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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Li, Zheyuan He, Lijun Wang, Juncheng Jia, Ziye Wang, Yanting Yuen, Chau School of Software Northwestern Polytechnical University China School of Information and Control Engineering China University of Mining and Technology China Hong Kong Baptist University Department of Computer Science Hong Kong Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350368369

The excessive proliferation of Low Earth Orbit (LEO) satellites inescapably bring the explosive growth of space data in LEO Satellite Networks (LSNs). Meanwhile, the stochastic arrivals of space data together with the time-varying satellite-ground links in LSNs pose significant challenges for offloading a large volume of space data from LSNs to ground stations. To circumvent these challenges, we systematically study the energy-constrained online data offloading problem to jointly optimize power allocation and task scheduling for LSNs. First, we leverage Lyapunov optimization to decouple our formulated long-term stochastic joint optimization problem into a set of per-time-slot subproblems. Then, each subproblem is decoupled into a task scheduling problem and a power allocation problem. Next, we derive the optimal solution to the power allocation problem and propose a multi-armed bandit based quasi-optimal solution to the task scheduling problem. Finally, extensive simulation results show that our proposed algorithm has superior performance over the state-of-the-art solutions. © 2025 IEEE.

关键词： Stochastic systems

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Image Super-Resolution Reconstruction Based on Multi-scale Residual Learning

Image Super-Resolution Reconstruction Based on Multi-scale R...

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2021 International Conference on Information Technology and Biomedical engineering, ICITBE 2021

作者： Huang, Shuying Wang, Jichao Yang, Yong School of Life Science and Technology University of Electronic Science and Technology of China China School of Computer Science and Technology Tiangong University Tianjin China School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China School of Electronics and Information Engineering Tiangong University Tianjin300387 China

ISBN: (纸本)9781665400992

Current image super-resolution (SR) reconstruction methods based on deep learning focus more on using the learning capabilities of deeper networks to obtain better reconstruction results, but the increase in depth will increase the complexity of the network structure and the difficulty of training. To address this problem, considering the advantages of residual learning that can overcome the problem of low learning efficiency, and the ability of multiscale networks that can extract global and local features at different scales, this paper proposes a multi-scale residual block (MSRB) for feature learning. Based on the constructed MSRB, a cascaded multi-scale residual network (CMSRN) is developed for image SR reconstruction. In the network, to reconstruct richer image texture details, multiple multi-scale residual blocks are cascaded to construct the residual feature learning part. Experimental results on four datasets show that the proposed network can obtain better reconstruction results, and is superior to state-of-the-art SR reconstruction methods in terms of subjective observation and objective quantitative evaluation. © 2021 IEEE.

关键词： Image texture

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Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization

Generalizable Decision Boundaries: Dualistic Meta-Learning f...

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International Conference on computer Vision (ICCV)

作者： Xiran Wang Jian Zhang Lei Qi Yinghuan Shi Nanjing University State Key Laboratory for Novel Software Technology and National Institute of Healthcare Data Science Nanjing University China Southeast University School of Computer Science and Engineering Southeast University China

Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains. However, most current methods do not account for a common realistic scenario where the source and target domains have different classes. To overcome this deficiency, open set domain generalization (OSDG) then emerges as a more practical setting to recognize unseen classes in unseen domains. An intuitive approach is to use multiple one-vs-all classifiers to define decision boundaries for each class and reject the outliers as unknown. However, the significant class imbalance between positive and negative samples often causes the boundaries biased towards positive ones, resulting in misclassification for known samples in the unseen target domain. In this paper, we propose a novel meta-learning-based framework called dualistic MEta-learning with joint DomaIn-Class matching (MEDIC), which considers gradient matching towards inter-domain and inter-class splits simultaneously to find a generalizable boundary balanced for all tasks. Experimental results demonstrate that MEDIC not only outperforms previous methods in open set scenarios, but also maintains competitive close set generalization ability at the same time. Our code is available at https://***/zzwdx/MEDIC.

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SyntaxLineDP: a Line-level software Defect Prediction Model based on Extended Syntax Information

SyntaxLineDP: a Line-level Software Defect Prediction Model ...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Jianzhong Zhu Yuan Huang Xiangping Chen Ruomei Wang Zibin Zheng School of Computer Science and Engineering Sun Yat-sen University Guangzhou Guangdong China School of Software Engineering Sun Yat-sen University Zhuhai Guangdong China School of Journalism and Communication Sun Yat-sen University Guangzhou Guangdong China

The existence of software defects greatly restricts the application of software and may bring great economic losses. Defect prediction techniques based on file level or module level can help code reviewers quickly locate defects and fix them. In recent years, researchers have used natural language techniques to explore the defectiveness of code lines by analyzing the semantic information of the code lines. However, the appearance of defects is often associated with the syntactic information of the code. To fully use the syntax information associated with code lines, we propose a code line-level representation considering the coverage of the syntax node. Specifically, we add the syntax node to its corresponding coverage lines. Combining with the BiLSTM model, we propose a new line-level defect prediction model, SyntaxLineDP, which shows good performance achieving 0.72 and 0.62 for AUC and Balanced Accuracy respectively. SyntaxLineDP outperforms the state-of-the-art model and the popular static tools (e.g., ErrorProne, PMD). Specifically, In the within-project evaluation, the SyntaxLineDP model outperforms the start-of-the-art model by 15% and 27% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. In the cross-project evaluation, the SyntaxLineDP outperforms the start-of-the-art model by 7% and 42% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. Through ablation experiments, we studied how the selection of extended syntax information contributes to the detection and found out some key features that are helpful for the defect prediction task: number of infix expressions of code line, number of blocks of code line, and number of method invocation of the code line.

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Federated Learning for Diffusion Models

arXiv

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arXiv 2025年

作者： Peng, Zihao Wang, Xijun Chen, Shengbo Rao, Hong Shen, Cong School of Mathematics and Computer Science Nanchang University Nanchang330000 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Software Nanchang University Nanchang330000 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Diffusion models are powerful generative models that can produce highly realistic samples for various tasks. Typically, these models are constructed using centralized, independently and identically distributed (IID) training data. However, in practical scenarios, data is often distributed across multiple clients and frequently manifests non-IID characteristics. Federated Learning (FL) can leverage this distributed data to train diffusion models, but the performance of existing FL methods is unsatisfactory in non-IID scenarios. To address this, we propose FEDDDPM—Federated Learning with Denoising Diffusion Probabilistic Models, which leverages the data generative capability of diffusion models to facilitate model training. In particular, the server uses well-trained local diffusion models uploaded by each client before FL training to generate auxiliary data that can approximately represent the global data distribution. Following each round of model aggregation, the server further optimizes the global model using the auxiliary dataset to alleviate the impact of heterogeneous data on model performance. We provide a rigorous convergence analysis of FEDDDPM and propose an enhanced algorithm, FEDDDPM+, to reduce training overheads. FEDDDPM+ detects instances of slow model learning and performs a one-shot correction using the auxiliary dataset. Experimental results validate that our proposed algorithms outperform the state-of-the-art FL algorithms on the MNIST, CIFAR10 and CIFAR100 datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Improved distributed quantum algorithm for Simon’s problem

arXiv

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arXiv 2025年

作者： Li, Hao Qiu, Daowen Qiu, D. Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China

Simon’s problem is one of the most important problems demonstrating the power of quantum computing. Recently, an interesting distributed quantum algorithm for Simon’s problem was proposed, where a key sorting operator requiring a large number of qubits was employed. In this paper, we design an improved distributed quantum algorithm for Simon’s problem without using sorting operators, and our algorithm has the advantage of reducing half number of qubits required for a single computing node. Moreover, our algorithm does not involve the classical search process. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption

arXiv

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arXiv 2023年

作者： Lee, Junghyun Lee, Eunsang Kim, Young-Sik Lee, Yongwoo Lee, Joon-Woo Kim, Yongjune No, Jong-Seon Department of Electrical and Computer Engineering INMC Seoul National University Korea Republic of Department of Software Sejong University Korea Republic of Department of Electrical Engineering and Computer Science DGIST Korea Republic of Department of Information and Communication Engineering Inha University Korea Republic of School of Computer Science and Engineering Chung-Ang University Korea Republic of Department of Electrical Engineering POSTECH Korea Republic of

Recent studies have explored the deployment of privacy-preserving deep neural networks utilizing homomorphic encryption (HE), especially for private inference (PI). Many works have attempted the approximation-aware training (AAT) approach in PI, changing the activation functions of a model to low-degree polynomials that are easier to compute on HE by allowing model retraining. However, due to constraints in the training environment, it is often necessary to consider post-training approximation (PTA), using the pre-trained parameters of the existing plaintext model without retraining. Existing PTA studies have uniformly approximated the activation function in all layers to a high degree to mitigate accuracy loss from approximation, leading to significant time consumption. This study proposes an optimized layerwise approximation (OLA), a systematic framework that optimizes both accuracy loss and time consumption by using different approximation polynomials for each layer in the PTA scenario. For efficient approximation, we reflect the layerwise impact on the classification accuracy by considering the actual input distribution of each activation function while constructing the optimization problem. Additionally, we provide a dynamic programming technique to solve the optimization problem and achieve the optimized layerwise degrees in polynomial time. As a result, the OLA method reduces inference times for the ResNet-20 model and the ResNet-32 model by 3.02 times and 2.82 times, respectively, compared to prior state-of-the-art implementations employing uniform degree polynomials. Furthermore, we successfully classified CIFAR-10 by replacing the GELU function in the ConvNeXt model with only 3-degree polynomials using the proposed method, without modifying the backbone model. © 2023, CC BY-NC-ND.

关键词： Polynomial approximation

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STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

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IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1972-2012 IEEE.

关键词： Convex optimization

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A Method of Multidimensional software Aging Prediction Based on Ensemble Learning: A Case of Android Os

SSRN

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SSRN 2023年

作者： Nie, Yuge Chen, Yulei Jiang, Yujia Wu, Huayao Yin, Beibei Cai, Kai-Yuan Beihang University School of Automation Science and Electrical Engineering Beijing100083 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China

Context: software aging refers to the phenomenon of performance degradation, increasing failure rate, or system crash due to resource consumption and error accumulation in software systems running for a long time. Precisely predicting software aging is crucial for enabling proactive measures before the situation substantially deteriorates. Previous machine learning (ML) methods for predicting aging predominantly rely on a single ML modeland a single labeling metric to attain their objectives. Furthermore, a common limitation shared by these methods is that training and prediction data stem from identical operational profiles. This could pose challenges for these models in delivering the desired performance when confronted with diverse user scenarios. Objective: In this paper, we aim to find a method that can both consistently and precisely predict aging trends across a wide range of application scenarios. Method: We propose a method of multidimensional software aging prediction based on ensemble learning called MSAP. Unlike the previous full-process single-threaded approaches, in the framework of MSAP, five dimensions including datasets, labeling metrics, labeling thresholds, algorithms, and model decisions are extracted and diversified to protest against diverse user scenarios and aging situations. Results: When subjected to identical workloads, MSAP demonstrates comparable performance to the majority of unidimensional models, albeit without significant prominence. Under different workloads, MSAP consistently outperforms unidimensional models whose performance drops dramatically in terms of precision, maintaining a notable lead while exhibiting minimal fluctuations. Conclusion: MSAP exhibits exceptional stability while concurrently upholding outstanding prediction precision across a spectrum of user scenarios. Therefore, it has better generalization characteristics and application prospects. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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The good, the bad, and why: unveiling emotions in generative AI 24

The good, the bad, and why: unveiling emotions in generative...

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Proceedings of the 41st International Conference on Machine Learning

作者： Cheng Li Jindong Wang Yixuan Zhang Kaijie Zhu Xinyi Wang Wenxin Hou Jianxun Lian Fang Luo Qiang Yang Xing Xie Microsoft Research Beijing China and CAS Institute of Software Beijing China Microsoft Research Beijing China Department of Computer Science William & Mary Williamsburg Virginia America School of Psychology Beijing Normal University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Emotion significantly affects our daily behaviors and interactions. Although recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance the performance of the AI model, 2) EmotionAttack to impair the performance of the AI model, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. More importantly, EmotionDecode reveals that AI models can comprehend emotional stimuli similar to the dopamine mechanism in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models, thus boosting the research and development of human-AI collaboration and mitigating potential risks.

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