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Optimizing Mobile Cloud computing: A Comparative Analysis and Innovative Cost-Efficient Partitioning Model

SN computer science 2025年第1期6卷 1-25页

作者： Ali, Mushtaq Muhammad, Dost Khalaf, Osamah Ibrahim Habib, Raja Faculty of Computing Riphah International University Islamabad Pakistan CRT-AI and ADAPT Research Centers School of Computer Science University of Galway Galway Ireland Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Iraq Department of Computing Shifa Tameer-e-Millat University Islamabad Pakistan

Mobile cloud computing (MCC) integrates mobile devices with cloud computing to enhance performance and provide scalable services. It enables the partitioning of computationally intensive mobile applications, utilizing the cloud resources for remote execution. This process is vital for optimizing resource utilization and energy efficiency in distributed computing environments. Initially, static application partitioning was developed, dividing system resources into fixed-sized partitions. Though it is simple and reduces operational overhead, it lacks adaptability to dynamic computing environments due to mobility. Modern frameworks use runtime partitioning, dynamically allocating resources based on runtime profiling, which increases computational overhead and energy consumption. Despite various studies on MCC, there is a notable oversight in numerical analysis of application partitioning. This paper presents a comparative numerical study of existing partitioning techniques and proposes a Cost-Efficient Partitioning (CEP) model. The CEP model combines the static nature to reduce computational overhead with the dynamic nature to address runtime challenges. This research addresses the shortcomings of existing approaches, potentially transforming computational offloading into energy-saving solutions. Our results show that the execution time (ET) of a task in the CEP model is 0.826 s, which is longer than the ET of static partitioning however shorter than the 0.962 s of dynamic partitioning models. Similarly, the CEP's energy consumption in a test scenario is 0.609 J, slightly higher than the 0.587 J of static partitioning but lower than the 0.711 J of dynamic partitioning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational offloading Dynamic partitioning Energy consumption Execution cost Mobile computing Remote execution Static partitioning

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Automatic APT Attack Reconstruction Supporting Lateral Movement 2nd

Automatic APT Attack Reconstruction Supporting Lateral Movem...

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2nd International Conference on Security and Information Technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Fan, Chun-I Wu, Wei-Chen Shie, Cheng-Han Kuo, Hsin-Nan Lee, Bo-Yi Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan National Center for High-Performance Computing Hsinchu Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9789819777853

This work proposes a framework for generating datasets that allows users to adjust the APT attack techniques within it. The framework utilizes the MITRE ATT&CK framework to label the attack traffic based on the Tactics, Techniques, and Procedures (TTP), which facilitating researchers in using the datasets for model training. Meanwhile, this work uses the framework to reconstruct the APT29 attack process and collect a dataset to validate the framework’s feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Advanced Persistent Threat (APT) Dataset generation The APT29 attack Threat intelligence

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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing For Fraud Detection In Payment Systems (February 2025)

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IEEE Access 2025年 13卷 92036-92056页

作者： Lawati, Hadi M. R. Al Zainal, Anazida Al-Rimy, Bander Ali Saleh Al-Azawi, Mohammad Kassim, Mohamad Nizam Almalki, Sultan Ahmed Alghamdi, Tami Abdulrahman Faculty of Computing Johor Bahru Malaysia Anti-Financial Crime Lab Faculty of Computing Johor Bahru Malaysia University of Portsmouth PAIDS Research Center School of Computing Buckingham Building Lion Terrace PortsmouthPO1 3HE United Kingdom Gulf College Muscat Oman Putrajaya Malaysia Najran University Applied College Computer Department Najran66462 Saudi Arabia Al-Baha University Faculty of Computing and Information Computer Science Department Al-Baha65779 Saudi Arabia

As fraudulent transaction methods evolve rapidly;it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent behaviors, resulting in lower detection accuracy and a higher false positive rate. This study proposes a complete framework that brings together advanced data preprocessing, effective drift detection, and a reliable detection model to address these issues. The method uses Mutual Information and SelectKBest for selecting important features, applies ADASYN to handle class imbalance, and adopts Convolutional Neural Networks (CNN) to capture complex transaction patterns. By implementing Early Drift Detection Method (EDDM) and ADaptive WINdowing (ADWIN), the drift can be detected in advance and the system can respond to changes, both gradual and *** framework was evaluated on three datasets, including real-world transactions and mixed-data environments, achieving superior accuracy, precision, and drift detection rates, with values up to 99.99% accuracy and 1.0 respectively. The findings show that the framework can adjust to changing patterns of fraud, reduce false positives, and enhance detection performance. These insights demonstrate the significance of dynamic preprocessing and drift-aware approaches in the context of real-time fraud detection. This also serves as a basis for future work in adaptive fraud detection model research areas such as of integrating online learning for improved speed and efficiency in high-frequency transactional environments. © 2013 IEEE.

关键词： Feature Selection

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Multi-Classification Refactoring Framework Using Hopfield Neural Network for Sustainable Software Development

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IEEE Access 2025年 13卷 31785-31808页

作者： Almogahed, Abdullah Mahdin, Hairulnizam Hyeon Gu, Yeong Al-Masni, Mohammed A. Alzaeemi, Shehab Abdulhabib Omar, Mazni Alawadhi, Abdulwadood Obaid Barraood, Samera Rehman Gilal, Abdul Ameen Bakather, Adnan Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Parit Raja Johor86400 Malaysia Sejong University College of AI Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center in Finance and Digital Economy Business School Dhahran31261 Saudi Arabia Universiti Utara Malaysia School of Computing Sintok06010 Malaysia Hadhramout University College of Computers and Information Technology Department of Computer Science Hadhramaut Yemen Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL33199 United States

Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time. © 2025 IEEE.

关键词： Hopfield neural networks

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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

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computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

arXiv

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arXiv 2025年

作者： Jensen, Simon B. Oehmcke, Stefan Møgelmose, Andreas Madadi, Meysam Igel, Christian Escalera, Sergio Moeslund, Thomas B. Visual Analysis and Perception Laboratory Aalborg University Denmark Pioneer Centre for Artificial Intelligence Denmark Department of Computer Science Copenhagen University Denmark Institute for Visual & Analytic Computing Rostock University Germany University of Barcelona and Computer Vision Center Spain

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment. © 2025, CC BY.

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Efficient and Distributed Computation of Electron Repulsion Integrals on AMD AI Engines

Efficient and Distributed Computation of Electron Repulsion ...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Johannes Menzel Christian Plessl Department of Computer Science Paderborn Center for Parallel Computing Paderborn University Paderborn Germany

ISBN: (数字)9798331502812

ISBN: (纸本)9798331502829

computing electron repulsion integrals (ERIs) is the major computational bottleneck of many quantum mechanical simulation methods, requiring trillions of ERI evaluations per time step. While the computation of independent ERIs is embarrassingly parallel, the efficient computation of individual ERIs on modern processor cores is difficult due to both an insufficient cache size for intermediates of the computation and irregular memory access patterns that are difficult to vectorize. In this paper, we present how our implementation on the AI Engine (AIE) architecture addresses both of these problems. First, we have defined a flexible graph structure, which we call an ERI-Engine, that can be implemented for all 231 canonical ERI quartets from {ss|ss} to {hh| hh} by distributing the computation over 2–14 AIEs. Second, for the larger quartets, we have devised a novel vectorization scheme that leverages the advanced floating-point unit of the AIEs, while also supporting vectorization of independent ERIs for the smaller quartets. Finally, ERI-Engines are horizontally and vertically stackable to fill the entire AIE array, and in particular, the vertically stacked ERI-Engines form a column that uses one or more time-shared channels to stream the results out of the AIE array, almost completely hiding the computational phases of individual ERI-Engines. In terms of absolute performance, we are competitive with recent high-performance implementations of ERI algorithms on FPGAs (SERI) and GPUs (LibintX), as well as well-established highly optimized CPU libraries (Libint, Libcint), while being the unequivocal leader in terms of energy efficiency.

关键词： Phased arrays Quantum computing Memory management Quantum mechanics Libraries Computational efficiency Artificial intelligence Engines Field programmable gate arrays Electrons

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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MatrikalinDiabetes: User-centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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