The last few years have seen the ubiquitous diffusion of JSON as one of the most widely used formats for publishing and interchanging data, as it combines the flexibility of semistructured data models with well-known data structures such as objects and arrays. While various schema languages for describing JSON data have been proposed in the past (e.g., JSound and Joi), JSON Schema established itself as de-facto standard schema language for JSON data. The main aim of this tutorial is to provide the reader with the basic notions about JSON Schema. This tutorial focuses on four main aspects: (1) we first describe Classical JSON Schema and introduce the features that are shared with the latest versions of the specification; (2) we introduce, then, Modern JSON Schema, explain why it differs from Classical JSON Schema, and discuss its novel evaluation model; (3) we analyze tools that support or exploit JSON Schema, like, for example, validators and data generators; and (4) we highlight open research challenges and opportunities related to JSON Schema.
This paper aims to explore the applications of smart bus systems and itgives an overview of the main terms related to public transportation systems in smart cities and introduces several relevant technologies. The study explores the application of machine learning and related technologies to enhance bus arrival predictions within smart city frameworks by reviewing 120 papers selected from IEEE Xplore, Google Scholar and arXiv. Key findings categorize research into four areas revealing a balanced distribution as depicted. The survey traces the evolution from early GPS-based methods to integrated data-driven approaches, highlighting improvements in prediction accuracy, urban efficiency, and passenger experience, while noting challenges. Statistical analysis of publication trends from 2002 to 2025, with a peak of 16 papers in 2017, underscores growing interest, offering a valuable reference for advancing intelligent transportation systems. By studying and analyzing the latest situations of smart bus arrivals, the paper summarizes the findings based on different applied technologies and presents several key points. Additional, this study discusses four main research topics related to smart bus arrivals and shows the potential value of smart bus arrival systems in smart cities. Through a systematic review of academic papers on smart bus arrivals, it provides a deeper understanding of the basic concepts and processes of these systems. It is expected that this review will serve as a useful reference for future research in the fields of smart buses and smart cities.
Static Code Analysis (SCA) tools play a vital role in software validation and verification. However, no recent secondary study summarises existing primary studies that evaluate the performance of existing SCA tools. Such a study is vital to identify and inform relevant and useful SCA tools for the community. To address the gap, systematic mapping and grey literature studies were conducted, comparing existing SCA tools evaluated in academia and by the software engineering practitioner community to generate insights into their performance. We performed detailed meta-analyses of the outcomes from these studies, identifying the SCA tools studied, the performance metrics used to study them, the research approaches used to evaluate SCA tools in academia, and the best-performing SCA tools. We found that only 26.7% of the SCA tools reported in the practitioner community have been studied in academia, and there are significant gaps in academia evaluating SCA tools compared with the software engineering practitioner community. Additionally, the best tools reported in the software engineering community have high false-positive and high false-negative rates. Outcomes of our study also show divergent approaches among the academic and practitioner communities for evaluating the performance of SCA tools. With enhancement efforts, SCA tools may enhance developers’ workflows.
Explainable AI (XAI) is often evaluated through claims about trust, reliance, understanding, and performance, yet studies measure these constructs differently and frequently overlook confounding factors. This PRISMA-guided systematic review with narrative synthesis examines human-centered XAI evaluation across these four constructs. The review covers publications from 2015 to 2025. A merged search combined an OpenAlex-informed DOI/title catalog, prior surveys, citation chasing, and a Scopus coverage-completion stage identifying 9,026 records. A targeted revision update added two eligible publications, yielding 119 validated full-text publications. The synthesis highlights three patterns: trust and reliance can diverge; perceived understanding can exceed objective understanding; and interface salience or perceived accuracy can confound explanation effects. Aggregate advice acceptance, for example, cannot distinguish beneficial reliance from automation bias. We therefore recommend measuring acceptance separately for correct and incorrect AI outputs, testing understanding beyond self-report, documenting study context and interface conditions, and evaluating explanations under uncertainty or model failure. The review contributes construct definitions, a synthesis across designs and domains, an analysis of evaluation measures, and practical reporting guidelines. It also examines generated explanations and citation-based interfaces, supporting more reproducible and interpretable human-centered XAI evaluations.
Endowing humanoid robots with expressive, controllable, and robust facial expressions and behaviors is essential for advancing human-like humanoid-human interaction across social, healthcare, and service domains. However, transforming the semantic richness and flexibility of virtual avatars into the mechanical, real-time, and calibration constraints of physical humanoids remains a fundamental challenge. This survey reviews facial expression and behavior modeling for both and analyzes their virtual-to-physical gaps from a deployment-oriented perspective. Our review is organized along three axes: (i) modeling paradigms , with emphasis on actuator compatibility and transferability; (ii) expression control strategies , assessed for their potential to enable robust robotic deployment; and (iii) deployment dimensions , which examine the challenges of parameter-to-actuator mapping, calibration, and real-time operation. This survey provides a critical synthesis of the trade-offs between expressive fidelity and hardware feasibility. We analyze persistent gaps between algorithmic advances and embodied realization, and review the datasets, metrics, and evaluation frameworks necessary for advancing expressive humanoids and their facial expression and behavior modeling. Finally, we identify open challenges and future research directions, including multimodal coordination, adaptive personalization, and standardized evaluation, providing guidance for next-generation expressive human-like humanoids, and systematically integrating advances in humanoid modeling with actionable deployment of humanoid facial expressions and behaviors.
