Information

24 pages, 1783 KB

Open AccessArticle

New Circuits for Simultaneously Initiating Two Different Quantum Superpositions

by Artyom M. Grigoryan and Alexis A. Gomez

Information 2025, 16(12), 1043; https://doi.org/10.3390/info16121043 (registering DOI) - 30 Nov 2025

This article presents, for the first time, a new approach to building quantum circuits for the initialization of two multi-qubit superpositions, namely, two different superpositions in one circuit, not in two separate circuits. For this, we introduce the concept of the discrete two [...] Read more.

This article presents, for the first time, a new approach to building quantum circuits for the initialization of two multi-qubit superpositions, namely, two different superpositions in one circuit, not in two separate circuits. For this, we introduce the concept of the discrete two signal-induced heap transformation (D2siHT). This transformation is generated by two signals, or vectors, which we call generators. The quantum analogue of the D2siHT is described. It allows us to build a quantum circuit to transform two superpositions

| x⟩

and

| y⟩

into the first conventual basis states

| 000 \dots 0⟩

and

| 010 \dots 0⟩

, respectively. Therefore, we can build a single quantum circuit to initiate two multi-qubit superpositions

| x⟩

and

| y⟩

from the basis states

| 000 \dots 0⟩

and

| 010 \dots 0⟩

, respectively. Examples with quantum circuits for the preparation and transformation of two 2- and 3-qubit superpositions are described in detail. The results of circuit simulation using Qiskit are also presented. The main characteristic of the D2siHT is its path of processing the data of two generators and input qubits. We consider different paths to effectively compute the D2siHT. Such paths can reduce, for instance, the depth of the resulting quantum circuits, which can lead to a reduction in execution times and susceptibility to decoherence and noise. Multi-qubit superpositions are considered with real amplitudes, but the presented approach can be extended to initiate two such superpositions with complex amplitudes, as well. Full article

15 pages, 2327 KB

Open AccessArticle

Enhancing Weakly Supervised Video Anomaly Detection with Object-Centric Features

by Yanyu Wang, Yang Chen and Chai Kiat Yeo

Information 2025, 16(12), 1042; https://doi.org/10.3390/info16121042 (registering DOI) - 30 Nov 2025

Abstract

Surveillance cameras are extensively deployed across public and private environments, driving the need for intelligent video monitoring systems. However, a major challenge arises in Weakly Supervised Video Anomaly Detection (WSVAD), where supervision is limited to video-level labels, making snippet-level anomaly localisation particularly difficult. [...] Read more.

Surveillance cameras are extensively deployed across public and private environments, driving the need for intelligent video monitoring systems. However, a major challenge arises in Weakly Supervised Video Anomaly Detection (WSVAD), where supervision is limited to video-level labels, making snippet-level anomaly localisation particularly difficult. This challenge is often formulated as a Multiple Instance Learning (MIL) problem. Although recent approaches have achieved encouraging results by modelling spatio-temporal dynamics, they often overlook the semantic information within videos that could further enhance anomaly detection. To bridge this gap, we propose enriching feature representations by applying object detection techniques to extract object-centric features. These features provide supplementary high-level semantic information that supports the discrimination of anomalous events. Experiments conducted on two benchmark datasets, UCF-Crime and ShanghaiTech, demonstrate that our approach achieves performance comparable to state-of-the-art (SOTA) methods. The results highlight that incorporating object-level semantics offers a promising direction for improving WSVAD, underscoring the potential of semantic-aware approaches for more effective anomaly detection. Full article

(This article belongs to the Special Issue Computer Vision for Security Applications, 2nd Edition)

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33 pages, 502 KB

Open AccessReview

Generalized Game Theory in Perspective: Foundations, Developments and Applications for Socio-Economic Decision Models

by Ewa Roszkowska

Information 2025, 16(12), 1041; https://doi.org/10.3390/info16121041 (registering DOI) - 29 Nov 2025

Abstract

Classical game theory provides powerful tools for modeling strategic interaction, but often overlooks the social, cultural, and institutional dimensions of human behavior. To address this gap, Tom Burns and collaborators developed generalized game theory (GGT) and later sociological game theory (SGT). These frameworks [...] Read more.

Classical game theory provides powerful tools for modeling strategic interaction, but often overlooks the social, cultural, and institutional dimensions of human behavior. To address this gap, Tom Burns and collaborators developed generalized game theory (GGT) and later sociological game theory (SGT). These frameworks extend classical game theory by embedding rules, norms, values, beliefs, roles, and institutional structures into formal models of interaction. This review synthesizes thirty key contributions to this research program, organizing the literature into eight thematic areas and providing an integrated overview of the field. The originality of this work lies in its comprehensive approach, which advances conceptual and formal foundations while exploring practical applications and outlining directions for future research. GGT/SGT develops rule-based modeling, the analysis of norms and values, multiple modalities of action determination, and various equilibrium types, offering a rigorous framework for understanding strategic behavior in complex social contexts. In application, these approaches provide insights into organizational processes, negotiation, legitimacy, distributive justice, and institutionalized procedures, while integrating interactionist and group-theoretical perspectives. By linking formal modeling with normative and institutional analysis, GGT/SGT offers innovative socio-economic decision models that capture uncertainty, fairness, legitimacy, and institutional transformation. It extends classical game theory by bridging mathematics, economics, and sociology, providing a versatile theoretical tool for understanding complex socio-economic systems and improving strategic decision-making in contemporary society. Full article

(This article belongs to the Special Issue Decision Models for Economics and Business Management)

28 pages, 2001 KB

Open AccessArticle

NIABIAuth: A Non-Interactive Attribute Binding Identity Authentication Protocol for Internet of Things Terminals

by Zilong Han, Xinge Li, Chaoqun Kang, Haowen Sun and Yali Gao

Information 2025, 16(12), 1040; https://doi.org/10.3390/info16121040 (registering DOI) - 28 Nov 2025

Abstract

The large-scale deployment of distributed devices in the Internet of Things (IoT) brings urgent demands for secure, scalable, and lightweight identity authentication. For example, virtual power plants integrate numerous heterogeneous energy terminals to support grid dispatch and market operations, while posing challenges such [...] Read more.

The large-scale deployment of distributed devices in the Internet of Things (IoT) brings urgent demands for secure, scalable, and lightweight identity authentication. For example, virtual power plants integrate numerous heterogeneous energy terminals to support grid dispatch and market operations, while posing challenges such as real-time access, resource constraints, and identity privacy protection. To address these challenges, this paper proposes NIABIAuth, a non-interactive attribute binding identity authentication protocol for IoT terminals. NIABIAuth supports dynamic challenge computation and binds cryptographic identity proofs with terminal attributes, enabling fine-grained and privacy preserving access control. By storing identity credentials and verification records on the chain, this protocol ensures traceability and tamper resistance. Experiments demonstrate that NIABIAuth maintains low authentication latency and is consistent throughput, even under constrained conditions. Compared with baseline methods, NIABIAuth achieves substantial reductions in communication and computation cost. The proposed NIABIAuth was formally verified using the AVISPA tool, which proved that it could resist common attacks, including replay attacks, man-in-the-middle attacks, etc. A large number of simulation experiments have demonstrated that the proposed protocol can provide real-time identity authentication for Internet of Things terminals. Full article

26 pages, 1737 KB

Open AccessArticle

Fairness-Aware Intelligent Reinforcement (FAIR): An AI-Powered Hospital Scheduling Framework

by Ruba Abualrous, Hala Zouzou, Rita Zgheib, Alaa Hasan, Bilal Hijazi and Arash Kermani

Information 2025, 16(12), 1039; https://doi.org/10.3390/info16121039 (registering DOI) - 28 Nov 2025

Abstract

Hospitals must allocate beds and staff effectively under volatile arrival patterns, where scheduling errors can cause preventable harm. This study introduces a fairness-aware, forecast-informed reinforcement learning framework for hospital scheduling, explicitly integrating fairness constraints, short-term demand forecasts, and SHAP-based explainability. The state fuses [...] Read more.

Hospitals must allocate beds and staff effectively under volatile arrival patterns, where scheduling errors can cause preventable harm. This study introduces a fairness-aware, forecast-informed reinforcement learning framework for hospital scheduling, explicitly integrating fairness constraints, short-term demand forecasts, and SHAP-based explainability. The state fuses patient and system context with short-horizon demand forecasts, mean arrivals (

\hat{λ}

), and volatility (

{\hat{σ}}^{2}

). The reward jointly optimizes efficiency, equity, and safety by penalizing waiting, diversions/transfers, ICU misuse, overtime, and cross-ward disparity. Using a benchmark-aligned synthetic cohort ( 60k visits over one year), the approach is compared against First-Come-First-Served (FCFS) and ablations without forecast features. The learned policy halves the mean waiting time (from 215.3 to 102.5 min), reduces diversions/transfers (from 27.6% to 7.8%), improves ICU match accuracy (from 93.4% to 95.1%), raises the fairness index by 45%, and cuts staff overtime by 56%. Adding forecast signals yields further gains over forecast-naive DQN ( 9% shorter waits; 28% fewer diversions/transfers), with robustness under demand surges and triage-mix shifts. By unifying equity constraints, anticipatory context, and explanation, the method turns reactive queues into proactive, auditable control and is extensible to perioperative flow, disaster triage, and outpatient capacity management. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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45 pages, 5846 KB

Open AccessArticle

A Machine Learning Framework for Harvesting and Harmonizing Cultural and Touristic Data

by Kimon Deligiannis, Christos Tryfonopoulos, Paraskevi Raftopoulou, Costas Vassilakis, Vassilis Kaffes and Spiros Skiadopoulos

Information 2025, 16(12), 1038; https://doi.org/10.3390/info16121038 (registering DOI) - 28 Nov 2025

Abstract

Cultural and touristic information is increasingly available through a multitude of heterogeneous sources, including official repositories, community platforms, and open data initiatives. While prominent landmarks are typically covered across sources, less-known attractions are also documented with varying degrees of detail, resulting in fragmented, [...] Read more.

Cultural and touristic information is increasingly available through a multitude of heterogeneous sources, including official repositories, community platforms, and open data initiatives. While prominent landmarks are typically covered across sources, less-known attractions are also documented with varying degrees of detail, resulting in fragmented, overlapping, or complementary content. To enable integrated access to this wealth of information, harvesting and consolidation mechanisms are required to collect, reconcile, and unify distributed content referring to the same entities. This paper presents a machine learning-driven framework for harvesting, homogenizing, and augmenting cultural and touristic data across multilingual sources. Our approach addresses entity resolution, duplication detection, and content harmonization, laying the foundation for enriched, unified representations of attractions and points of interest. The framework is designed to support scalable integration pipelines and can be deployed in applications aimed at tourism promotion, digital heritage, and smart travel services. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Systems")

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19 pages, 428 KB

Open AccessArticle

Vocational Education and Training in the European Union: A Data-Driven Comparative Analysis

by Alicia Vila, Laura Calvet, Josep Prieto and Angel A. Juan

Information 2025, 16(12), 1037; https://doi.org/10.3390/info16121037 - 27 Nov 2025

Abstract

Vocational education and training (VET) is a strategic driver of national education and skills development systems. It covers both Initial VET (IVET), which provides young people with vocational qualifications before they enter the labor market, and Continuing VET (CVET), which supports adults in [...] Read more.

Vocational education and training (VET) is a strategic driver of national education and skills development systems. It covers both Initial VET (IVET), which provides young people with vocational qualifications before they enter the labor market, and Continuing VET (CVET), which supports adults in updating or expanding their skills throughout their working lives. VET provides individuals with essential skills for employment and supports economies in adapting to technological, labor market, and social changes. Within the European Union (EU), VET plays a central role in addressing labor market transformation, the green and digital transitions, the rise of artificial intelligence, and the pursuit of social equity. This paper presents a data-driven analysis of VET in the EU countries. It reviews the relevant literature and outlines the role of Cedefop, the European Centre for the Development of Vocational Training, together with its main VET performance indicators. The analysis draws on publicly available Cedefop data on key VET indicators, filtered for reliability and systematically processed to ensure robust results. This research focuses on a selected set of key indicators covering participation in IVET at upper- and post-secondary levels, adult participation in both formal and non-formal learning, government and enterprise expenditure on training, the gender employment gap, and adult employment rates. These indicators are derived from Cedefop data spanning the period 2010–2024, with coverage varying across indicators. This study applies descriptive analysis to identify outlier countries, correlation analysis to explore relationships between indicators, and cluster analysis to group countries with similar VET profiles. It also compares the largest EU countries using common indicators. The results suggest key patterns, differences, and connections in VET performance across EU countries, offering insights for policy development and future research in VET. Full article

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45 pages, 2794 KB

Open AccessSystematic Review

Explainable AI-Based Intrusion Detection Systems for Industry 5.0 and Adversarial XAI: A Systematic Review

by Naseem Khan, Kashif Ahmad, Aref Al Tamimi, Mohammed M. Alani, Amine Bermak and Issa Khalil

Information 2025, 16(12), 1036; https://doi.org/10.3390/info16121036 - 27 Nov 2025

Abstract

Industry 5.0 represents a paradigm shift toward human–AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented/Virtual Reality (AR/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, [...] Read more.

Industry 5.0 represents a paradigm shift toward human–AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented/Virtual Reality (AR/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, including intrusion detection, malware identification, and phishing prevention, cybersecurity professionals have shown reluctance toward adopting black-box machine learning solutions due to their opacity. This hesitation has accelerated the development of explainable artificial intelligence (XAI) techniques that provide transparency into AI decision-making processes. This systematic review examines XAI-based intrusion detection systems (IDSs) for Industry 5.0 environments. We analyze how explainability impacts cybersecurity through the critical lens of adversarial XAI (Adv-XIDS) approaches. Our comprehensive analysis of 135 studies investigates XAI’s influence on both advanced deep learning and traditional shallow architectures for intrusion detection. We identify key challenges, opportunities, and research directions for implementing trustworthy XAI-based cybersecurity solutions in high-stakes Industry 5.0 applications. This rigorous analysis establishes a foundational framework to guide future research in this rapidly evolving domain. Full article

(This article belongs to the Special Issue Reliable and Secure AI Systems)

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19 pages, 1415 KB

Open AccessArticle

LFRE-YOLO: Lightweight Edge Computing Algorithm for Detecting External-Damage Objects on Transmission Lines

by Min Liu, Benhui Wu and Ming Chen

Information 2025, 16(12), 1035; https://doi.org/10.3390/info16121035 - 27 Nov 2025

Abstract

Transmission lines in complex outdoor environments often suffer external damage in construction areas, severely affecting the stability of power systems. Traditional manual detection methods have problems of low efficiency and poor real-time performance. In deep learning-based detection methods, standard convolution has a large [...] Read more.

Transmission lines in complex outdoor environments often suffer external damage in construction areas, severely affecting the stability of power systems. Traditional manual detection methods have problems of low efficiency and poor real-time performance. In deep learning-based detection methods, standard convolution has a large parameter count and computational complexity, making it difficult to deploy on edge devices; while lightweight depthwise separable convolution offers low computational cost, it suffers from insufficient feature extraction capability. This limitation stems from its independent processing of each channel’s information, making it unable to simultaneously meet the practical requirements for both lightweight design and high detection accuracy in transmission line monitoring applications. To address the above problems, this study proposes LFRE-YOLO, a lightweight external damage detection algorithm for transmission lines based on YOLOv10n. This study proposes LFRE-YOLO, a lightweight external damage detection algorithm based on YOLOv10n. First, we design a lightweight feature reuse and enhancement convolution (LFREConv) that overcomes the limitations of traditional depthwise separable convolution through cascaded dual depthwise convolution structure and residual connection mechanisms, significantly expanding the effective receptive field with minimal parameter increment and compensating for information loss caused by independent channel processing in depthwise convolution through feature reuse strategies. Second, based on LFREConv, we propose an efficient lightweight feature extraction module (LFREBlock) that achieves cross-channel information interaction enhancement and channel importance modeling. Additionally, we propose a lightweight feature reuse and enhancement detection head (LFRE-Head) that applies LFREConv to the regression branch, achieving comprehensive lightweight design of the detection head while maintaining spatial localization accuracy. Finally, we employ layer-adaptive magnitude-based pruning (LAMP) to prune the trained model, further optimizing the network structure through layer-wise adaptive pruning. Experimental results demonstrate significant improvements over YOLOv10n baseline: mAP50 increased from 92.0% to 94.1%, mAP50-95 improved from 66.2% to 70.2%, while reducing parameters from 2.27 M to 0.99 M, computational complexity from 6.5 G to 3.1 G, and achieving 86.9 FPS inference speed, making it suitable for resource-constrained edge computing environments. Full article

(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)

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17 pages, 1689 KB

Open AccessArticle

Exchangeability and Bayesian Inference: A Theoretical and Computational Framework for Reliable Experimental Data Analysis

by Tommaso Costa and Mario Ferraro

Information 2025, 16(12), 1034; https://doi.org/10.3390/info16121034 - 27 Nov 2025

Abstract

Exchangeability is a foundational concept in Bayesian statistics, crucial for ensuring the validity and generalizability of inferences from experimental data. This paper presents a theoretical and computational framework for understanding the role of exchangeability in the reliability of scientific conclusions, with specific reference [...] Read more.

Exchangeability is a foundational concept in Bayesian statistics, crucial for ensuring the validity and generalizability of inferences from experimental data. This paper presents a theoretical and computational framework for understanding the role of exchangeability in the reliability of scientific conclusions, with specific reference to psychology, neuroimaging, and clinical trials. We build on de Finetti’s representation theorem to show how exchangeability enables using hierarchical Bayesian models, and we analyze how its violation can lead to interpretative errors and paradoxes, such as Simpson’s Paradox. In addition to theoretical discussion, we present practical strategies to evaluate and enforce exchangeability, including randomization, matching, stratification, and hierarchical modeling. We also introduce computational tools—such as the Shuffle Test and Stratified Bootstrap—to empirically test for exchangeability and detect latent structures in the data. The novelty of this work lies in unifying theoretical reasoning and empirical testing within a single framework that bridges de Finetti’s representation theorem and resampling-based diagnostics. By providing concrete tools to evaluate exchangeability prior to model fitting, the proposed approach introduces a pre-analysis verification step that strengthens the reliability and transparency of Bayesian inference. Our results emphasize that exchangeability is not merely a technical assumption, but a structural property that governs the coherence and informational integrity of the data. This framework provides both conceptual clarity and operational tools for researchers aiming to perform robust Bayesian inference in complex and heterogeneous datasets. Full article

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19 pages, 2181 KB

Open AccessArticle

Non-Line-of-Sight Identification Method for Ultra-Wide Band Based on Dual-Branch Feature Fusion Transformer

by Guangyong Xi, Shuaiyang Hu, Jing Wang and Dongyao Zou

Information 2025, 16(12), 1033; https://doi.org/10.3390/info16121033 - 27 Nov 2025

Abstract

In Ultra-Wide Band (UWB) positioning, wireless signals are subject to non-line-of-sight (NLOS) propagation due to obstruction by obstacles, which leads to ranging and positioning estimation errors. How to accurately and efficiently identify line-of-sight (LOS) and NLOS propagation paths is a key research task [...] Read more.

In Ultra-Wide Band (UWB) positioning, wireless signals are subject to non-line-of-sight (NLOS) propagation due to obstruction by obstacles, which leads to ranging and positioning estimation errors. How to accurately and efficiently identify line-of-sight (LOS) and NLOS propagation paths is a key research task in UWB positioning systems. By effectively integrating the characteristics of global channel impulse response (CIR) sequence features and statistical time-domain features, a dual-branch feature fusion Transformer (DBFF-Transformer) is proposed for NLOS path identification. Firstly, the original CIR sequence data is processed using the Transformer to learn the global feature relationships within the data. Secondly, four key time-domain features are extracted from the CIR sequence: the first-path energy ratio, the root-mean-square time delay spread, the kurtosis and the phase difference. Finally, by integrating the sequence features and the time-domain features, the two features’ branches are fused through a fully connected network. The proposed method is evaluated in two typical indoor scenarios from the latest open-source datasets of the eWINE project. The ablation experiment proves that the fusion of the sequence features and time-domain features of the CIR sequence can effectively improve NLOS identification accuracy. The identification accuracy in the two experimental scenarios is 95.9% and 95.7%, with F1 scores of 97.2% and 97.1% and Recall of 97.4% and 96.4%, respectively. The comparative analysis of the DBFF-Transformer with the state-of-the-art baseline models demonstrates superior accuracy and robustness, which can provide a novel solution for NLOS identification in UWB indoor positioning. Full article

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7 pages, 154 KB

Open AccessEditorial

Advances in Data and Network Sciences Applied to Computational Social Science

by Leonardo N. Ferreira

Information 2025, 16(12), 1032; https://doi.org/10.3390/info16121032 - 26 Nov 2025

Abstract

Data Science, a cornerstone of modern research, focuses on extracting knowledge and insights from vast and often unstructured datasets [...] Full article

(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)

22 pages, 1533 KB

Open AccessArticle

Neural Network-Based Optimization of Repair Rate Estimation in Performance-Based Logistics Systems

by Milan Dejanović, Stefan Panić, Nataša Kontrec, Danijel Đošić and Saša Milojević

Information 2025, 16(12), 1031; https://doi.org/10.3390/info16121031 - 26 Nov 2025

Abstract

Performance-Based Logistics (PBL) frameworks prioritize system availability by optimizing maintenance strategies, with repair rate estimation playing a critical role in predictive maintenance planning. This study proposes a machine learning-based approach for repair rate prediction, leveraging fully connected neural networks (FCNNs) and Long Short-Term [...] Read more.

Performance-Based Logistics (PBL) frameworks prioritize system availability by optimizing maintenance strategies, with repair rate estimation playing a critical role in predictive maintenance planning. This study proposes a machine learning-based approach for repair rate prediction, leveraging fully connected neural networks (FCNNs) and Long Short-Term Memory (LSTM) networks trained on repair rate samples generated from a stochastic model. The FCNN estimates maximum repair rates, while the LSTM predicts minimum repair rates, capturing both steady-state and sequential dependencies in repair rate variations. By eliminating the need for complex mathematical formulations, the proposed methodology provides a scalable and computationally efficient alternative to traditional stochastic models. Extensive performance evaluations demonstrate that the neural networks achieve higher accuracy and lower computational costs compared to stochastic approaches, making them well-suited for real-time predictive maintenance applications. This research enhances decision-making in maintenance planning, optimizes resource allocation, and improves overall system reliability within PBL frameworks. Full article

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19 pages, 3709 KB

Open AccessArticle

Constructing “China Data Valley”: Computational Text Mining Reveals the Media Framing of an Inland City’s Reinvention

by Miao Ao and Li Shao

Information 2025, 16(12), 1030; https://doi.org/10.3390/info16121030 - 26 Nov 2025

Abstract

This study examines how an inland city constructs a cutting-edge technological identity through media framing, thereby addressing a critical gap in urban image research. Focusing on Guiyang’s reinvention as “China Data Valley”, we analyzed a comprehensive corpus of news articles (January 2018–September 2025) [...] Read more.

This study examines how an inland city constructs a cutting-edge technological identity through media framing, thereby addressing a critical gap in urban image research. Focusing on Guiyang’s reinvention as “China Data Valley”, we analyzed a comprehensive corpus of news articles (January 2018–September 2025) using an integrated computational framework that incorporates descriptive statistics, LDA topic modeling, and co-word network analysis. This study investigated three dimensions: the distribution of discourse power among media actors, the narrative architecture of the urban image, and the distinct framing strategies employed by different media types. The results reveal that national official media are the primary drivers of image construction. The narrative is structured around a three-layer architecture. Furthermore, media types exhibit distinct framing strategies. This study provides practical insights for urban branding and contributes to media framing theory by extending its geographical focus to inland technology hubs and elucidating image construction within China’s unique media ecosystem. Finally, it proposes a replicable analytical model for latecomer regions seeking a discursive breakthrough in the digital era. Full article

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18 pages, 1169 KB

Open AccessArticle

Fusion of Deep Reinforcement Learning and Educational Data Mining for Decision Support in Journalism and Communication

by Weichen Jia and Zhi Li

Information 2025, 16(12), 1029; https://doi.org/10.3390/info16121029 - 26 Nov 2025

Abstract

The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data [...] Read more.

The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data mining (EDM) and deep reinforcement learning (DRL) to address these issues. A bidirectional long short-term memory (Bi-LSTM) network models behavioral sequences, while a conditional generative adversarial network (cGAN) with Wasserstein optimization enhances low-activity student data. The extracted and augmented features are then fed into a Double Deep Q-Network (DQN) to generate adaptive teaching intervention strategies. Experimental results from a 26-week study show that the proposed framework improved personalized learning-path matching from 0.42 to 0.68, increased knowledge mastery from 40.46% to 77.13%, and reduced intervention latency from 210.5 min to 144.6 min. The results demonstrate that the fusion of EDM and DRL can achieve efficient and adaptive decision-making, providing a viable approach for intelligent teaching support in journalism and communication education. Full article

(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)

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23 pages, 494 KB

Open AccessArticle

Exploring the Paradigm of Enterprise Data Protection: Constructing Enterprise Data Rights Under the Rightification of Behavioral Regulation

by Xiaowei Hu, Wen Ding and Deqian Fu

Information 2025, 16(12), 1028; https://doi.org/10.3390/info16121028 - 25 Nov 2025

Abstract

Against the backdrop of the marketization of data as a production factor, the configuration of enterprise data rights has become a core legal issue for incentivizing data production and promoting data circulation and utilization. However, the complexity of enterprise data—manifested in its object [...] Read more.

Against the backdrop of the marketization of data as a production factor, the configuration of enterprise data rights has become a core legal issue for incentivizing data production and promoting data circulation and utilization. However, the complexity of enterprise data—manifested in its object forms, interest subjects, and usage scenarios—has made both the traditional rights-protection paradigm and the behavioral-regulation paradigm difficult to apply. Through an analysis of existing enterprise data protection models, this study identifies that the “rightification of behavioral regulation” paradigm—originating from the bundle of rights theory and applied in the intellectual property law—combines and designs the object types and object utilization methods it encompasses to design a rights architecture logic. This framework effectively overcomes the challenges of the variability of object forms, the diversity of subject definitions and the dynamism of power division in the construction of enterprise data rights, providing a crucial paradigm foundation for the construction of enterprise data rights. This effectively overcomes the variability of object forms, the diversity of subject definitions and the dynamism of power division in the construction of enterprise data rights, providing a crucial paradigm foundation for the construction of enterprise data rights. Building upon this theoretical basis, the study proposes a systematic framework for enterprise data rights: at the level of system positioning, positioning them as a new form of property right parallel to real rights and intellectual property rights; and at the level of rights content, defining their objects to include both data sets and data products, while designing a dynamic bundle of rights centered on possession, processing and use, and operation. Full article

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25 pages, 1832 KB

Open AccessArticle

A Bibliographic Analysis of Research Trends on Privacy in Technology Adoption: Information Synthesis Perspective

by Sung Hee Jang and Chang Won Lee

Information 2025, 16(12), 1027; https://doi.org/10.3390/info16121027 - 25 Nov 2025

Abstract

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 [...] Read more.

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 to 2025. The analysis was conducted using CiteSpace, incorporating cluster analysis, timeline analysis, and burst detection to identify key patterns and developments. Fifteen sub-areas of privacy related to technology adoption were identified, including health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, location-based services, mobile commerce, ubiquitous commerce adoption, tracing apps, metaverse adoption, and facial recognition payment. Timeline analysis provided insights into the growth or decline of these research clusters over time. Based on the findings, a framework was developed to illustrate key insights and their interconnections, offering guidance for future research. The study concludes by discussing its implications, limitations, and recommendations for further research. Full article

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17 pages, 1722 KB

Open AccessSystematic Review

The Role of Mobile Applications in Shaping Digital Transformation in Higher Education Among Generation I: A Bibliographic Study

by Anusuyah Subbarao, Aysa Siddika, Muhammad Afif Fathullah and Mohd Azizi Bin Sanwani

Information 2025, 16(12), 1026; https://doi.org/10.3390/info16121026 - 25 Nov 2025

Abstract

This study examines the current Digital Transformation (DT) knowledge in higher education, focusing on mobile app usage among Generation I. It examines publication trends, influential works, intellectual structures, keyword trends, and network visualizations to provide a foundation for future research and practice. This [...] Read more.

This study examines the current Digital Transformation (DT) knowledge in higher education, focusing on mobile app usage among Generation I. It examines publication trends, influential works, intellectual structures, keyword trends, and network visualizations to provide a foundation for future research and practice. This study used bibliometric analysis to examine articles from the Scopus database published from 2014 to 2024. Using the PRISMA method, the authors identified the articles and bibliographic information in the database and utilized VOS viewer to produce mapping and graphical presentations. This study presents how DT in higher education through mobile apps has evolved between 2014 and 2024. Studies on DT in higher education saw an 86% increase in 2020, primarily due to the COVID-19 pandemic. The top journals, influential authors, and affiliated countries are identified. The United States leads in contributions to this field with 35 publications, accounting for 19% of the total, followed by China and the UK, which account for 9% and 7%, respectively. Several key research gaps are identified based on existing literature, and suggestions are made for future studies. By examining current and influential trends, this study contributes to the knowledge of digital transformation in higher education and offers insights for institutions seeking to enhance mobile-based support services. Full article

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22 pages, 648 KB

Open AccessArticle

Unpacking AI Chatbot Dependency: A Dual-Path Model of Cognitive and Affective Mechanisms

by Na Zhai, Xiaomei Ma and Xiaojun Ding

Information 2025, 16(12), 1025; https://doi.org/10.3390/info16121025 - 24 Nov 2025

Abstract

With AI chatbots becoming increasingly embedded in everyday life, growing concerns have emerged regarding users’ psychological dependency on these systems. While previous studies have mainly addressed utilitarian drivers, less attention has been paid to the cognitive and affective mechanisms driving chatbot dependency. Drawing [...] Read more.

With AI chatbots becoming increasingly embedded in everyday life, growing concerns have emerged regarding users’ psychological dependency on these systems. While previous studies have mainly addressed utilitarian drivers, less attention has been paid to the cognitive and affective mechanisms driving chatbot dependency. Drawing upon Uses and Gratifications Theory, Compensatory Internet Use Theory, and Attachment Theory, this study proposes a dual-path model that investigates how instrumental motivations (e.g., information-seeking, entertainment, efficiency) and affective motivations (e.g., companionship, loneliness, anxiety) influence chatbot dependency through two mediating mechanisms: cognitive reliance and emotional attachment. Using survey data collected from 354 participants, the model was tested through structural equation modeling (SEM). The results indicate that information-seeking and efficiency significantly predict cognitive reliance, which subsequently enhances chatbot dependency. In contrast, entertainment does not exhibit a significant influence. Furthermore, affective motivations such as companionship, loneliness, and anxiety are indirectly linked to dependency through emotional attachment, with loneliness demonstrating the strongest indirect effect. These findings underscore the dual influence of functional cognition and emotional vulnerability in fostering chatbot dependency, emphasizing the importance of emotionally sensitive and ethically responsible AI design. Full article

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