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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Chang Deng Wenjun Xu Chia-Han Lee Hui Gao Wenbo Xu Zhiyong Feng Key Lab of Universal Wireless Communications Beijing University of Posts and Telecommunications Ministry of Education China National Chiao Tung University Taiwan Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

关键词： Unmanned aerial vehicles Trajectory optimization Data communication Multicast communication Energy consumption

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-replica clustering management method based on limited-coding

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Ruan Jian Xue Bao/Journal of Software 2007年第6期18卷 1456-1467页

作者： Zhou, Jing Wang, Yi-Jie Li, Si-Kun School of Computer National University of Defense Technology Changsha 410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

In this paper, according to the resource management problems brought by a large number of replicas, a multi-replica clustering management method based on limited-coding is proposed. In this method, according to the process of creating new replicas from existent single replica, replicas are partitioned into different hierarchies and clusters. Then replicas are coded and managed based on the user-defined limited-coding rule consisting of replica hierarchy and replica sequence, which can also dispose the alteration of clusters caused by dynamic adjustments on replicas (replica addition or replica removal) effectively. After that, a management model of centralization in local and peer to peer in wide area is adopted to organize replicas, and the cost of reconciling consistency can be greatly depressed combining with defined minimal-time of update propagation. The relevance between the coding rule and the number of replicas, and the solutions to replica failure and replica recover are discussed. The results of the performance evaluation show that the clustering method is an efficient way to manage a large number of replicas, achieving good scalability, not sensitive to moderate node failure, and adapting well to applications with frequent updates.

关键词： Data processing

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WBRP: Consensus Algorithm Based on Weak Proof of Work

WBRP: Consensus Algorithm Based on Weak Proof of Work

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IEEE International Conference on Blockchain (Blockchain)

作者： Jiarui Li Dongbin Wang Hui Zhi Wencheng Zhu Jianing Li Zedong Wang School of Cyberspace Engineering Beijing University of Post and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Nakamoto Consensus (NC) is a classic Proof of Work (PoW) consensus algorithm where the probability of miners earning the right to a block is proportional to their computational power. To prevent blockchain forks, the interval between block outputs is restricted, which results in lower throughput for PoW. To address blockchain performance issues, a transaction ledger scheme based on weak PoW consensus has been proposed. This approach combines strong and weak blocks, allowing miners to submit weak blocks with lower mining difficulty. Nodes can then use these weak blocks to organize transaction information on the main chain and ensure strict linear ordering of blocks and transactions through sorting rules. Experimental results demon-strate that the consensus algorithm based on weak PoW can significantly enhance system performance, allowing transaction throughput to increase linearly with bandwidth. Specifically, with a node bandwidth of 1 Gb/s, the performance can exceed NC by more than 300 times.

关键词： System performance Consensus algorithm Bandwidth Throughput Proof of Work Extensibility Blockchains

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ShenTu: Processing Multi-Trillion Edge Graphs on Millions of Cores in Seconds 18

ShenTu: Processing Multi-Trillion Edge Graphs on Millions of...

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ACM/IEEE International Conference for High Performance computing, Networking, Storage, and Analysis

作者： Heng Lin Xiaowei Zhu Bowen Yu Xiongchao Tang Wei Xue Wenguang Chen Lufei Zhang Torsten Hoefler Xiaosong Ma Xin Liu Weimin Zheng Jingfang Xu Tsinghua University and Fma Technology Tsinghua University and Qatar Computing Research Institute Tsinghua University State Key Laboratory of Mathematical Engineering and Advanced Computing ETH Zurich Qatar Computing Research Institute National Research Centre of Parallel Computer Engineering and Technology Beijing Sogou Technology Development Co. Ltd

ISBN: (纸本)9781538683859

Graphs are an important abstraction used in many scientific fields. With the magnitude of graph-structured data constantly increasing, effective data analytics requires efficient and scalable graph processing systems. Although HPC systems have long been used for scientific computing, people have only recently started to assess their potential for graph processing, a workload with inherent load imbalance, lack of locality, and access irregularity. We propose ShenTu ⁸ the first general-purpose graph processing framework that can efficiently utilize an entire Petascale system to process multi-trillion edge graphs in seconds. ShenTu embodies four key innovations: hardware specialization, supernode routing, on-chip sorting, and degree-aware messaging, which together enable its unprecedented performance and scalability. It can traverse a record-size 70-trillion-edge graph in seconds. Furthermore, ShenTu enables the processing of a spam detection problem on a 12-trillion edge Internet graph, making it possible to identify trustworthy and spam webpages directly at the fine-grained page level.

关键词： Random access memory Servers Data analysis Supercomputers Task analysis Computer architecture Optimization supercomputer task analysis random access machine Computer Architecture Servers Data analysis core Core samples Scalability Edge of a figure or solid Unsolicited electronic mail

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Balancing CPU-GPU Collaborative High-Order CFD Simulations on the Tianhe-1A Supercomputer

Balancing CPU-GPU Collaborative High-Order CFD Simulations o...

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Chuanfu Xu Lilun Zhang Xiaogang Deng Jianbin Fang Guangxue Wang Wei Cao Yonggang Che Yongxian Wang Wei Liu College of Computer Science National University of Defense Technology Changsha P.R. China parallel and Distributed Systems Group Delft University of Technology Delft The Netherlands State Key Laboratory of Aerodynamics Mianyang P.R. China National University of Defense Technology Changsha Hunan CN

ISBN: (纸本)9781479938018

HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.

关键词： Graphics processing units Kernel Collaboration Computational fluid dynamics Computational modeling Memory management Performance evaluation

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RBCP: A Reputation-based Blockchain Consensus Protocol for Internet of Things

RBCP: A Reputation-based Blockchain Consensus Protocol for I...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Jiaze Shang Tianbo Lu Yingjie Cai Shuang Luo Zhaoxin Jin School of Computer Science (National Pilot Software Enginerring School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

The application of blockchain in the Internet of Things (IoT) solves the problem of centralization, enables data interaction of IoT nodes in untrusted environments. However, most of the blockchain consensus protocols are computationally expensive and poorly scalable, which cannot meet the current demand for low power consumption and low latency of IoT devices. Meanwhile, IoT nodes suffer from many malicious attacks. An effective reputation evaluation scheme is valuable for establishing a trusted, secure IoT environment with certain applications. In this paper, we propose RBCP, a novel reputation-based blockchain consensus protocol, and apply it to the IoT. The protocol divides IoT nodes into multiple regions, and consensus nodes in each region are selected based on real-time updated reputation values, which are used to participate in light PoW consensus and finally propose blocks. Then, we model the deposits and profits of the nodes as Stackelberg game and analyze the relationship between them. To evaluate our proposed protocol, we compare it with existing consensus protocols through several experiments. Our experimental results show that RBCP has advantages in terms of TPS, consensus time, and communication consumption, proving that it has low latency and less consensus time under the premise of sufficient security.

关键词： Time-frequency analysis Smart cities Scalability Games Real-time systems Consensus protocol Behavioral sciences

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Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

arXiv

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arXiv 2022年

作者： Jin, Haibo Chen, Ruoxi Zheng, Haibin Chen, Jinyin Cheng, Yao Yu, Yue Liu, Xianglong College of Information Engineering Zhejiang University of Technology Hangzhou China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Huawei International Singapore National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China State Key Laboratory of Software Development Environment Beihang University Beijing China

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared with the state-of-the-art testing approaches, DeepSensor can find more test errors due to adversarial inputs (∼ ×1.2), polluted data (∼ ×5) and incompletely-trained DNNs (∼ ×1.3). Additionally, it can help DNNs build larger l2-norm robustness bound (∼ ×3) via retraining according to CLEVER's certification. We further provide interpretable proofs for effectiveness of DeepSensor via excitable neuro

关键词： Deep neural networks

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LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers

SSRN

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SSRN 2023年

作者： Xu, Jia Tang, Rongrong Lv, Pin Yu, Minghe Yu, Ge Chen, Enhong School of Computer Electronics and Information Guangxi University GuangXi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi530004 China Northeastern University Shenyang110819 China University of Science and Technology of China Hefei230088 China

Knowledge Tracing (KT) is a critical but challenging problem for many educational applications. As an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing KT tasks. However, existing KT methods only consider the positive learning transfer and neglect the negative learning transfer. To this end, in this paper, we propose a novel deep knowledge tracing model, called positive and negative Learning Transfers based Knowledge Tracing (LTKT). To the best of our knowledge, LTKT makes the first attempt to concurrently utilize the positive and negative learning transfer relations among concepts to improve KT results. First, LTKT employs a statistical-based approach to construct a learning transfer graph (LTG). Then, LTKT quantifies the impact of an exercise's practice result on the knowledge state of the concept via a direct learning effect component, after which a learning transfer effect component is carefully designed to quantify the impact of the practice result on the knowledge states of neighboring concepts based on the positive and negative learning transfer relations modeled by LTG. We conduct extensive experiments on public real-world datasets, and the experimental results show that LTKT outperforms all state-of-the-art KT methods and has good interpretability. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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