Sentiment analysis involves using computational methods to identify and classify opinions expressed in text, with the goal of determining whether the writer's stance towards a particular topic, product, or idea is positive, negative, or neutral. However, sentiment analysis in Arabic presents unique challenges due to the complexity of Arabic morphology and the variety of dialects, which make language classification even more difficult. To address these challenges, we conducted to investigation and overview the techniques used in the last five years for embedding and classification of Arabic sentiment analysis (ASA). We collected data from 100 publications, resulting in a representative dataset of 2,300 detailed records that included attributes related to the dataset, feature extraction, approach, parameters, and performance measures. Our study aimed to identify the most powerful approaches and best model settings by analyzing the collected data to identify the significant parameters influencing performance. The results showed that Deep Learning and Machine Learning were the most commonly used techniques, followed by lexicon and transformer-based techniques. However, Deep Learning models were found to be more accurate for sentiment classification than other Machine Learning models. Furthermore, multi-level embedding was found to be a significant step in improving model accuracy.

In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.

Knowledge graph is a knowledge representation technique that helps representing entities and relations in a machine understandable way.This promising trend suffers from the problem of incompleteness that was best solved by link prediction.Indeed, link prediction is the most successful method for understanding the structure of the large knowledge graphs.Knowledge graph embedding KGE is one of the best link prediction methods.Its effectiveness is mainly affected by the accuracy of learning representations of entities and relations.In this paper, we propose a new knowledge graph embedding model called KEMA ( Knowledge-graph Embedding using Modular Arithmetic).KEMA has the ability to represent simple and complex relations in an efficient way.Consequently, this allows our model to outperform the majority of the existing models.Mainly, KEMA depends on representing the relations in a knowledge graph by modular arithmetic operations applied between entities.Experimental results on multiple benchmark knowledge graphs verify the accurate representation, low complexity and scalability of KEMA.

Nowadays, representing entities and relations in a machine understandable way through Knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation patterns such as symmetry, anti-symmetry, inversion, and composition. However, there are few models in the literature that take into consideration the modeling of complex relation patterns like 1-[Formula: see text], [Formula: see text]-1 and [Formula: see text]-[Formula: see text], which are common in real-world applications. To overcome this challenge, this paper presents a new KGE model called KEMA[Formula: see text], i.e. KGE using Modular Arithmetic, that relies on the combination of projection and modular arithmetic. The main idea behind KEMA[Formula: see text] is to project the entities of a relation to represent the relations of a KG, before applying a modular arithmetic over it. Thus, KEMA[Formula: see text] will be able to infer all simple and complex relation patterns for any KGE applications. Through extensive experiments on several datasets, we demonstrated the relevance of KEMA[Formula: see text] in terms of effectively representing all model relations in a KG. Simulations on several tested datasets show that KEMA[Formula: see text] obtains the good scores for Mean Rank (MR) and Hits@1 tests. Moreover, KEMA[Formula: see text] obtains the good Hits@1 score compared to the existing models.

Civic engagement refers to any collective action towards the identification and solving of public issues. Current civic technologies are traditional Web-or mobile-based platforms that make difficult, or just impossible, the participation of citizens via different communication technologies. Moreover, connected objects sensing physical-world data can nourish participatory processes by providing physical evidence to citizens; however, leveraging these data is not direct and still a time-consuming process for civic technologies developers. This paper introduces the concept of social middleware for civic engagement. Social middleware allows citizens to engage in participatory processes - supported by civic technologies-via their favorite communication tools, and to interact not only with other citizens but also with relevant connected objects and software platforms. The mission of social middleware goes beyond the connection of all these heterogeneous entities. It aims at easing the implementation of distributed applications oriented toward civic engagement by featuring dedicated built-in services.

Online Social Network Services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the past few decades, an aggressive market rivalry has led to the emergence of multiple competing, “closed” OSNSs. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about what they can do with their personal data, the people they can interact with, and the information they get access to. As an alternative to the platform lock-in, “open” OSNSs promote the adoption of open, standardized APIs. However, users still massively adopt closed OSNSs to benefit from the services’ advanced functionalities and/or follow their “friends,” although the users’ virtual social sphere is ultimately limited by the OSNSs they join. Our work aims at overcoming such a limitation by enabling users to meet and interact beyond the boundary of their OSNSs, including reaching out to “friends” of distinct closed OSNSs. We specifically introduceUniversal Social Network Bus (USNB), which revisits the “service bus” paradigm that enables interoperability across computing systems to address the requirements of “social interoperability.” USNB featuressynthetic profilesandpersonaefor interaction across the boundaries of closed and open and profile- and non-profile-based OSNSs through areference social interaction service. We ran a 1-day workshop with a panel of users who experimented with the USNB prototype to assess the potential benefits of social interoperability for social network users. Results show the positive evaluation of users for USNB, especially as an enabler of applications for civic participation. This further opens up new perspectives for future work, among which includes enforcing security and privacy guarantees.

Despite the smooth weaving of the Internet-of-Things into people's daily lives, many challenges, such as diversity and multiplicity of things' development technologies and communication standards, and users' reluctance due to privacy invasion, are slowing down this weaving. This paper tackles the challenge of things' passive nature that has confined them into a data-supplier role. Empowering things with additional capabilities would make them proactive so, that, they can for instance, reach out to peers exposing collaborative attitude and (un)form dynamic communities when necessary. In this paper, this empowerment takes shape through thing agentification that relies on norms (specialized into business and social) to regulate the operations of things and commitments to ensure thing compliance with these norms. No-compliance would lead to sanctions over things, which should affect their credibility and reputation. A proof-of-concept and missing-child case study technically illustrate thing agentification.

During the execution of a Composite Web Service (CWS), different faults may occur and cause a Web Service (WS) to fail. To repair failures some strategies can be applied, such as WS retry or substitution, compensation of the performed execution, roll-back, replication, or take checkpoints to later restart the execution. Each strategy has advantages and disadvantages on different execution scenarios and can produce different impact on the CWS QoS, depending on the execution environment and execution state at the moment of the failure. In this paper we propose a model and show experimental results to dynamically decide which recovery strategy is the best choice in terms of the impact on the CWS QoS.