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Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling

Science China(Information Sciences) 2019年第7期62卷 156-169页

作者： Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.

关键词： radio broadcast scheduling branch-and-bound algorithm constrained maximum weighted bipartite matching Kuhn-Munkres algorithm strategy combinations

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Devils in the Clouds: An Evolutionary Study of Telnet Bot Loaders

Devils in the Clouds: An Evolutionary Study of Telnet Bot Lo...

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IEEE International Conference on Communications (ICC)

作者： Yuhui Zhu Zhenxiang Chen Qiben Yan Shanshan Wang Alberto Giaretta Enlong Li Lizhi Peng Chuan Zhao Mauro Conti Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China Department of Computer Science and Engineering Michigan State University USA AASS MPI Lab Örebro University Sweden Department of Mathematics University of Padua Italy

One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Different approaches are necessary for studying the loaders evolution and defining a genealogy. To address the insufficient knowledge about loaders' lineage in existing studies, in this paper, we propose a semantic-aware method to measure, categorize, and compare different loader servers, with the goal of highlighting their evolution, independent from the payload evolution. Leveraging behavior-based metrics, we cluster the discovered loaders and define eight families to determine the genealogy and draw a homology map. Our study shows that the source code of Mirai is evolving and spawning new botnets with new capabilities, both on the client side and the server side. In turn, shedding light on the infection loaders can help the cybersecurity community to improve detection and prevention tools.

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Digital Twin Aided Predictive Scheduling and Bandwidth Allocation for Multi-Vehicle Cooperative Perception Systems

Digital Twin Aided Predictive Scheduling and Bandwidth Alloc...

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IEEE Conference on Vehicular Technology (VTC)

作者： Binbin Lu Xumin Huang Yuan Wu Liping Qian Dusit Niyato Tony Q. S. Quek Cheng-Zhong Xu State Key Lab of Internet of Things for Smart City Macau China School of Automation Guangdong University of Technology Guangzhou China Zhuhai- UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Computer Science and Engineering Nangyang Technological University Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

As an emerging technology, Digital Twin (DT) can provide a virtual presentation of the physical Intelligent Trans-portation Systems (ITS) to enhance the applications of ITS such as cooperation perception. In cooperative perception, accurate location is crucial for selecting proper cooperative vehicles (CoVs) to improve the perception performance. However, due to the high mobility of vehicles, the deviation between DT and physical world may lead to non-negligible location errors, which raises the challenges for achieving efficient CoV selection in cooperative perception. In this paper, we propose a DT-empowered multi-vehicle cooperative perception system, in which the CoV selection and bandwidth allocation are jointly optimized to improve the performance of cooperative perception. Specifically, an asyn-chronous federated learning scheme is deployed in DT for location prediction to mitigate the effect of the deviation. Based on the prediction results, the problem of joint predictive scheduling and bandwidth allocation is then formulated as the average delay minimization problem while reaching the required performances. The adaptive CoV selection and bandwidth allocation algorithm based on deep reinforcement learning is proposed to find the optimal scheduling strategy. Simulation results demonstrate that the proposed algorithm achieves the lowest average delay while effectively guaranteeing the performance requirement.

关键词： Vehicular and wireless technologies Federated learning Simulation Optimal scheduling Channel allocation Prediction algorithms Minimization

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Research on Microelectronic System of Antagonism Inhibiting Spasticity Based on Electro-Myo-Graphic Bridge

Research on Microelectronic System of Antagonism Inhibiting ...

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International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Peng-Yu Lou Yun-Hua Feng Yun-Long Wang Xiao-Ying Lü Zhi-Gong Wang College of Computer and Information Hohai University Nanjing China School of Software Pingdingshan University Pingdingshan China State Key Lab of Bioelectronics Southeast University Nanjing China Institute of RF-& OE-ICs Southeast University Nanjing China

ISBN: (纸本)9781665460583

In this paper, based on the principle of stimulating antagonistic muscles, functional electrical stimulation (FES) is used to achieve the purpose of spasticity suppression. The microelectronic system for spasticity suppression was designed and studied, and then used to simulated spasticity of healthy people. First, based on the characteristics of spastic EMG signals, obtained by the EMG signal detection system of the research group, a detection module was designed to detect EMG signals of voluntary movement, simulated EMG signals of spastic patients. Furthermore, an adaptive pattern recognition algorithm for spasticity was designed to identify spasticity signals from the detected EMG signals. Then, on basis of the functional electrical stimulation core circuit of the research group, a microelectronic FES system based on the myoelectric bridge was designed. Finally, the functional verification experiment of the FES system to stimulate antagonist muscle to inhibit spasm was carried out, The results show that the system is effective for stimulating the wrist extensor muscle as an antagonist muscle to inhibit spasm.

关键词： Wrist Micromechanical devices Bridge circuits Muscles Electromyography Iron Microelectronics

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Parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System

Parallel-Friendly and Work-Efficient Single Source Shortest ...

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

作者： Jie Zhang Yuan Zhang Yiming Sun Huawei Cao Xuejun An Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of Information Superbahn CAS Nanjing China

The Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. Then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. The experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation.

关键词： Partitioning algorithms Kernel Optimization Faces

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MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

arXiv

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arXiv 2022年

作者： Duan, Yue Zhao, Zhen Qi, Lei Wang, Lei Zhou, Luping Shi, Yinghuan Gao, Yang The National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia The School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while low-confidence samples are employed to achieve a simpler goal — to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Our code and model weights have been released at https://***/NJUyued/MutexMatch4SSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks

arXiv

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arXiv 2023年

作者： Wang, Shao-Di Wang, Hui-Ming Li, Zhetao Leung, Victor C.M. The School of Information and Communication Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China The Key Laboratory of Hunnan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent Network School of Computer Science Xiangtan University Xiangtan411105 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

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Intelligent Medicine 2023年第2期3卷 144-149页

作者： Yahan Yang Ruiyang Li Yifan Xiang Duoru Lin Anqi Yan Wenben Chen Zhongwen Li Weiyi Lai Xiaohang Wu Cheng Wan Wei Bai Xiucheng Huang Qiang Li Wenrui Deng Xiyang Liu Yucong Lin Pisong Yan Haotian Lin Chinese Association of Artificial Intelligence Medical Artificial Intelligence Branch of Guangdong Medical Association Zhongshan Ophthalmic Center Sun Yat-sen UniversityState Key Laboratory of OphthalmologyGuangzhouGuangdong 510623China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhouGuangdong 510080China Nanjing University of Aeronautics and Astronautics NanjingJiangsu 210016China Institute of Software Engineering School of Computer Science and TechnologyXidian UniversityXi’anShaanxi 710071China Cloud Intelligent Care Tech.Ltd. GuangzhouGuangdong 510115China Centre for Precision Medicine Sun Yat-sen UniversityGuangzhouGuangdong 510080China 不详

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based *** has a massive amount of medical ***,a uniform criteria for medical data quality have yet to be ***,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical *** would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

关键词： Artificial intelligence Big data Intelligent medicine Data collection Data storage Data annotation Data management

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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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Downscaled Correction of Temperature Forecast Algorithm with Encoder-Decoder over the Hengduan Mountains 4

Downscaled Correction of Temperature Forecast Algorithm with...

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4th IEEE International Conference on computer and Communication engineering Technology, CCET 2021

作者： Meng, Jingwei Dong, Danhong Zhang, Wulong Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing China Institute of Atmospheric Physics Chinese Academy of Sciences State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Beijing China Heavy Rain and Drought-Flood Disaster in Plateau and Basin Key Laboratory of Sichuan Province Sichuan Provincial Meteorology Observatory Chengdu China

ISBN: (纸本)9781665438902

Global climate models (GCMs), a primary tool of Numerical weather prediction (NWP) has an irreplaceable position in weather forecasting. Because of the inevitable model error and the coarse resolution, it is necessary to develop the downscaled correction method to reduce the model prediction error and improve the forecast result on the basis of the existing model, especially in the mountains. The Hengduan Mountains (HMs) belongs to the monsoon region accompanied by the highly complex mountainous topography, leading to the accuracy of GCMs limited in this region. The study aims to propose a more accurate deep learning algorithm than the downscaled correction model based on the deep learning method before. In this study, downscaled correction of temperature forecast algorithm with Encoder-Decoder (DCTA-ED) is proposed for downscaling the GCMs results of 2m temperature from coarse grid data to meteorological station point forecast over HMs. The proposed algorithm extracts the interaction between multiple meteorological data and time features of temperature, wind speed, air pressure and humidity from previous position observation, to make the downscaled correction prediction results more consistent with the characteristics of positions. The experimental results demonstrate that the proposed algorithm exceeds most of the downscaled correction models for weather stations with different terrains. © 2021 IEEE.

关键词： Weather forecasting

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