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International Conference on Internet Technology and Applications (iTAP)

作者： Mingxin Yang Jingsha He Xuguang Sun College of Computer Science Beijing University of Technology Beijing China College of Economics Management Hebei University of Science and Technology Shijiazhuang China School of Software Engineering Beijing University of Technology Beijing China

We discuss the technology detail of data aggregation in wireless sensor networks (WSNs) in this paper, which consists of three parts. First, we apply the classical K-average clustering algorithm to the phase of cluster division which is the first step of the aggregate tree. Second, the cluster head node and the cluster group nodes are constructed based on the entropy formula H(S g |x)g(c) and pseudo-random function. Finally, we study the secure data fusion method among the nodes.

关键词： Aggregates Wireless sensor networks Clustering algorithms Data mining Partitioning algorithms Security Educational institutions

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Identification of Abnormal Behavior in Electric Power Operations Using the Human Skeleton's Key Features 8

Identification of Abnormal Behavior in Electric Power Operat...

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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Zhou, Yang Wang, Xingjian Hu, Guoqiang Luo, Yong Chai, Zheng Xu, Wei Liu, Qiuming State Grid Jiangxi Electric Power Co Information and Communication Branch Nanchang330095 China Computer College Nanjing University of Posts and Telecomunications Nanjing210032 China State Grid Nanchang Power Supply Company Nanchang330095 China School of Software Jiangxi Normal University Nanchang330022 China School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China

ISBN: (纸本)9798350335231

At present, the safety monitoring of electric power sites mainly monitors the monitoring video through personnel, which has problems such as heavy workload, missed detection, and false alarms. In order to realize the intelligent recognition of the behavior of personnel at the electric power operation site, an abnormal behavior recognition method based on the key points of human skeleton is proposed, which can realize the real-time detection of the behavior of personnel at the operation site. First, the backbone network of YOLOv5s is improved for target detection, and the trajectory of the target is obtained by combining the DeepSort target tracking algorithm;then, the AlphaPose attitude estimation method is used to extract the target skeleton sequence from the trajectory;finally, the multi-partition spatiotemporal graph convolutional network is used to track the personnel Abnormal behavior identification. The experimental results show that the recognition accuracy rate of the algorithm reaches 93.5%, and the processing speed reaches 18.32 fps, which can identify abnormal behaviors of electric power workers in real time and accurately. © 2023 IEEE.

关键词： Personnel

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Amplifying the Voice of Blacks in Computing

Amplifying the Voice of Blacks in Computing

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Black Issues in Computing Education (BICE)

作者： Cheryl Swanier Cheryl Seals Dale-Marie Wilson Manning College of Information and Computing Sciences University of Massachusetts USA Computer Science and Software Engineering Auburn University USA Department of Software and Information Systems University of North Carolina at Charlotte USA

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

This workshop is designed to intentionally share experiences and connect participants to their greatest asset, their voice. There is power in your voice. Engaging interaction, thought-provoking storytelling, and authentic truth telling will open the way for understanding the value of harnessing voice power when navigating professionally and personally. Despite diversity, equity, and inclusion efforts, stated commitments to diversity, the demographic characteristics of the professoriate and industry look remarkably like the faculty and professionals of 50 years ago for Blacks in computing. Blacks in computing constantly face discrimination in the form of microaggressions and white fragility. This discrimination has not translated to greater Black faculty and professional representation in their career advancement. Instead, the health of both faculty and professionals are being adversely impacted, as they are experiencing insomnia, anxiety, depression, emotional distress, and physical exhaustion. This disruption of norms has a negatively impacted the productivity of both Black men and women at work and can lead to their demotion and dismissal. Many times, these struggles are not shared, and their voices are silenced. This workshop primarily addresses how Blacks in computing can use their voice to advance their careers and utilize proven voice positioning strategies to harness their voice power and to maximize professional and personal wellbeing. These voice strategies will facilitate both Black men and women in their career advancement, skill development, and preparation for entry into their next career level. A failure to address these challenges has negative implications for both academia and industry

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A novel object detection algorithm based on enhanced R-FCN and SVM

A novel object detection algorithm based on enhanced R-FCN a...

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Telecommunications, Optics and computer science (TOCS), IEEE Conference on

作者： Cong Xu Jiahao Fan Lin Liu College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Space Technology (Jilin) Limited Company Jilin China

ISBN: (数字)9781728181172

ISBN: (纸本)9781728181189

Object detection is an extremely important part of computer vision. However, the object detection result of R-FCN is not good enough in terms of speed and accuracy. In this paper, a novel architecture called Enhanced R-FCN (ER-FCN) is proposed for object detection. Two improvements are presented in ER-FCN. Firstly, novel anchor boxes, 3 scales with box areas of 512 2 , 256 2 and 128 2 pixels, and 3 aspect ratios of 0.618:1, 1:1 and 1:0.618, are designed to suit the different scales object detection in RPN. Hence, the performance of object localization and detection speed are increased. Secondly, since the softmax classifier is not optimal to deal with the binary classification problem, a Whale Optimization Algorithm based on support vector machine, termed WOA-SVM, is introduced to improve the accuracy of classification. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 datasets show that the mean average precision of ER-FCN is improved by 3.9% compared with that of R-FCN.

关键词： Support vector machines Location awareness Object detection Optics Classification algorithms Telecommunications Optimization

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Research on Scheduling Scheme for Hadoop Clusters

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Procedia computer science 2013年 18卷 2468-2471页

作者： Jiong Xie FanJun Meng HaiLong Wang HongFang Pan JinHong Cheng Xiao Qin Inner Mongolia Electric Power Information and Communication Center Hohhot China Department of Computer Science and Software Engineering Auburn University Auburn AL 36849-5347 USA Computer & Information Engineering College Inner Mongolia Normal University Hohhot China

In this paper, we import a prefetching mechanism into MapReduce model while retaining compatibility with the native Hadoop. Given a data-intensive application running on a Hadoop MapReduce cluster, our approach estimates the execution time of each task and adaptively preloads an amount of data to the memory before the new task is assigned to the computing node. We implement a predictive schedule and prefetching (PSP) mechanism, which is integrated into the native MapReduce runtime system. We also evaluate performance on a 10-node cluster using two popular benchmarks–grep and wordcount. The PSP mechanism reduces the execution time of grep and wordcount up to 28% with an average of 19%. Moreover, the PSP model increases the overall throughput and improves the I/O utilization. Because of the limitation of length, we did not present the experiment result detail in this paper.

关键词： MapReduce Prefetching Schedule

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Generative Semantic Communication for Joint Image Transmission and Segmentation

arXiv

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arXiv 2024年

作者： Yuan, Weiwen Ren, Jinke Wang, Chongjie Zhang, Ruichen Wei, Jun Kim, Dong In Cui, Shuguang FNii-Shenzhen China SSE United Kingdom Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the two task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results demonstrate that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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General Particle Swarm Optimization Based on Simulated Annealing for Multi-Specification One-dimensional Cutting Stock Problem

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International Conference on Computational Intelligence and Security

作者： Xianjun Shen Yuanxiang Li Zhifeng Dai Bojin Zheng State Key Lab of Software Engineering Wuhan University Wuhan China Department of Computer Science HuaZhong Normal University Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

ISBN: (纸本)1424406048

In this paper, a general particle swarm optimization based on SA algorithm (SA-GPSO) for the solution to multi-specification one-dimensional cutting stock problem is proposed. Due to the limitation of its velocity-displacement search model, particle swarm optimization (PSO) has less application on discrete and combinatorial optimization problems effectively. SA-GPSO is still based on PSO mechanism, but the new updating operator is developed from simulated annealing algorithm, crossover operator and mutation operator of genetic algorithm. In order to repair invalid particle and reduce the searching space, best fit decrease (BFD) is introduced into repairing algorithm of SA-GPSO. According to the experimental results, it is observed that the proposed algorithm is feasible to solve both sufficient one-dimensional cutting problem and insufficient one-dimensional cutting problem

关键词： Particle swarm optimization Simulated annealing Computational modeling computer simulation computer science Genetic mutations Genetic algorithms Educational institutions Birds Upper bound

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On the security of networked control systems in smart vehicle and its adaptive cruise control

arXiv

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arXiv 2020年

作者： Farivar, Faezeh Haghighi, Mohammad Sayad Jolfaei, Alireza Wen, Sheng Department of Mechatronics and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

With the benefits of Internet of Vehicles (IoV) paradigm, come along unprecedented security challenges. Among many applications of inter-connected systems, vehicular networks and smart cars are examples that are already rolled out. Smart vehicles not only have networks connecting their internal components e.g. via Controller Area Network (CAN) bus, but also are connected to the outside world through road side units and other vehicles. In some cases, the internal and external network packets pass through the same hardware and are merely isolated by software defined rules. Any misconfiguration opens a window for the hackers to intrude into vehicles’ internal components e.g. central lock system, Engine Control Unit (ECU), Anti-lock Braking System (ABS) or Adaptive Cruise Control (ACC) system. Compromise of any of these can lead to disastrous outcomes. In this paper, we study the security of smart vehicles’ adaptive cruise control systems in the presence of covert attacks. We define two covert/stealth attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks. More precisely, we focus on the covert cyber attacks that compromise the integrity of cruise controller and employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it against the ACC output. If any anomaly is detected, an embedded substitute controller kicks in and takes over the control. We conducted extensive experiments in MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment. Copyright © 2020, The Authors. All rights reserved.

关键词： software defined networking

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An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model

An Empirical Study of Parameter Efficient Fine-tuning on Vis...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuxin Tian Mouxing Yang Yunfan Li Dayiheng Liu Xingzhang Ren Xi Peng Jiancheng Lv College of Computer Science Sichuan University Chengdu China School of Software and Microelectronics Peking University Beijing China College of Computer Science Sichuan University and Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size and fine-tunable parameter size. A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size. However, according to the evaluation of five PEFTs on two downstream vision-language (VL) tasks, we find that such an intuition holds only if the downstream data and task are not consistent with pre-training. For downstream fine-tuning consistent with pre-training, data size no longer affects the performance, while the influence of fine-tunable parameter size is not monotonous. We believe such an observation could guide the choice of training strategy for various PEFTs.

关键词： Training Data models

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Recent Advances in computer science and Information engineering 1

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丛书名： Lecture Notes in Electrical engineering

1000年

作者： Zhihong Qian Lei Cao Weilian Su Tingkai Wang Huamin Yang

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