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Enhanced multi-objective scheduling for heterogeneous computing platforms using hybrid MOEAD with SA and TS-guided initialization strategies

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Journal of Supercomputing 2025年第7期81卷 1-62页

作者： Saad, Abla Abdel-Raouf, Osama Hadhoud, Mohy Kafafy, Ahmed Machine Intelligence Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt Information Technology Department Faculty of Computers & Information Menoufia University Menoufia Shibīn al-Kawm Egypt Data Science Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt

Rapid growth in data and computational demands has driven the widespread adoption of heterogeneous computing systems that combine different processing units, such as CPUs, GPUs, and FPGAs. Efficient scheduling of tasks in these systems is essential to optimize resource utilization, reduce power consumption, and meet performance requirements. However, the complexities of managing multiple tasks, diverse machine architectures, and conflicting objectives make scheduling in these environments an NP-hard problem. This study reformulates the traditional scheduling model as a multi-objective optimization problem focusing on minimizing makespan and maximizing parallelization across heterogeneous systems. Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) have effectively solved these complex scheduling challenges. MOEA/D offers a flexible and powerful approach for handling multi-objective scheduling tasks in a heterogeneous computing environment. This paper explores how MOEA/D can be combined with other metaheuristics to further improve task scheduling and balance competing performance objectives. The effectiveness of the approach is demonstrated through 28 validation scenarios. The algorithm’s performance is evaluated using four scientific workflows: structured as (FFT and Montage) and unstructured as (Molecular-A and Molecular-B). Performance evaluation is performed using appropriate metrics, specifically hypervolume and inverted generational distance plus, to assess the quality of the solutions. Experimental results demonstrate that the GRASP-MOEA/D hybrid approach outperforms all competitors in structured workflows, achieving the best schedule by minimizing makespan and maximizing resource utilization within a reasonable runtime. For unstructured workflows, GSA-MOEA/D achieves the best performance in 75% of cases, while SA-MOEA/D is the best in 37.5% of cases. However, GSA-MOEA/D and SA-MOEA/D face significant computational overhead as workflow size

关键词： Multiobjective optimization

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Analysis of Device-To-Device Communication in Iot System 1st

Analysis of Device-To-Device Communication in Iot System

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nawaz, Usman Rehman, Faisal Ullah, Kainat Azmat Raza, Usman Ahmed Sharif, Hanan Rafique, Furqan Ali, Abid Ashiq, Masood Iqbal, Junaid Department of Computer Science Lahore Leads University Lahore Pakistan Department of Statistics and Data Science University of Mianwali Mianwali Pakistan Faculty of Information Technology University of Central Punjab Lahore Pakistan

ISBN: (纸本)9783031776199

Peer-to-Peer communication is based on an important feature of communication which is Device-to-Device (D2D) communication. IoT is a computational device that is equipped with accurate identifiers and the capacity to speak data across a network without the assistance of a human. D2D communication is referred to as "Direct verbal exchange among the taking part gadgets without the use of the centralized infrastructure". This research study examines the advantages and barriers of D2D communication as a means of meeting the IoT's requirements. Many companies and quality management organizations have demonstrated a strong interest in implementing the D2D approach in wireless networks. The thought of working without the necessary control of centralized supervision and making Wi-Fi networks wider as well as energy saving can be made possible with D2D communication in IoT. These devices’ technical limitations and the connection's support limitations provide an upward push to numerous problems that must be addressed. Some of the IoT studies Electricity production, connectivity, security, scenario technologies, and so forth are all issues. We cognizance’s on troubles that in the IoT there is an influence in D2D communication surroundings. We also discovered some important stumbling blocks to the success of smart D2D speech communication in the Internet of Things. We talked about how to deal with these difficult situations. This research explains the idea of a tool to tool verbal exchange and IoT system and defines its predominant techniques and applications imparting a subsequent era therefore as procedure strategy to the demanding situations. To boost research and development in the domains IoT problems have been studied. Every cited research work's contribution and limitations are discussed as well as any potential problem statement and open rely. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wi-Fi

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APPLYING TEXT MINING TO ANALYZE HUMAN QUESTION ASKING IN CREATIVITY RESEARCH

arXiv

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arXiv 2025年

作者： Wróblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Sciences Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research. Copyright © 2025, The Authors. All rights reserved.

关键词： Text mining

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

arXiv

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arXiv 2025年

作者： Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Alsheikh, Mohammad Abu Kuhn, Carlos C.N. Alem, Yibeltal F. Radwan, Ibrahim School of Electrical Data Engineering University of Technology SydneyNSW2007 Australia School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia Faculty of Science & Technology University of Canberra CanberraACT2617 Australia

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 37% for the reconstructed human poses compared to the baseline. © 2025, CC BY.

关键词： Mean square error

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Thai voice language encryption and decryption using 3DES and AES algorithm for communication

Thai voice language encryption and decryption using 3DES and...

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International Electrical Engineering Congress (iEECON)

作者： Naruemon Dobay Mongkhon Thakong Krit Somkantha Kritsanapong Somsuk Department of Data Science and Information Technology Faculty of Science Udon Thani Rajabhat University Thailand Graduate School Udon Thani Rajabhat University Udon Thani Thailnad General Education Udon Thani Rajabhat University Thailand Faculty of Science Udon Thani Rajabhat University Thailand Faculty of Technology and Engineering Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The aim of this paper is to compare the performance of the encryption and decryption of Thai voice language by using Triple data Encryption Standard (3DES) and Advanced Encryption Standard (AES) algorithms for secure communication. The research methodology involves collecting Thai voice samples, converting speech to text by using Google’s API to be applied with cryptography algorithms. Performance comparison between 3DES and AES focuses on accuracy and processing time. Experimental results demonstrate 100% accuracy in preserving original text content for both algorithms. However, AES consistently outperforms 3DES in processing speed, particularly for larger data sizes. The study reveals a linear relationship between input size and processing time for both algorithms. Furthermore, the findings imply that AES offers superior performance in terms of speed and security for encrypting Thai voice data, making it more suitable for secure communication applications. Therefore, this research contributes to improve data security in Thai voice communications by providing empirical evidence on the efficacy of modern encryption standards.

关键词： Electrical engineering Three-dimensional displays Accuracy Encryption Text to speech Standards Speech to text Automatic speech recognition

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Optimizing hyperparameters for dual-attention network in lung segmentation

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Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

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