Boualem Benatallah

Automated paraphrasing has numerous applications such as query-answering, dialog systems, and machine translation. Recently, due to the limitations of automatic paraphrasing systems, crowdsourcing emerged as an alternative for obtaining corpora of paraphrases. Since crowdsourced paraphrases are generated by crowd workers with varied skills and motivations, such paraphrases may suffer from various quality issues (e.g. grammatical and spelling errors). Such quality issues stem from the lack of quality assessment methods for open-ended text in crowdsourcing platforms. Thus unqualified or malicious workers may generate erroneous paraphrases. Crowdsourced paraphrases must be assessed to filter low quality paraphrases, imposing an extra cost. Detecting such erroneous paraphrases requires an understanding of how malicious workers generate paraphrases. In this paper, we propose an effective approach to detect malicious worker behaviors based on a comprehensive analysis of cheating behavior characteristics. We investigate what models and features are effective in detecting such behaviors, and explore mechanisms and crowdsourcing prompts that could lead to an effective integration in an actual crowdsourcing task. Our results point to a significant reduction of cheating behavior (from 18% to 4.4%) in applying automatic malicious worker behavior detection, and highlight the impact of the detection (and detection mistakes) on workers corrective measures.

In the Metaverse, social activities and business processes are performed based on the sequences of workflows or service processes. Bridging the virtual space and the real world, such Metaverse services, present many new challenges and research questions to the aspects including, but not limited to service design, development, execution and optimization, interoperability, service governance, as well as the ethical and cultural implications. This special issue showcases the latest novel research efforts in advancing Metaverse services.

The Agentic Web is emerging as billions of AI agents discover, communicate, and coordinate across the open Web, shifting from isolated models to Web-integrated entities. This workshop explores the state of the art, open challenges, and emerging research directions in the foundations and architectures of the Agentic Web. It provides a forum for researchers and practitioners to examine interoperable architectures, protocols, and standards enabling AI agents to operate as first-class Web entities. Beyond technical interoperability, it also adresses economic and societal mechanisms including reputation, governance, accountability, and large-scale coordination.

The increasing deployment of opaque AI models in high-stakes domains has intensified the demand for Explainable AI (XAI) that is both cognitively aligned and operationally embedded. This survey reconceptualizes explainability as a reflexive, system-level property spanning the entire machine learning lifecycle. We introduce two novel dimensions: (i) a cognitively grounded taxonomy of explanation strategies–including analogical, contrastive, conceptual, narrative, and interactive forms–aligned with human reasoning models; and (ii) a lifecycle-centric architecture that embeds explainability across four interdependent layers: Operational, Explainability, Interactivity, and Governance. Through a systematic review of 202 peer-reviewed studies, we analyze trends in explanation formats, evaluation metrics, and domain-specific adaptations. We further present a comprehensive benchmark of 17 XAI techniques across tabular, image, and text modalities, evaluated using lifecycle-aware and cognitively aligned metrics such as fidelity, completeness, monotonicity, stability, and complexity. Together, these contributions offer a unified foundation for designing, evaluating, and deploying transparent, trustworthy, and human-centered AI systems.

Collaborative content generation (CCG) enables collective creation of artifacts like scientific articles. Quality is a paramount concern in CCG, and a multitude of methods have been proposed to evaluate the quality of artifacts. Nevertheless, the majority of these methods are reliant on centralized architectures, which present challenges pertaining to security, privacy, and availability. Blockchain technology proffers a potential resolution to these challenges, by furnishing a decentralized and immutable ledger of quality scores. In this manuscript, we introduce a blockchain-based quality control model for CCG that uses a semi-iterative algorithm to interdependently compute quality scores of artifacts and reputation of nodes. Our model addresses critical challenges in academic informetrics, such as citation manipulation, transparency in collaborative scholarship, and decentralized trust in metric computation. Our model also exhibits sensitivity to processing latency, rendering it more agile in the presence of delays. Our model’s quality scores, evaluated against PageRank and HITS baselines, show comparable performance, with additional assessments of throughput, latency, and robustness against malicious nodes confirming its reliability. A theoretical comparison with recent studies validates its feasibility for real-world informetric application.