In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

The user location information represents a core dimension as understanding user context is a prerequisite for providing human-centered services that generally improve quality of life. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task due in part to the expected various objects (such as walls and people) that reflect and disperse signals. In this paper, we survey the related work in the field of indoor positioning by providing a comparative analysis of the state-of-the-art technologies, techniques, and algorithms. Unlike previous studies and surveys, our survey present new taxonomies, review some major recent advances, and argue on the area open problems and future potential. We believe this paper would spur further exploration by the research community of this challenging problem space.

With the rise of hate speech phenomena in the Twittersphere, significant research efforts have been undertaken in order to provide automatic solutions for detecting hate speech, varying from simple machine learning models to more complex deep neural network models. Despite this, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aimed to investigate several neural network models based on convolutional neural network (CNN) and recurrent neural network (RNN) to detect hate speech in Arabic tweets. It also evaluated the recent language representation model bidirectional encoder representations from transformers (BERT) on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contained 9316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, gated recurrent units (GRU), CNN + GRU, and BERT. Our experimental results in our dataset and an out-domain dataset showed that the CNN model gave the best performance, with an F1-score of 0.79 and area under the receiver operating characteristic curve (AUROC) of 0.89.

Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer‐based language models used for Arabic FND. We examine the use of neural networks and transformer‐based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer‐based models outperform the neural network‐based solutions, which led to an increase in the F1 score from 0.83 (best neural network‐based model, GRU) to 0.95 (best transformer‐based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network‐based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.

Loneliness and social isolation are serious and widespread problems among older people, affecting their physical and mental health, quality of life, and longevity. In this paper, we propose a ChatGPT-based conversational companion system for elderly people. The system is designed to provide companionship and help reduce feelings of loneliness and social isolation. The system was evaluated with a preliminary study. The results showed that the system was able to generate responses that were relevant to the created elderly personas. However, it is essential to acknowledge the limitations of ChatGPT, such as potential biases and misinformation, and to consider the ethical implications of using AI-based companionship for the elderly, including privacy concerns.

The following article investigates the Metaverse and its potential to bolster digital accessibility for persons with disabilities. Through qualitative analysis, we examine responses from eleven experts in digital accessibility, Metaverse development, disability advocacy, and policy formulation. This exploration uncovers key insights into the Metaverse’s current state, its inherent principles, and the challenges and opportunities it presents in terms of accessibility. The findings reveal a mixed state of inclusivity within the Metaverse, highlighting significant advancements along with notable gaps, especially in integrating assistive technologies and ensuring interoperability across different virtual environments. This study emphasizes the Metaverse’s potential to revolutionize experiences for individuals with disabilities, provided that accessibility is embedded in its foundational design. Ethical and legal considerations, such as privacy, non-discrimination, and evolving legal frameworks, are identified as critical factors that shape an inclusive Metaverse. We propose a comprehensive framework that emphasizes technological adaptation and innovation, user-centric design, universal access, social and economic considerations, and global standards. This framework aims to guide future research and policy interventions to foster an inclusive digital environment in the Metaverse. This paper contributes to the emerging discourse on the Metaverse and digital accessibility, offering a nuanced understanding of its complexities and a roadmap for future exploration and development. This underscores the necessity of a multi-faceted approach that incorporates technological innovation, user-centered design, ethical considerations, legal compliance, and continuous research to create an inclusive and accessible Metaverse.