Information (Switzerland)

The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented.

Francis Marco Maria Cirianni

Antonio Comi

Agata Quattrone

Information

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

City planners are facing an emergency to develop, promote, and implement actions that allow the mobility needs of people and businesses in cities (and their surroundings) to be satisfied to assure a better quality of life. Among the different actions to promote, there is public transport, which should be the cornerstone of sustainable urban mobility. The only way to achieve the transition from private to public is by integrating services into a multimodal network and then encouraging interchange between different modes of transportation. In this context, the development of mobility as a service (MaaS) solutions is significant, and different studies have been developed in recent years. The paper thus introduces a scientometric review of such a topic in relation to the Sustainable Development Goals of Agenda 2030. The study focuses on the current state of MaaS implementation, trends, and research gaps, with an in-depth analysis of emerging themes, based on 819 documents selected from the WoS and Scopus databases. Introducing the database, and research methodology, an accurate interpretation of the data generated by the bibliometric analysis, and the primary evaluation parameters are outlined. The identified studies have been then categorized into three thematic groups with the intention of offering a comprehensive study that identifies the shortcomings and difficulties in the research carried out in these areas up to today. Particular attention is paid to how this research relates to the Sustainable Development Goals.

Francis M. M. Cirianni

Lorenzo Cabras

Sustainable Mobility as a Service: A Scientometric Review in the Context of Agenda 2030

可持续移动即服务：在2030年议程背景下的科学计量学综述

With the support of big data mining techniques, utilizing social media data containing location information and rich semantic text information can construct large-scale daily activity OD flows for urban populations, providing new data resources and research perspectives for studying urban spatiotemporal structures. This paper employs the ST-DBSCAN algorithm to identify the residential locations of Weibo users in four communities and then uses the BERT model for activity-type classification of Weibo texts. Combined with the TF-IDF method, the results are analyzed from three aspects: temporal features, spatial features, and semantic features. The research findings indicate: ① Spatially, residents’ daily activities are mainly centered around their residential locations, but there are significant differences in the radius and direction of activity among residents of different communities; ② In the temporal dimension, the activity intensities of residents from different communities exhibit uniformity during different time periods on weekdays and weekends; ③ Based on semantic analysis, the differences in activities and venue choices among residents of different communities are deeply influenced by the comprehensive characteristics of the communities. This study explores methods for OD information mining based on social media data, which is of great significance for expanding the mining methods of residents’ spatiotemporal behavior characteristics and enriching research on the configuration of public service facilities based on community residents’ activity spaces and facility demands.

Zhiyuan Ou

Bingqing Wang

Bin Meng

Changsheng Shi

Dongsheng Zhan

Research on Resident Behavioral Activities Based on Social Media Data: A Case Study of Four Typical Communities in Beijing

Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Konstantinos Lavidas

Iro Voulgari

Stamatios Papadakis

Stavros Athanassopoulos

Antigoni Anastasiou

Andromachi Filippidi

Vassilis Komis

Nikos Karacapilidis

Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes

Legal knowledge involves multidimensional heterogeneous knowledge such as legal provisions, judicial interpretations, judicial cases, and defenses, which requires extremely high relevance and accuracy of knowledge. Meanwhile, the construction of a legal knowledge reasoning system also faces challenges in obtaining, processing, and sharing multisource heterogeneous knowledge. The knowledge graph technology, which is a knowledge organization form with triples as the basic unit, is able to efficiently transform multisource heterogeneous information into a knowledge representation form close to human cognition. Taking the automated construction of the Chinese legal knowledge graph (CLKG) as a case scenario, this paper presents a joint knowledge enhancement model (JKEM), where prior knowledge is embedded into a large language model (LLM), and the LLM is fine-tuned through the prefix of the prior knowledge data. Under the condition of freezing most parameters of the LLM, this fine-tuning scheme adds continuous deep prompts as prefix tokens to the input sequences of different layers, which can significantly improve the accuracy of knowledge extraction. The results show that the knowledge extraction accuracy of the JKEM in this paper reaches 90.92%. Based on the superior performance of this model, the CLKG is further constructed, which contains 3480 knowledge triples composed of 9 entities and 2 relationships, providing strong support for an in-depth understanding of the complex relationships in the legal field.

Jun Li

Lu Qian

Peifeng Liu

Taoxiong Liu

Construction of Legal Knowledge Graph Based on Knowledge-Enhanced Large Language Models

基于知识增强大型语言模型的法律知识图谱构建

Current research on futures price prediction focuses on the autocorrelation of historical prices, yet the resulting predictions often suffer from issues of inaccuracy and lag. This paper uses Chinese corn futures as the subject of study. First, we identify key influencing factors, such as Chinese soybean futures, U.S. soybean futures, and the U.S.-China exchange rate, that exhibit ‘predictive causality’ with corn futures prices through the Granger causality test. We then apply the sample convolution and interaction network (SCINet) to perform both single-step and multi-step predictions of futures prices. The experimental results show that incorporating key influencing factors significantly improves prediction accuracy. For instance, in the single-step prediction, combining historical prices with Chinese soybean futures prices reduces the MAE and RMSE values by 5.12% and 3.45%, respectively, compared to using historical prices alone. Furthermore, the SCINet model outperforms traditional models such as temporal convolutional networks (TCN), gated recurrent units (GRU), and long short-term memory (LSTM) networks when based solely on historical prices. This study validates the effectiveness of key influencing factors in forecasting Chinese corn futures prices and demonstrates the advantages of the SCINet model in futures price prediction. The findings provide valuable insights for optimising the agricultural futures market and enhancing the ability to predict price risks.

Yongxiang Wang

Qingyang Liu

Yanrong Hu

Hongjiu Liu

A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets

期货价格预测研究——聚焦不同经济市场的作用

Automatic speech recognition (ASR) and natural language processing (NLP) play key roles in advancing human–technology interactions, particularly in healthcare communications. This study aims to enhance French-language online mental health platforms through the adaptation of the QuartzNet 15 × 5 ASR model, selected for its robust performance across a variety of French accents as demonstrated on the Mozilla Common Voice dataset. The adaptation process involved tailoring the ASR model to accommodate various French dialects and idiomatic expressions, and integrating it with an NLP system to refine user interactions. The adapted QuartzNet 15 × 5 model achieved a baseline word error rate (WER) of 14%, and the accompanying NLP system displayed weighted averages of 64.24% in precision, 63.64% in recall, and an F1-score of 62.75%. Notably, critical functionalities such as ‘Prendre Rdv’ (schedule appointment) achieved precision, recall, and F1-scores above 90%. These improvements substantially enhance the functionality and management of user interactions on French-language digital therapy platforms, indicating that continuous adaptation and enhancement of these technologies are beneficial for improving digital mental health interventions, with a focus on linguistic accuracy and user satisfaction.

Mariem Jelassi

Khouloud Matteli

Houssem Ben Khalfallah

Jacques Demongeot

Enhancing Personalized Mental Health Support Through Artificial Intelligence: Advances in Speech and Text Analysis Within Online Therapy Platforms

通过人工智能增强个性化心理健康支持：在线治疗平台中语音和文本分析的进展

Healthcare institutions and health registries often store patients’ health data. In order to ensure privacy, sensitive medical information is stored separately from the identifying information of the patient. Generally, institutions anonymize medical information while sharing it for external use. However, internal users may also use it for identifying inaccuracies or missing information. Even though internal users may be legally permitted to access sensitive medical information, such access may lead to the identification of the patient, which can be vulnerable to patient privacy. Ensuring the accountability and responsibility of the internal users may lead to tractability in case of adversarial access with malicious intentions. Therefore, a secure system must be developed for the storage and retrieval of health data. To this end, in this paper, we propose a ledger-based system that cryptographically ensures that all access to health data must be logged into a ledger. Nevertheless, the ledger entries must be protected against adversarial access, too. At the same time, the ledger must be traversable by the patients as well as internal users. To address these needs, we propose techniques for the construction of a ledger to permit both internal users and patients to securely traverse and view only the entries to which they are linked.

Sunanda Bose

Dusica Marijan

Traversable Ledger for Responsible Data Sharing and Access Control in Health Research

可遍历账本：健康研究中负责任的数据共享与访问控制

In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure.

Nikitha Donekal Chandrashekar

Anthony Lee

Mohamed Azab

Denis Gracanin

Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms

理解用户行为以通过沉浸式游戏化平台增强网络安全培训

This paper explores the problem of the security of unmanned aerial vehicles (UAV) by introducing the drone security scoring system (D3S). D3S is a security assessment method that analyzes the security of a UAV model by analyzing its components. Penetration tests were carried out to support D3S and identify potential vulnerabilities in UAVs. Specific cyber-attacks, such as deauthentication, flooding, and replay, were executed in an effort to take full control of the UAVs. Eight different UAV models were assessed using D3S, revealing notable variations in performance, both in control communications and video transmission. Security scores ranging from 0.9 to 4.5 out of 5 were obtained, showing significantly divergent security levels.

Abbreviation
Journal Impact	2.22
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q3)

ISSN	2078-2489
h-index	59

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2010，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate: 99.53
Journal Fees	$Details
Average review cycle	6 weeks

Information (Switzerland)

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