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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Yan Yang, Zhenyu Hu, Wenyue Xu, Baojie Zhang, Zhibo Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9781665410205

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP. © 2024 IEEE.

关键词： Inverse problems

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria 13

A Framework for Automated Cardiovascular Magnetic Resonance ...

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13th International Conference on computer and Knowledge Engineering, ICCKE 2023

作者： Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran The University of Manchester Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences Manchester United Kingdom

ISBN: (纸本)9798350330151

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts' quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts' opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank. © 2023 IEEE.

关键词： Quality control

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Ihoya: knowledge management system based on community using User-Centered Design (UCD) method

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2067-2074页

作者： Rafi, Muhammad Puspitodjati, Sulistyo Apriani, Niken Fitria Hakim, Shidiq Al Siagian, Al Hafiz Akbar Maulana Riyanto, Slamet Rodiah, Rodiah Kushadiani, Siti Kania Faculty of Computer Science and Information Technology Universitas Gunadarma West Java Depok Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency West Java Bogor Indonesia

Hoya represents a significant component of Indonesia’s biodiversity with considerable potential;however, its utilization remains suboptimal. There exists a keen interest among researchers, collectors, and horticultural enthusiasts to delve into this fascinating plant. The wide variety of Hoya species in Indonesia substantially challenges their identification. Consequently, there is a pressing need for a system that facilitates the identification of the Hoya plant and its various species. In this study, we propose developing a knowledge management system (KMS) to identify Hoya and its species. This proposal is anchored in the user-friendly nature of mobile-based applications, which offer accessibility at any time. To ensure the creation of a high-quality application that meets user satisfaction, a User Interface and User Experience (UI/UX) design process is implemented, employing the User Centered Design (UCD) methodology that concentrates on the preferences and requirements of users. Subsequently, the System Usability Scale (SUS) method is adopted to assess the design and verify its alignment with user needs. This research culminates in our developed KMS prototype, namely iHoya, encompassing mockup pages tailored to users’ preferences, requirements, and navigational flows. An evaluation of the prototype design using the SUS method, conducted through a survey of targeted users, categorizes the design as acceptable while indicating the necessity for certain enhancements. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Hoya Knowledge management system SUS UCD UI/UX

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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LTL Model Checking for Verification of Electronic Medical Record (EMR) Design 8th

LTL Model Checking for Verification of Electronic Medical R...

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8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023

作者： Taryana, Acep Adzkiya, Dieky Mufid, Muhammad Syifa’ul Mukhlash, Imam Abate, Alessandro Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Engineering Faculty Universitas Jenderal Soedirman Purwokerto Indonesia Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9789819721351

This paper presents an in-depth investigation into the verification of a software design model, focusing on the context of Electronic Medical Record (EMR) systems. Verification plays a critical role in ensuring the correctness and reliability of such systems, making it a topic of paramount importance. In this study, we undertake the task of remodeling the EMR requirements using a finite transition system to facilitate rigorous analysis and verification. Additionally, we express the formal specification through Linear Temporal Logic (LTL), leveraging its expressive power and ability to capture temporal properties accurately. To assess the validity of the design model, we employ the widely recognized SPIN model checker, a tool known for its effectiveness in detecting errors and inconsistencies in software systems. Our analysis reveals several critical findings. Specifically, the model checker successfully identifies paths within the design model that fail to satisfy the specified LTL formula, shedding light on potential areas of concern. Moreover, the generation of a comprehensive counterexample provides valuable insights for further analysis and improvement of the software design. Overall, this research highlights the significance of verification techniques in software design and emphasizes the applicability of the finite transition system and LTL in this context. The findings underscore the importance of thorough verification and the potential benefits of employing tools like SPIN to enhance the reliability and robustness of EMR systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

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