The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly.

Large Language Models (LLMs) have transformed Natural Language Processing (NLP) and Software Engineering by fostering innovation, streamlining processes, and enabling data-driven decision-making. Recently, the adoption of LLMs in time-series analysis has catalyzed the emergence of time-series LLMs, a rapidly evolving research area. Existing reviews provide foundational insights into time-series LLMs but lack a comprehensive examination of recent advancements and do not adequately address critical challenges in this domain. This Systematic Literature Review (SLR) bridges these gaps by analysing state-of-the-art contributions in time-series LLMs, focusing on architectural innovations, tokenisation strategies, tasks, datasets, evaluation metrics, and unresolved challenges. Using a rigorous methodology based on PRISMA guidelines, over 700 studies from 2020 to 2024 were reviewed, with 59 relevant studies selected from journals, conferences, and workshops. Key findings reveal advancements in architectures and novel tokenization strategies tailored for temporal data. Forecasting dominates the identified tasks with 79.66% of the selected studies, while classification and anomaly detection remain underexplored. Furthermore, the analysis reveals a strong reliance on datasets from energy and transportation domains, highlighting the need for more diverse datasets. Despite these advancements, significant challenges persist, including tokenization inefficiencies, prediction hallucinations, and difficulties in modelling long-term dependencies. These issues hinder the robustness, scalability, and adaptability of time-series LLMs across diverse applications. To address these challenges, this SLR outlines a research roadmap emphasizing the improvement of tokenization methods, the development of mechanisms for capturing long-term dependencies, the mitigation of hallucination effects, and the design of scalable, interpretable models for diverse time-series tasks.

Massive open online courses (MOOCs) have revolutionized todays education by offering a global accessible form of online learning. Over the years, MOOCs have been an attractive research area and have yielded an ample amount of research publications. However, the existing review studies in MOOCs are characterized by short year coverage or focusing on a specific theme. As such, a systematic mapping methodology was adopted to provide a fine-grain overview of MOOC research domain by identifying the quantity, types of research, available results and publication trends in educational aspects of MOOCs from 2009 to 2018. Key findings show that I) MOOC research have been on the rise since MOOCs became mainstream in 2011. II) MOOC research largely resides in the United States and few European countries. II) Most of MOOC studies focused on addressing learners’completion/dropout/retention. In addition, we proposed some recommendations for future research on MOOCs.

Effective debugging is necessary for producing high quality and reliable software. Fault localization plays a vital role in the debugging process. However, fault localization is the most tedious and expensive activity in program debugging, as such, effective fault localization techniques that can identify the exact location of faults is eminent. Despite various fault localization techniques proposed, their application in multiple-fault programs is limited. The presence of multiple faults in a program reduces the efficacy of the existing fault localization techniques to locate faults effectively due to fault interference. Moreover, most of these techniques are unable to localize faults simultaneously. This has led researchers to adopt alternative approaches such as one-fault-at-a-time debugging and parallel debugging. In this paper, we propose a novel fault localization technique based on complex network theory (FLCN) to improve localization effectiveness in programs with single-fault and multiple-fault and to aid developers to localize multiple faults simultaneously in a single diagnosis rank list. The proposed technique ranks faulty statements based on their behavioural abnormalities and distance between faulty statements in both passed and failed test executions. Two graph-based centrality measures are adopted for fault diagnosis, namely degree centrality and closeness centrality and a new ranking formula is proposed. FLCN is evaluated across 14 subjects with both single-fault and multiple-fault programs. Our experimental results show that FLCN is more effective at locating faults when compared with existing state-of-the-art fault localization techniques with improvement of fault localization effectiveness in both single-fault and multiple-fault programs.