experiences that optimize engagement and learning outcomes.

Blockchain is becoming a core technology in all application domains as it brings several advantages, such as immutability, transparency, high availability, strong data consistency, and interoperability. Despite these advantages, blockchain systems still have several concerns that might significantly reduce their effectiveness, including motivational, human concerns and miners’ involvement and participation. Gamification, on the other hand, was proposed as a solution for these concerns, among others. However, little is known in the literature on the use of gamification for blockchain systems, calling for more investigation in this regard. Therefore, this study conducts a systematic literature review to investigate why and how gamification is used in blockchain systems. Specifically, 41 studies were included and analyzed in this systematic review. The obtained results highlighted that gamification is integrated into blockchain systems to achieve different purposes, including motivating users/miners and increasing data validation and trust. Additionally, the findings revealed that Ethereum is the most used blockchain platform, where points and challenges are the most used game elements in blockchain systems. Furthermore, when using gamification as an incentive mechanic in blockchain, there is no difference in the effect of the consensus algorithm on miners’ motivation. The findings of this study provide insights into the ways of integrating gamification to enhance blockchain systems, as well as reveal future research directions to consider before deploying the next generation of gamified blockchain systems.

Constrained machine learning (ML) models consists of incorporating a set of constraints into the ML model. The latter is generally used to incorporate domain knowledge, enhance performance, and define fair and robust ML models. Several papers reported that defining a full set of constraints is challenging. To address this issue, approaches for learning constraints were proposed. These approaches established general rules governing the data and dismissed any sample-related information. However, in user profiling tasks it is important to understand the position of each individual. In this paper, we define behavioral constraints as the set of numerical values that reflect the characteristics of each individual in the dataset. We propose an unsupervised approach for learning these behavioral constraints (ULBC). Applied to two datasets, the approach effectively provided behavioral constraints that unravel the similarities and dissimilarities between individuals.

The world has been experiencing several crises recently, particularly on the social front. Therefore, new technologies have been adapted to provide the most diverse possible solutions, including crowdfunding platforms that concentrate on social projects. They have recently piqued the interest of investors and donors, particularly those based on blockchain technology, thanks to their ability to achieve reliability. Social crowdfunding platforms have developed new strategies for luring donations and investments. However, there is still a lack of development of these ideas and exploiting the benefits and services provided by blockchain technology properly. This paper presents blockchain technology in a socially oriented crowdfunding platform reward-based that aims to provide a transparent, secure, auditable, and efficient system. BELONG is the first leading platform that merged the ideas of crowdfunding, donations, and charitable investments with a type of blockchain-based token called Non-fungible tokens (NFTs). The goal is to create safe investment channels, and that is because of the dearth of studies on the idea of integrating NFTs into humanitarian, charitable, or social activities. It relies on two strategies for seeking funds; the bedrock on which all two are built is the NFT. This study intends to reach out to all societal stakeholders interested in this field. As a result, each strategy targets a specific category, including donors, investors, and individuals, to make funding opportunities available for everyone. A dedicated prototype, using Ethereum and Vuejs, is implemented to demonstrate the platform's feasibility.

Thyroid disease is one of the most common endocrine disorders worldwide. However, thyroid conditions can be challenging to diagnose because symptoms are very similar to those of other diseases. A proper diagnosis depends on clinical examination and many blood tests involving a large amount of complex data that is difficult to interpret. Early thyroid detection is crucial since it significantly reduces complications and minimizes death risk. The main objective of this study is to create an accurate framework for improving the diagnostic accuracy of thyroid diseases. For this purpose, we propose a three-stage approach based on dimensionality reduction using feature selection, data sampling to handle the data-imbalance problem, and stacked ensemble learning instead of a single machine learning algorithm to give the final prediction. This research shows that the proposed approach can diagnose thyroid disease more accurately than existing techniques, achieving 99.49% of precision and 99.46% in terms of F1-score.

Detecting communities of interest is a complex problem that has been addressed from different perspectives. In this work, we propose a user-centered approach incorporating social user profiles in community detection for online Social Networks. In our approach, we first compute explicit knowledge acquisition. By exploring the egocentric networks of users, we can infer implicit similarities of interest. The similarity is estimated with reference to homophily and social influence. The latter is leveraged to enhance Sentiment Analysis within communities. Finally, we conduct experiments on datasets extracted from real-world Social Networks.

Recently, terrorist risks are continuing to increase in the form of terrorist acts due to various and simultaneous factors. This subject is of great scientific interest and importance because our society is vulnerable and we have a low level of protection against terrorism. This paper aims at developing a decision support model, called jCatt (jCOLIBRI against terrorist attacks). The preventive model is based on acquiring and reusing past attacks historically solved to assist a decision maker. It helps to understand each terrorist attack situation and to propose possible solutions in the form of preventive and / or corrective measures. It is composed of two main parts: knowledge models described by an ontology, and a reasoning process based on Case-Based Reasoning (CBR). In this paper, we present the development environment used, the architecture and in particular the various components of our model as well as the phases of the reasoning cycle.

Retrieving information based on the users' preferences and profiles represent a challenging issue to overcome. Moreover, in the public transport field, this task becomes increasingly complex due to the heterogeneous data fetched from various sources. Though, ontologies have emerged in retrieving information field to reduce this complexity. This paper describes a visual framework aiming to search and extract the suitable itinerary that fits better with the user's information needs and based on the ontologies. The framework relies on three main components: a simple search component, an advanced search based on users' preferences and profile component and a Question-Answering search component represented in a natural language. Our framework has been implemented and evaluated.

Mobile applications are more and more present everywhere (at home, at work, in public places, etc.). Many academic and industrial studies are conducted about design methods and tools for mobile user interface generation. However, the evaluation of such interfaces is object of relatively few propositions and studies in the literature. The existing evaluation methods are widely based on a questionnaire, survey, eye tracking, etc. to assess mobile interface. These methods are time-consuming, error-prone task. In fact, one of the widely used methods to assess quality of MUI is using detection rules. But, the manual definition of these methods is still a difficult task. In this context, we define a method that generates evaluation rules for assessing the quality of mobile interfaces. To this end, we consider the generation of evaluation rules as a mono-objective technique problem where the goal is to find the best rules maximizing the quality of mobile interfaces. We evaluate our approach on four mobile applications. This study was designed around the android mobile devices. The obtained results confirm the efficiency of our technique with an average of more than 70% of precision and recall.

Providing the user with tailored and personalized services has a paramount importance when developing an interactive system. However, anticipating his interest remains another challenge to be overcame. This is related on one hand, to the lack of specific computational modeling for knowledge discovery, and on other hand, to fitting correctly with what the user requires. In this paper, a new method based on user profile ontology built through ontology modularization is proposed. This latter, is composed of explicit and implicit user's preferences. The discovering of such preferences is achieved by a new method of content based ontology that aims to take into consideration both the hierarchical and the non-hierarchical (semantic) relations jointly with related properties. However, the extraction phase is gained by ontological rules SWRL which are extended through the Spreading activation technique in order to infer as much as possible new knowledge. The proposal is applied in the public transport system to retrieve the most suitable path. The assessment phase shows that this method can provide meaningful personalized results.

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the use rs’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.

Ontology is a new paradigm introduced with the semantic web to describe in an explicit and formal way the various aspects of knowledge of a specific field. For this purpose, a single ontology may not be comprehensive to represent all due to the lack of a common and shared ontology between communities. Ontologies need to establish a number of interlinks to ensure communication between them, which is not always obvious because of their terminological, syntactic and semantic heterogeneity. The proposed matching system aims to discover in an automatic way, the correspondence links between two intrinsically heterogeneous ontologies, through different techniques of calculations of similarity between their entities .It allows to reveal on one hand the issue of searching for the most relevant, coherent and meaningful alignments and on the other hand, to propose a new strategy that ensures flexibility and scalability of the system by the combination of the matchers.

A novel ontology-Based Personalized Retrieval model using the Case Base Reasoning (CBR) tool is designed and presented in this paper. The proposed approach is aimed at achieving a scalable and user friendly data retrieval system with high retrieval performance where search results are ranked based on user preferences. The proposed retrieval framework integrates the advantages of two methods, a content-based method (ontology) to represent data and a case-based method (CBR) to personalize the search process and to provide users with alternative documents recommendations. To analyze the performance of the proposed approach, computer experiments are carried out using recall-precision curve and average precision (AP) metric. The performance of our approach is then compared to a framework that uses the classic vector space model. Results clearly indicate the strength of the proposed approach as well as its ability to accurately retrieve pertinent information. The proposed approach is particularly promising in applicable related to city logistics, especially in the field of itinerary research for urban freight transport.

Personalization plays an important role in information systems. It is an effective solution for reducing complexity when searching information. In this way, the user feels like the system was developed for him/her. In this context, personalization can be seen as an optimization problem. To this end, we propose a multi-criteria decision making approach to personalize systems. The proposed approach has been validated by applying it to personalize a system in intelligent transport field.

Model Driven Architecture (MDA) has gained attention from human-computer interface community because of its capability of code generation from abstract models and transformations. MDA approaches and tools in this context include, usually, the personalization of interface design elements (such as I/O fields, screen resolution, screen size, and so on) based on some information about the context of use. However, to really achieve the personalization, it is important not only to consider the container but also the content that means, which information should be provided in each situation. This paper presents a MDA approach in this direction. Our goal is to define during user interface design what information should be personalized for a specific domain considering the context information. To address this goal, a context model and a domain ontology are used as central elements for models design and transformations.

Choose the best way to move from one place to another can involve different information: offers of different transport modes, their combination in the same journey and other information about services (such as restaurants, libraries, etc) that can be available in the route and useful for the passenger. Different approaches have been proposed to support the passenger's planning considering some part of this information. In this paper we present a public transportation domain ontology that considers different concepts related to the best and more relevant planning for the passenger. This ontology is formalized with OWL in Protégè tool. Using real instances and inferences, we show the ontology application, its relevance and consistency.