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Identifying key nodes for enhancing community stability in bipartite networks

World Wide Web 2025年第3期28卷 1-26页

作者： Chen, Jinyi Choudhury, Farhana Xin, Junchang Wang, Zhiqiong School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Information Systems The University of Melbourne Melbourne3053 Australia College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

Bipartite networks are widely applied to model relationships between two distinct types of entities in various real-world applications. The departure of key nodes can trigger a cascading effect, potentially leading to the collapse of the entire community. Identifying such key nodes to enhance community stability is crucial, a topic well-explored in unipartite networks but largely unexplored in bipartite networks. In this paper, we aim to identify key nodes whose removal results in the smallest (α,β)-core in bipartite networks, and protecting these nodes from being removed can greatly enhance community stability. Formally, given a bipartite graph G with degree constraints α and β, and budgets b1 and b2, our goal is to identify b1 upper and b2 lower vertices (collapsers) whose removal maximizes the number of non-collapsed vertices (followers) cascading out of the (α,β)-core. We prove the problem is NP-hard and propose a greedy algorithm that identifies the best collapser in each of the b1+b2 iterations. Several well-designed pruning strategies are employed to reduce the pool of candidate collapsers and expedite follower computation. Theoretical analysis and extensive empirical evaluations on 12 real-world datasets demonstrate the efficiency and effectiveness of our proposed algorithms. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： NP-hard

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uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

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IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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A Trust-Based Computation Offloading Framework in Mobile Cloud-Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5370-5385页

作者： Wang, Jianhui Li, Zhetao Liu, Haolin Qiu, Tie Luo, Hongbin Jinan University College of Information Science and Technology Guangzhou510632 China Xiangtan University School of Computer Science Xiangtan411105 China Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Beihang University School of Computer Science and Engineering Beijing100191 China

Cloud service centers (CSCs) can purchase edge computation resources to improve service quality in mobile cloud-edge computing networks. However, edge servers (ESs) are owned by different entities, and dishonest entities may launch computational forgery attacks, i.e., the ES falsely reports its idle computation resources to win more tasks for increased revenue. Most existing approaches ignore the threat of dishonest ESs. To address the challenges, we design a Trust-based Computation Offloading (TCO) framework. First, we construct the problem for minimizing the difference between the CSC's cost and the expected revenue (DCER), which is a mixed-integer nonlinear programming problem. Second, we develop a trust-based computation offloading method that quickly finds a good solution by decomposing the problem. Finally, a two-tier trust evaluation method was proposed to obtain accurate trust values. Experimental results indicate that TCO's comprehensive performance surpasses the benchmarks and significantly enhances computation offloading reliability with a lower performance loss. Notably, tasks are preferentially offloaded to honest ESs to ensure their revenue and promote ESs’ honesty under the TCO framework. Additionally, compared with no trust mechanisms, TCO reduces the service timeout count in an interval by 34.37% - 73.80% with a performance loss of only 1.42% - 4.10%. © 2002-2012 IEEE.

关键词： Cloud platforms

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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UltraADV: An Unsupervised Deep Learning Lightweight Framework for Anomaly Detection in V2X

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IEEE Internet of Things Journal 2025年第9期12卷 12735-12747页

作者： Huang, Weixian Xu, Hongyun Gong, Yujing Liu, Zhikang Li, Feng Lin, Zhihui Hu, Bin-Jie South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China

Designing anomaly detection systems for vehicle-to-everything (V2X) is a challenge. Deep learning has shown strong advantages in anomaly detection. However, labeling anomalies is often difficult and expensive, and deep neural networks often prioritize performances by incorporating a large number of parameters. Therefore, there is an urgent need for a lightweight anomaly detection framework which can be built with unsupervised learning methods. In this article, we propose a framework, the UltraADV, which is suitable for anomaly detection systems in V2X. In the framework, the autoencoder facilitates an unsupervised setting, while the knowledge distillation transfers the knowledge of a larger network to a smaller network. Ultimately, the compressed model can be deployed onto the resource-constrained devices for real-world applications in V2X. We evaluate the performances of multiple architectures of UltraADV on two publicly available datasets. Our best performing architecture demonstrably surpasses the benchmark in terms of performance metrics, parameter efficiency, and prediction speed. © 2014 IEEE.

关键词： Contrastive Learning

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Machine Learning-Based Diagnosis of Defects in 2.5D and 3D Interconnects

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年第5期15卷 1104-1116页

作者： Li, Junming Liang, Huaguo Lv, Hao Pan, Yuqi Huang, Zhengfeng Chen, Tian Lu, Yingchun Hefei University of Technology School of Microelectronics Hefei230009 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Diagnosing interconnect line defects becomes increasingly challenging in advanced Chiplet integration due to the immaturity of fabrication processes, reduced interconnect spacing, and increased density. In this paper, a non-destructive interconnect defect diagnosis method is proposed. Firstly, redistribution layers (RDL) on the 2.5D silicon interposer and 3D through silicon via-redistribution layer (TSV-RDL) interconnect channels are simulated by ANSYS HFSS, with open and short defects injected at various positions to analyze signal integrity;utilizing S parameters and group delay time as feature vectors, machine learning algorithms are employed specifically for the classification and identification of defects. For defect localization, a novel high-precision scheme driven by adaptive regression and clustering is proposed, achieving highly accurate and reliable localization of defects. The results show that the algorithm can accurately identify open and short defects. In defect localization, the Mean Relative Error (MRE) of the proposed method is less than 8%, and the Maximum Relative Error (MaxRE) does not exceed 13%. Compared with the related algorithms, the localization accuracy is significantly improved, providing a novel perspective for the identification and localization of defects within package interconnect lines. © 2025 IEEE. All rights reserved.

关键词： Group delay

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Spectrum-Energy Efficient Sink-AAV-LEO Backbone Networks to Ensure Ubiquitous Data Collection Timeliness

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IEEE Internet of Things Journal 2025年第9期12卷 12761-12776页

作者： Liu, Hanjian Gui, Jinsong Central South University School of Computer Science and Engineering Changsha410083 China Central South University School of Electronic Information Changsha410075 China

The rise of low Earth orbit (LEO) satellites and autonomous aerial vehicles (AAVs) has facilitated the integration of space-air–terrestrial networks, enabling ubiquitous data collection in Internet of Remote Things (IoRT) networks. However, limited battery capacity of sinks/AAVs and scarce spectrum in these networks make it challenging to build spectrum-energy efficient backbone networks for timely data collection. In this article, we explore the resource allocation problem in Sink-AAV-LEO-based data collection scenarios, where sink nodes gather data from sensors, and AAVs relay it to LEO satellites. To ensure data collection timeliness while optimizing energy and spectrum use, we focus on optimizing the three-hop network structure, power control for sink nodes, path selection and power control for AAVs, and bandwidth allocation for AAV-LEO links. Although these tasks should theoretically be modeled as a global optimization problem, solving them within tolerable time complexity is difficult. Therefore, we first establish a three-hop network using our proposed optimization mechanism. Then, we describe the Sink-AAV and AAV-LEO resource allocation problems, and propose the corresponding solutions via utilizing our improved deep reinforcement learning algorithm, namely ISS-proximal policy optimization. Finally, we solve the global problem based on the solutions to the above two local problems. Besides, our simulation results are compared with various benchmark schemes, validating the effectiveness and superiority of our proposed solutions across different perspectives. © 2014 IEEE.

关键词： Global optimization

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Multi-parameter ultrasound imaging for musculoskeletal tissues based on a physics informed generative adversarial network

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Chinese Physics B 2025年第4期34卷 442-455页

作者： Pengxin Wang Heyu Ma Tianyu Liu Chengcheng Liu Dan Li Dean Ta Institute of Biomedical Engineering&Technology Academy for Engineering and TechnologyFudan UniversityShanghai 200433China State Key Laboratory of Integrated Chips and Systems Fudan UniversityShanghai 201203China Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China Department of Biomedical Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China

Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal ***,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging *** inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping *** learning methods have been applied in musculoskeletal imaging,but need a large amount of data for *** by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and *** the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue *** results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best *** specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.

关键词： ultrasound image physics informed generative adversarial network musculoskeletal imaging

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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