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arXiv 2025年

作者： Sun, Geng Yuan, Minghua Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Han, Zhu Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Engineering Jilin University Changchun130012 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Md Nurul Absur Sourya Saha Sifat Nawrin Nova Kazi Fahim Ahmad Nasif Md Rahat Ul Nasib Department of Computer Science City University of New York New York USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia USA Samsung Austin Research Center Austin Texas USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

关键词： Content distribution networks Heuristic algorithms Scalability computer architecture Dynamic scheduling Fabrics Performance analysis Servers Resource management Optimization

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Hyperrectangle Embedding for Debiased 3D Scene Graph Prediction From RGB Sequences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Feng, Mingtao Yan, Chenbo Wu, Zijie Dong, Weisheng Wang, Yaonan Mian, Ajmal Xidian University School of Artificial Intelligence Xi'an710071 China Hunan University College of Electrical and Information Engineering Changsha410082 China The University of Western Australia Department of Computer Science and Software Engineering Crawley PerthWA6009 Australia

3D scene graph has emerged as a powerful high-level representation of the environment and is regarded as a prerequisite for long-term autonomous robotic operations. A practical research problem here is to predict the 3D scene graph from sequentially captured data. However, existing methods neglect the polysemy of semantic roles that coarse feature vectors are insufficient to represent entities in different relationship semantics. This extremely limits their capability to predict relationships. We propose an approach to tackle the aforementioned challenge by introducing a novel representation, the hyperrectangle embedding, which represents entity using distinctive geometry for more effective scene understanding, rather than learning within vector-based feature with blindly increasing dimensions. By incorporating an entity within two affine-transformed embeddings, each representing either the subject or object and characterized by separate learnable transformations, we achieve the polysemy of semantic roles. The intersections of affine-transformed hyperrectangle embeddings represent the bidirectional relationship between two entities. We identify bias and reliability as two challenges impeding the model learning process. In response to the bias, that arises from long-tailed distributions in the data, we propose a history-guided debiasing strategy that utilizes a confusion history block comprised of previous hyperrectangle embeddings. This strategy mitigates inherent biases by extracting pertinent information and facilitating knowledge transfer from dominant categories to rare ones. To enhance the reliability of predictions, we introduce predictive uncertainty into the 3D scene graph prediction task. We develop a post-hoc reliability enhancement strategy to identify potentially unreliable predictions and subsequently enhance the model's predictive accuracy. Extensive experiments on the 3DSSG dataset show the effectiveness of the proposed method in this challenging task

关键词： Graph embeddings

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration

arXiv

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arXiv 2025年

作者： Xu, Yisen Lin, Feng Yang, Jinqiu Chen, Tse-Hsun Tsantalis, Nikolaos SPEAR Lab Concordia University Montreal Canada O-RISA Lab Concordia University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Maintaining and scaling software systems relies heavily on effective code refactoring, yet this process remains labor-intensive, requiring developers to carefully analyze existing codebases and prevent the introduction of new defects. Although recent advancements have leveraged Large Language Models (LLMs) to automate refactoring tasks, current solutions are constrained in scope and lack mechanisms to guarantee code compilability and successful test execution. In this work, we introduce MANTRA, a comprehensive LLM agent-based framework that automates method-level refactoring. MANTRA integrates Context-Aware Retrieval-Augmented Generation, coordinated Multi-Agent Collaboration, and Verbal Reinforcement Learning to emulate human decision-making during refactoring while preserving code correctness and readability. Our empirical study, conducted on 703 instances of "pure refactorings" (i.e., code changes exclusively involving structural improvements), drawn from 10 representative Java projects, covers the six most prevalent refactoring operations. Experimental results demonstrate that MANTRA substantially surpasses a baseline LLM model (RawGPT), achieving an 82.8% success rate (582/703) in producing code that compiles and passes all tests, compared to just 8.7% (61/703) with RawGPT. Moreover, in comparison to IntelliJ’s LLM-powered refactoring tool (EM-Assist), MANTRA exhibits a 50% improvement in generating Extract Method transformations. A usability study involving 37 professional developers further shows that refactorings performed by MANTRAare perceived to be as readable and reusable as human-written code, and in certain cases, even more favorable. These results highlight the practical advantages of MANTRA and emphasize the growing potential of LLM-based systems in advancing the automation of software refactoring tasks. © 2025, CC BY.

关键词： Decision making

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A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM

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Intelligence-Based Medicine 2025年 11卷 100206-100206页

作者： Naveed, Sajid Husnain, Mujtaba Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects. © 2025 The Authors

关键词： Drug response recommendation system Drug SMILES Gene expression Glioblastoma K-mer fragmentation LightGBM

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Revisiting Kernel Attention with Correlated Gaussian Process Representation

arXiv

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arXiv 2025年

作者： Bui, Long Minh Huu, Tho Tran Dinh, Duy Nguyen, Tan Minh Hoang, Trong Nghia FPT Software AI Center Viet Nam Department of Mathematics National University of Singapore Singapore School of Electrical Engineering and Computer Science Washington State University United States

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks. The code for our experiments is available at https://***/MinhLong210/CGP-Transformers. Copyright © 2025, The Authors. All rights reserved.

关键词： Global positioning system

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Analysis and Development of Kids’ Cell Activities Monitoring App 1st

Analysis and Development of Kids’ Cell Activities Monitorin...

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

作者： Tehreem, Umme Iqbal, Muhammad Waseem Hamid, Khalid Saeed, Muhammad Mohsin Department CS&IT Superior University Lahore Lahore54000 Pakistan Department of Software Engineering Superior University Lahore54000 Pakistan Department of Computer Science Superior University Lahore Lahore54000 Pakistan

ISBN: (纸本)9783031776168

Due to the COVID-19 lockdown, everything did go online so online education and business tools got very useful and vital, although those tools need improvements and innovation. The use of online applications for the educational purpose made it easy to keep things going for learning, smoothly. But the excessive use of technology for educational purposes comes up with many issues, particularly the negative use of technology instead of learning and a lot of bad habits as well. To counter this issue parents, need to digitally track and monitor the activities of their children. A lot of monitoring applications are available but the desired vigilance is not possible with the available applications. That’s why in this research a Kids Monitoring App is designed and developed with all the features that make it happen for the parents to efficiently monitor all the activities of their children without disturbing any educational operation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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iCTS: Iterative and Hierarchical Clock Tree Synthesis With Skew-Latency-Load Tree

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Weiguo Huang, Zhipeng Yu, Bei Zhu, Wenxing Chen, Jian He, Zhixue Li, Xingquan South China University of Technology School of Software Engineering Guangzhou China Pengcheng Laboratory Shenzhen China Beijing Institute of Open Source Chip Beijing China Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Fuzhou University Center for Discrete Mathematics and Theoretical Computer Science Fuzhou China

The advancement of modern clock tree synthesis (CTS) encounters a bottleneck, primarily due to the difficulty in achieving multi-objective co-optimization among complex design processes. To concurrently optimize skew, latency, and load capacitance, we propose an iterative and hierarchical CTS framework, which is composed of clustering, topology generation and routing, buffering, and optimization. First, we introduce a capacitance-based metric to achieve adaptive balanced clustering and optimize the cluster results through simulated annealing. Second, to construct a clock tree with lower latency, load capacitance, and skew, we introduce the skew-latency-load tree (SLLT), which combines the advantages of bound skew tree and Steiner shallow-light tree, and we propose an effective SLLT construction algorithm. Third, to further optimize CTS result by buffering, we introduce the critical wirelength evaluation (CWE) to evaluate the capability of each buffer, and propose the insertion delay estimation (IDE) to reduce the evaluation bias during buffering, then design the iterative skew convergence algorithm (ISCA) to achieve complete convergence of skew. We validate our solution using 28nm process technology. Compared to our method, the commercial tool increases skew, latency, and clock capacitance by 39.5%, 13.0%, and 18.5%, respectively, while the OpenROAD by 101.6%, 50.7%, and 25.5%, respectively. © 1982-2012 IEEE.

关键词： Simulated annealing

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