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作者： Jian, Zhaolong Xie, Xueshuo Fang, Yaozheng Jiang, Yibing Lu, Ye Dash, Ankan Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Department of Computer Science New Jersey Institute of Technology NewarkNJ United States

Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud-native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube-scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube-scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube-scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average. © 2023 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on computer and Information Processing technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Literature Review of Circulating Current of Parallel Voltage Source Converters

Literature Review of Circulating Current of Parallel Voltage...

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2023 IEEE PELS Students and Young Professionals Symposium, SYPS 2023

作者： Zhang, Haoyu Wong, Man-Chung University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering Faculty of Science and Technology China

ISBN: (纸本)9798350307832

To increase the power density of voltage source converters (VSC) usually use parallel structures. However, parallel VSC will easily introduce circulating current, which can cause a power efficiency decline. This paper gives a review of the circulating current and the solutions of it. Through the analysis of the principle of circulating current generation and the detail of the solutions, this paper gives an overview of the current research stage of circulating current. The technology can be divided into hardware solutions and software solutions are then analyzed and evaluated. © 2023 IEEE.

关键词： Power converters

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Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Chen, Chenglizhao Liu, Xinyu Song, Mengke Li, Luming Yuan, Shaojiang Yu, Xu Pang, Shanchen Qingdao Institute of Software College of Computer Science and Technology China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China State Key Laboratory of Chemical Safety China

Video anomaly detection methods are mainly classified into two categories based on their primary feature types: appearance-based and action-based. Appearance-based methods rely on low-level visual features like color, texture, and shape, learning patterns specific to training scenes. While effective in familiar settings, they struggle with unknown or altered scenes due to poor generalization and limited understanding of action-scene relationships. In contrast, action-based methods focus on detecting action anomalies but often overlook contextual scene associations, leading to misjudgments (e.g., running on a street being deemed normal without considering scene context). To overcome these limitations, we propose a novel decoupling-based anomaly detection architecture (DecoAD). Its core lies in the decoupling and interweaving of scenes and actions, enabling explicit modeling of their complex relationships. By reconstructing these interactions using knowledge graphs, DecoAD achieves a deeper understanding of behaviors and contexts. This design ensures strong performance in both known and unknown scenarios, significantly enhancing generalization. To evaluate its effectiveness in dynamic scenes and its ability to handle scene-related anomalies, we introduce UFSR, the first video anomaly detection dataset featuring dynamic scenes and scene-related anomalies. DecoAD supports fully-supervised, weakly-supervised, and unsupervised settings, improving AUC on UBnormal by 1.1%, 3.1%, and 2.1% in fully-supervised, weakly-supervised, and unsupervised settings, and on UFSR by 1.2% and 8.2% in weakly-supervised and unsupervised settings. The source code and datasets are available at: https://***/liuxy3366/DecoAD. © 1991-2012 IEEE.

关键词： Video analysis

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A robust TDoA-based localization and tracking method designed for intelligent recommender systems

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Information sciences 2025年 717卷

作者： Mei, Hong-Yuan Zhou, Zhi Shen, Fu-Rao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

In today's fast-paced society, the demand for intelligent Recommender Systems(RS) is increasing day by day. Users may hope that RSs can take their real-time situation into consideration to help them make timely decisions. To fulfill this goal, RSs need to accurately understand users' information, especially their real-time location. Therefore, localization of the users is very helpful in building an intelligent RS. Meanwhile, great efforts have been made to deal with localization based on time-difference-of-arrival (TDoA) measurements in the past decades. It is widely used in applications such as radar, sonar, sensor networks, and mobile communications. However, such a task is still challenging in complex real-world scenarios where large-scale noise and none-light-of-sight (NLoS) occur. To address this challenge, we propose a noise-tolerant method consisting of two stages: the Robust Multilateration Solver (RMS) and the Restricted Particle Filter (RPF). In the first stage, anomaly detection is adopted to eliminate outliers, and NLoS effects are mitigated by a data augmentation strategy. Then a rough estimate of the current position is obtained by combining two modified multilateration algorithms. In the second stage, the rough estimate is passed through an enhanced particle filter in order to reduce its bias caused by heavy Gaussian noise and smooth the motion trajectory. Meanwhile, localization rationality is guaranteed by confining the position to a predefined area. Sufficient testing in real indoor environments and simulation results demonstrate the effectiveness of our method and reveal its fitness for various industrial fields. © 2025

关键词： Indoor localization Recommender systems Source tracking TDoA

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Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs

arXiv

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arXiv 2024年

作者： Hu, Haichuan Shang, Ye Xu, Guolin He, Congqing Zhang, Quanjun Alibaba Cloud State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Chongqing University China University Sains Malaysia Malaysia

LLMs have long demonstrated remarkable effectiveness in automatic program repair (APR), with OpenAI’s ChatGPT being one of the most widely used models in this domain. Through continuous iterations and upgrades of GPT-family models, their performance in fixing bugs has already reached state-of-the-art levels. However, there are few works comparing the effectiveness and variations of different versions of GPT-family models on APR. In this work, inspired by the recent public release of the GPT-o1 models, we conduct the first study to compare the effectiveness of different versions of the GPT-family models in APR. We evaluate the performance of the latest version of the GPT-family models (i.e., O1-preview and O1-mini), GPT-4o, and the historical version of ChatGPT on APR. We conduct an empirical study of the four GPT-family models against other LLMs and APR techniques on the QuixBugs benchmark from multiple evaluation perspectives, including repair success rate, repair cost, response length, and behavior patterns. The results demonstrate that O1’s repair capability exceeds that of prior GPT-family models, successfully fixing all 40 bugs in the benchmark. Our work can serve as a foundation for further in-depth exploration of the applications of GPT-family models in APR. © 2024, CC BY-SA.

关键词： Repair

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Graph Contrastive Learning with Cohesive Subgraph Awareness 24

Graph Contrastive Learning with Cohesive Subgraph Awareness

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33rd ACM Web Conference, WWW 2024

作者： Wu, Yucheng Wang, Leye Han, Xiao Ye, Han-Jia Key Lab of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University Nanjing China

ISBN: (纸本)9798400701719

Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping, to generate augmented graphs. However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process. We argue that incorporating an awareness of cohesive subgraphs during the graph augmentation and learning processes has the potential to enhance GCL performance. To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. In particular, CTAug comprises two specialized modules: topology augmentation enhancement and graph learning enhancement. The former module generates augmented graphs that carefully preserve cohesion properties, while the latter module bolsters the graph encoder's ability to discern subgraph patterns. Theoretical analysis shows that CTAug can strictly improve existing GCL mechanisms. Empirical experiments verify that CTAug can achieve state-of-the-art performance for graph representation learning, especially for graphs with high degrees. The code is available at https://***/10.5281/zenodo.10594093, or https://***/wuyucheng2002/CTAug. © 2024 ACM.

关键词： Stochastic systems

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A Privacy-Preserving Decision Tree Evaluation Scheme for Multiple Wearable Devices

A Privacy-Preserving Decision Tree Evaluation Scheme for Mul...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuanshun Huang Guihua Duan State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Privacy-Preserving Decision Tree (PPDT) evaluation is a typical algorithm in Machine Learning as a Service scenario. For services like health analysis, PPDT evaluation by fusing private data from multiple Wearable Devices is necessary. Homomorphic Encryption (HE) implements PPDT evaluation without revealing locally sensitive data. However, existing HE schemes need better scalability and are incompatible with clients such as Wearable Devices, which have limited computing power, bandwidth, memory, and storage. This paper proposed a noninteractive PPDT evaluation scheme for multiple Wearable Devices. We apply transciphering technology and an efficient Fully Homomorphic Encryption (FHE) cryptosystem to optimize overall performance. Simultaneously, a Computing Server and a Trusted Third Party (TTP) are adopted to perform all the homomorphic operations on the server side, leaving clients to encrypt and decrypt local data using stream ciphers. Then, we implement an instance of our scheme based on FiLIP stream cipher, a homomorphic comparison algorithm, and a homomorphic traverse algorithm. The experimental results show that the various overheads on the client side are relatively lower.

关键词： Performance evaluation Ciphers Privacy Machine learning algorithms Federated learning Scalability Servers

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UniGPS: A Unified Programming Framework for Distributed Graph Processing 27

UniGPS: A Unified Programming Framework for Distributed Grap...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Wang, Zhaokang Li, Junhong Qi, Yifan Zhu, Guanghui Yuan, Chunfeng Huang, Yihua Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9781665408783

The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm engineers. On the one hand, the programming models and interfaces differ a lot in the existing systems, leading to high learning costs and program migration costs. On the other hand, these graph processing systems are tightly bound to the underlying distributed computing platforms, requiring users to be familiar with distributed computing. To improve the usability of distributed graph processing, we propose a unified distributed graph programming framework UniGPS. Firstly, we propose a unified cross-platform graph programming model VCProg for UniGPS. VCProg hides details of distributed computing from users. It is compatible with the popular graph programming models Pregel, GAS, and Push-Pull. VCProg-based programs can be executed by compatible distributed graph processing systems without modification, reducing the learning overheads of users. Secondly, UniGPS supports Python as the programming language. We propose an interprocess-communication-based execution environment isolation mechanism to enable Java/C++-based graph processing systems to call user-defined methods written in Python. The experimental results show that UniGPS enables users to process big graphs beyond the memory capacity of a single machine without sacrificing usability. UniGPS shows near-linear data scalability and machine scalability. © 2021 IEEE.

关键词： Python

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Accurate and Complete Captions for Question-controlled Text-aware Image Captioning

Accurate and Complete Captions for Question-controlled Text-...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yehuan Wang Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

Question-controlled Text-aware Image Captioning (Qc-TextCap), is the task of generating a distinctive scene text aware caption according to several personalized questions when given an image. However, due to the diversity of visual scene, it is hard for current Optical Character Recognition (OCR) systems to extract complete scene text sentences from the images. Besides, existing works are limited in their use of question features and visual features. In this paper, we propose a Multimodal Transformer plus Scene text clustering and Cross modal attention (MTSC) to tackle the above challenges. We devise scene text clustering to group relevant scene text pieces which are detected as separate results by current OCR systems. To better utilize the information in questions and images, we design cross modal attention to enrich the features of both modalities. We extensively evaluate our model on the two Qc-TextCap datasets and superior results are achieved when comparing to state-of-the-art approaches.

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