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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

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arXiv 2025年

作者： Nasiruzzaman, Md. Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. School of Computing & Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David Wales United Kingdom School of Computing Faculty of Arts Science & Technology Wrexham University United Kingdom Faculty of Computing Engineering & Science University of South Wales United Kingdom

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Machine learning

Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges

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Computers in Biology and Medicine 2025年 189卷 109834-109834页

作者： Ibrahim, Mahmoud Khalil, Yasmina Al Amirrajab, Sina Sun, Chang Breeuwer, Marcel Pluim, Josien Elen, Bart Ertaylan, Gökhan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands VITO Belgium Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered in previous reviews. The survey also emphasizes the aspect of conditional generation, which is not focused on in similar work. Key contributions include a broad, multi-modality scope that identifies cross-modality insights and opportunities unavailable in single-modality surveys. While core generative techniques are transferable, we find that synthesis methods often lack sufficient integration of patient-specific context, clinical knowledge, and modality-specific requirements tailored to the unique characteristics of medical data. Conditional models leveraging textual conditioning and multimodal synthesis remain underexplored but offer promising directions for innovation. Our findings are structured around three themes: (1) Synthesis applications, highlighting clinically valid synthesis applications and significant gaps in using synthetic data beyond augmentation, such as for validation and evaluation;(2) Generation techniques, identifying gaps in personalization and cross-modality innovation;and (3) Evalu

关键词： Generative adversarial networks

Topology Decoupled All-reduce Algorithm

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Topology Decoupled All-reduce Algorithm

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruixing Zong Jiapeng Zhang Zhuo Tang Anwitaman Datta College of Computer Science and Electronic Engineering Hunan University China College of Computing and Data Science Nanyang Technological University Singapore Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of deep learning, network communication has become the most critical factor in model training. Especially, the all-reduce operation can comprise over 70% of the cumulative training duration as a pivotal component within data parallelism. However, existing all-reduce algorithms often perform poorly in complex network topologies, and there is currently no universal and straightforward all-reduce algorithm that can effectively adapt to diverse topological *** this work, we propose a topology decoupled all-reduce algorithm. We decouple the network into multiple tree substructures, select the trees with the smallest heights, and then split the data to perform aggregate communication within these selected structures. This approach significantly reduces the number of communications and enhances efficiency. Experimental results show that our topology decoupled all-reduce algorithm reduces communication time compared to NCCL’s by 42.9% and enhances end-to-end training efficiency by 11.7%.

关键词： Training Deep learning Adaptation models Signal processing algorithms Complex networks Signal processing Parallel processing data aggregation Topology Speech processing

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models

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arXiv 2025年

作者： Awad, Alaa Aly, Salah A. CS & IT Department E-Japan University of Science and Tech. Alexandria Egypt Faculty of Computing and Data Science Badya University Giza Egypt CS & Math Section Faculty of Science Fayoum University Fayoum Egypt

Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the question of their applicability and reliability remains unresolved. This study aims to utilize image processing and deep learning methodologies to achieve state-of-the-art results for the detection of Acute Lymphoblastic Leukemia (ALL) using data that best represents real-world scenarios. ALL is one of several types of blood cancer, and it is an aggressive form of leukemia. In this investigation, we examine the most recent advancements in ALL detection, as well as the latest iteration of the YOLO series and its performance. We address the question of whether white blood cells are malignant or benign. Additionally, the proposed models can identify different ALL stages, including early stages. Furthermore, these models can detect hematogones despite their frequent misclassification as ALL. By utilizing advanced deep learning models, namely, YOLOv8, YOLOv11, ResNet50 and Inception-ResNet-v2, the study achieves accuracy rates as high as 99.7%, demonstrating the effectiveness of these algorithms across multiple datasets and various real-world situations. © 2025, CC0.

关键词： Lung cancer

Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

An Evaluation of Security Features for Preventing Illegal Distribution of Digital Content Using Quantitative Methods

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An Evaluation of Security Features for Preventing Illegal Di...

computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Muhammad Isma Farhan Bin Mohd Razak Mazlina Abdul Majid Husnul Ajra Md. Shohidul Islam Aziman Abdullah Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Kuantan Malaysia Faculty of Computing Centre of Excellence for Artificial Intelligent & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Kuantan Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In the current digital landscape, safeguarding digital content from unauthorized sharing is of paramount importance. This study concerns three key issues: the prevalent unauthorized sharing of content, user dissatisfaction with existing security measures, and the lack of alignment between expert opinions and user perceptions. This research addresses three critical problem statements. Firstly, it seeks to identify the current security features utilized to prevent the unauthorized distribution of digital content. Secondly, it aims to assess user perceptions of these features through a meticulously constructed survey; and lastly, the study endeavors to compare findings from a thorough literature review with user perceptions. Methodologically, this research encompasses an exhaustive literature review and a user perception survey to gain insights into effective security measures and recommendations for enhancement. The expected outcomes include a more profound understanding of the effectiveness of current security measures and suggestions for enhancing digital content protection. Through this study, it has been emphasized the significance of digital content security and put forward collaborative avenues for improvement.

关键词： Surveys Industries Current measurement Gain measurement Market research Security Protection Information technology Research and development Systematic literature review

FocusDD: Real-World Scene Infusion for Robust dataset Distillation

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arXiv 2025年

作者： Hu, Youbing Cheng, Yun Saukh, Olga Ozdemir, Firat Lu, Anqi Cao, Zhiqiang Li, Zhijun Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland Graz University of Technology Austria Complexity Science Hub Vienna Austria

dataset distillation has emerged as a strategy to compress real-world datasets for efficient training. However, it struggles with large-scale and high-resolution datasets, limiting its practicality. This paper introduces a novel resolution-independent dataset distillation method Focused dataset Distillation (FocusDD), which achieves diversity and realism in distilled data by identifying key information patches, thereby ensuring the generalization capability of the distilled dataset across different network architectures. Specifically, FocusDD leverages a pre-trained Vision Transformer (ViT) to extract key image patches, which are then synthesized into a single distilled image. These distilled images, which capture multiple targets, are suitable not only for classification tasks but also for dense tasks such as object detection. To further improve the generalization of the distilled dataset, each synthesized image is augmented with a downsampled view of the original image. Experimental results on the ImageNet-1K dataset demonstrate that, with 100 images per class (IPC), ResNet50 and MobileNet-v2 achieve validation accuracies of 71.0% and 62.6%, respectively, outperforming state-of-the-art methods by 2.8% and 4.7%. Notably, FocusDD is the first method to use distilled datasets for object detection tasks. On the COCO2017 dataset, with an IPC of 50, YOLOv11n and YOLOv11s achieve 24.4% and 32.1% mAP, respectively, further validating the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词：

SAC-ViT: Semantic-Aware Clustering Vision Transformer with Early Exit

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arXiv 2025年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Wei, Dawei Li, Zhijun Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in global modeling but faces deployment challenges on resource-constrained devices due to the quadratic computational complexity of its attention mechanism. To address this, we propose the Semantic-Aware Clustering Vision Transformer (SAC-ViT), a non-iterative approach to enhance ViT’s computational efficiency. SAC-ViT operates in two stages: Early Exit (EE) and Semantic-Aware Clustering (SAC). In the EE stage, downsampled input images are processed to extract global semantic information and generate initial inference results. If these results do not meet the EE termination criteria, the information is clustered into target and non-target tokens. In the SAC stage, target tokens are mapped back to the original image, cropped, and embedded. These target tokens are then combined with reused non-target tokens from the EE stage, and the attention mechanism is applied within each cluster. This two-stage design, with end-to-end optimization, reduces spatial redundancy and enhances computational efficiency, significantly boosting overall ViT performance. Extensive experiments demonstrate the efficacy of SAC-ViT, reducing 62% of the FLOPs of DeiT and achieving 1.98× throughput without compromising performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis