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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Lin, Ziqi He, Zhenli Zhai, Xiaolong Xiao, Yuanfei School of Software Yunnan University Kunming650504 China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China Engineering Research Center of Cyberspace Yunnan University Kunming China

ISBN: (纸本)9789819628636

Mobile Edge Computing (MEC) extends computational resources to the network edge, enabling low-latency services for users and IoT devices. While existing research advances service migration, it often isolates migration from resource allocation, leading to suboptimal edge server utilization and increased response delays. Moreover, the impact of migrating user-specific service contexts within network topologies is often overlooked, which undermines the effectiveness of migration strategies and increases costs. To address these issues, we propose a joint optimization of service migration and resource allocation in MEC, modeled as a Multi-Agent Markov Decision Process (MAMDP). We propose an algorithm based on Multi-Agent Reinforcement Learning (MARL). By employing Multi-Agent Proximal Policy Optimization (MAPPO) and Karush-Kuhn-Tucker (KKT) conditions, our approach optimizes both service migration and resource allocation strategies, thereby enhancing service quality. Simulations demonstrate that our method significantly outperforms benchmarks, achieving substantial reductions in response delays, service failures, and migration costs. © IFIP International Federation for Information Processing 2025.

关键词： Markov processes

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BTDS:Blockchain-Enabled Trusted Vehicle Violation Detection by Self-Supervision

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IEEE Internet of Things Journal 2025年第12期12卷 20156-20173页

作者： Zhu, Rui Hu, Shengnan Helal, Sumi Song, Junqiao Wang, Jishu Chen, Yeting Yunnan University School of Software Kunming650091 China Key Laboratory in Software Engineering Yunnan Province Kunming650091 China University of Florida GainesvilleFL32611 United States Yunnan Normal University School of Economics and Management Kunming650500 China

In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this paper, we propose a blockchain-based self-supervision model for vehicle violations that utilizes inter-vehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles. © 2014 IEEE.

关键词： Blockchain

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Learning a generalizable re-identification model from unlabelled data with domain-agnostic expert

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Visual Intelligence 2024年第1期2卷 337-349页

作者： Fangyi Liu Mang Ye Bo Du National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication EngineeringSchool of Computer ScienceHubei Luojia LaboratoryWuhan UniversityWuhan430072China

In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains ***,the performance of current DG ReID relies heavily on labor-intensive source domain *** the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in *** goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target *** address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person *** involves independently training multiple experts to account for label space inconsistencies between source *** the same time,the DaE captures domain-generalizable information for *** experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG *** results demonstrate the superiority of our method over state-of-the-art *** will make our code and models available for public use.

关键词： Domain generalization(DG) Unlabeled source domains Label space inconsistencies Domain-agnostic expert(DaE)

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Prediction of New Functional Fluorooxoborates with Expected Anionic Framework:A Case of CaB_(3)O_(5)F

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Chinese Physics Letters 2025年第3期42卷 85-92页

作者： Ke-Wang Zhang Wen-Qi Jin Ke Li Abudukadi Tudi Lin-Lin Liu Cong-Wei Xie Yu Xie Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special Environmental ConditionsXinjiang Key Laboratory of Functional Crystal MaterialsXinjiang Technical Institute of Physics&ChemistryCASUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Materials Simulation Methods&Software of Ministry of Education International Center of Future ScienceKey Laboratory of Physics and Technology for Advanced Batteries(Ministry of Education)College of PhysicsJilin UniversityChangchun 130012China

The prediction of new fluorooxoborates as ultraviolet(UV)/deep ultraviolet(DUV)opto-electronic functional materials from a largely unexplored chemical space is a challenging *** has been suggested that the anionic frameworks formed by B–O and B–O–F units significantly determine the physical properties of ***,the rational design of anionic frameworks could facilitate the materials discovery ***,we propose that a candidate anionic framework can be efficiently derived from an existing one by slightly altering its oxygen *** this idea,we hypothesized the existence of a 1D[B_(3)O_(5)F]_(∞)chain from the wellknown 2D[B_(6)O_(9)F_(2_)]_(∞)***,seven CaB_(3)O_(5)F structures with the expected anionic framework were successfully ***-principles calculations show that all these structures have potential in the UV/DUV birefringent or nonlinear optical(NLO)material field,indicating that the 1D[B_(3)O_(5)F]_(∞)chain is indeed a promising anionic framework for achieving UV/DUV birefringent and NLO performance.

关键词： process borate anionic

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Counterfactual Fairness for Graph Neural Networks with Limited and Privacy Protected Sensitive Attributes 16

Counterfactual Fairness for Graph Neural Networks with Limit...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Wang, Xuemin Wang, Lei Gu, Tianlong Bao, Xuguang Guangxi Key Laboratory of Trusted Software China Engineering Research Center of Trustworthy AI Ministry of Education China

Graph Neural Networks (GNNs) have shown outstanding performance in learning graph representations, which increases their application in high-risk areas. However, GNNs may inherit biases from the graph data and make unfair predictions towards the protected sub-groups. To eliminate bias, a natural idea is to achieve counterfactual fairness from a causal perspective. Concretely, counterfactual fairness requires sufficient sensitive attributes as guidance, which is infeasible in the real world. The reason is that users with various privacy preferences may selectively publish their sensitive attributes and only limited sensitive attributes can be collected. Besides, the users who publish sensitive attributes still face privacy risks. In this paper, we first consider the situation in which the sensitive attributes are limited and propose a framework called PCFGR (Partially observed sensitive Attributes in Counterfactual Fair Graph Representation Learning) to learn fair graph representation from limited sensitive attributes. The framework trains a sensitive attribute estimator, which is applied to provide sufficient and accurate sensitive attributes. With these sensitive attributes, it can generate counterfactuals and eliminate the bias efficiently. Secondly, we aim to protect the privacy of the sensitive attributes and further propose PCFGR\D. Specifically, PCFGR\D first perturbs the sensitive attributes using Local Differential Privacy (LDP). Then it employs forward correction loss to train an accurate sensitive attributes estimator. We conduct extensive experiments and the experiment results show that it outperforms other alternatives in balancing utility and fairness. © 2024 X. Wang, L. Wang, T. Gu & X. Bao.

关键词： Differential privacy

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Open-Book Neural Algorithmic Reasoning 38

Open-Book Neural Algorithmic Reasoning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Hefei Peng, Chao Xu, Chenyang Yang, Zhengfeng Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm - feeding an individual problem instance into the network each time and training it to approximate the execution steps of a classical algorithm. We challenge this mode and propose a novel open-book learning framework. In this framework, whether during training or testing, the network can access and utilize all instances in the training dataset when reasoning for a given instance. Empirical evaluation is conducted on the challenging CLRS Algorithmic Reasoning Benchmark, which consists of 30 diverse algorithmic tasks. Our open-book learning framework exhibits a significant enhancement in neural reasoning capabilities. Further, we notice that there is recent literature suggesting that multi-task training on CLRS can improve the reasoning accuracy of certain tasks, implying intrinsic connections between different algorithmic tasks. We delve into this direction via the open-book framework. When the network reasons for a specific task, we enable it to aggregate information from training instances of other tasks in an attention-based manner. We show that this open-book attention mechanism offers insights into the inherent relationships among various tasks in the benchmark and provides a robust tool for interpretable multi-task training. © 2024 Neural information processing systems foundation. All rights reserved.

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Pairwise tagging framework for end-to-end emotion-cause pair extraction

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Frontiers of Computer Science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

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MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5684-5696页

作者： Wang, Meng Ding, Yan Liu, Yumeng Qin, Yunchuan Li, Ruihui Tang, Zhuo The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China

Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field of view and occlusion problems caused by image input. Moreover, existing methods tend to produce erroneous scene hallucinations and overly smooth boundary segmentation due to a lack of information. To address this problem, we propose MixSSC, which mixes the sparsity of forward projection with the denseness of depth-prior backward projection. The aim is to use sparse features to fill information-poor regions and dense features to enhance visible regions. Specifically, we develop the forward-backward mixture module, which enables the generation of scene mixture voxel representation by leveraging the benefits of both forward and backward projection. Subsequently, we design the semantic-spatial fusion module, which utilizes a coarse-to-fine approach to process mixture voxel features at the semantic-spatial level. Extensive experimental results on the SemanticKITTI, SSCBench-KITTI-360 and nuScenes datasets demonstrate the superiority of MixSSC. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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Science China(Information Sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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