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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Qi, Zexuan Yang, Sihan He, Xinjian Shi, Desheng Xinjiang University of Science & Technology Xinjiang Korla China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Yunnan Mengzi China

ISBN: (纸本)9798350360240

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market. © 2024 IEEE.

关键词： Prediction models

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Pondering About Task Spatial Misalignment: Classification-Localization Equilibrated Object Detection 48

Pondering About Task Spatial Misalignment: Classification-Lo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Yudong Lu, Wei Wang, Xu Wang, Pengkun Wang, Yang Hefei China School of Software Engineering Ustc Hefei China School of Computer Science and Technology Ustc Hefei China School of Data Science Ustc Hefei China Suzhou Institute for Advanced Research Ustc Suzhou China

ISBN: (纸本)9781728163277

Object detection is a fundamental task in computer vision, consisting of both classification and localization tasks. Previous works mostly perform classification and localization with shared feature extractor like Convolution Neural Network. However, the tasks of classification and localization exhibit different sensitivities with regard to the same feature, hence the "task spatial misalignment"issue. This issue can result in a hedge issue between the performances of localizer and classifier. To address these issues, we first propose a novel Dynamic Coefficient Loss to simultaneously consider and balance the performances of classification and localization tasks. To well address anchor label misjudgment issue in irregular- shaped object detection, we define a new classification-aware IoU metric to assign anchors intelligently. Finally, we further introduce the localization factor into NMS by proposing a Classification-Localization balanced NMS. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our proposals can improve RetinaNet by around 1.5% AP with various backbones. © 2023 IEEE.

关键词： Non-Maximum Suppression Object detection task spatial misalignment

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Machine Learning-Based Analysis of Tennis Match Processes 6

Machine Learning-Based Analysis of Tennis Match Processes

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6th International Symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2024

作者： Li, Ke Yang, Nuo Yu, Yan School of Economics and Finance Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Geography and Remote Sensing Hohai University Nanjing China

ISBN: (纸本)9798331528942

Prediction is an important topic in machine learning. Given the random nature of sports events, they are meaningful experimental subjects for conducting research. In this paper, we focused on answering three key questions: (1) How to determine which player is performing better at a specific time? (2) What is the impact of so-called 'momentum' on the outcome of a match? And (3) How to determine which player has the advantage at a certain point in the match. Firstly, we construct the wavelet neural network, in which Spearman's correlation coefficient is 0.7069, to decompose the performance of a player in real-time into relevant quantifiable features, evaluating which player is performing better at the moment. Then, we identified the main features affecting momentum and proposed the Additive-Multiplicative Model. The results confirm the momentum role in determining the outcome of a player's match. Finally, we established a random forest model with 30 decision trees. The accuracy and recall rates are 83.3% and 89.0%, respectively. Our study shows high accuracy and robustness in the outcome of sports events prediction and demonstrates promising potential for further applications. © 2024 IEEE.

关键词： Adversarial machine learning

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A robust deformed convolutional neural network(CNN)for image denoising

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 331-342页

作者： Qi Zhang Jingyu Xiao Chunwei Tian Jerry Chun‐Wei Lin Shichao Zhang School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina School of Computer Science Central South UniversityChangshaChina School of Software Northwestern Polytechnical UniversityXi'anShaanxiChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina Yangtze River Delta Research Institute Northwestern Polytechnical UniversityTaicangChina Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway

Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image *** relations of surrounding pixels can effectively resolve this *** by that,we propose a robust deformed denoising CNN(RDDCNN)in this *** proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding *** EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed *** address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean ***,we implement a blind denoising *** results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative *** can be obtained at https://***/hellloxiaotian/RDDCNN.

关键词： blind denoising CNN deformed block enhanced block

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FSC: File Storage in Coded Blockchain with C-PBFT Consensus Protocol

FSC: File Storage in Coded Blockchain with C-PBFT Consensus ...

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Yu, Ruize Yang, Changlin Liu, Ying Zhongyuan University of Technology School of Computer Science China Sun Yat-sen University School of Software Engineering China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Secure file storage and distribution is a key challenge in the information era. It is important to ensure the files are not tampered or eavesdropped during storage and transmission. A promising solution is to store the files in blockchain. However, the massive blockchain data makes it hard to operate a file blockchain in resource restricted end devices. To this end, this paper proposes a File Storage in Coded Blockchain (FSC) solution that leverage the advantage of error correction code. In particular, the block data can be encoded and distributed stored at different nodes to reduce the storage requirement. In addition, this paper propose a Coding Practical Byzantine Fault Tolerant (C-PBFT) consensus protocol to distribute the coded information with reduced communication overhead. Simulation results show that the proposed FSC significantly reduces the storage requirement as compared with traditional blockchain technologies. © 2022 IEEE.

关键词： Blockchain

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DRRS-BC:Decentralized Routing Registration System Based on Blockchain

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1868-1876页

作者： Huimin Lu Yu Tang Yi Sun College of Computer Science and Technology Qingdao UniversityQingdao 266071China School of Engineering Kyushu Institute of TechnologyKitakyushu 804-8550Japan School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

The border gateway protocol(BGP)has become the indispensible infrastructure of the Internet as a typical inter-domain routing ***,it is vulnerable to misconfigurations and malicious attacks since BGP does not provide enough authentication mechanism to the route *** a result,it has brought about many security incidents with huge economic *** solutions to the routing security problem such as S-BGP,So-BGP,Ps-BGP,and RPKI,are based on the Public Key Infrastructure and face a high security risk from the centralized *** this paper,we propose the decentralized blockchain-based route registration framework-decentralized route registration system based on blockchain(DRRS-BC).In DRRS-BC,we produce a global transaction ledge by the information of address prefixes and autonomous system numbers between multiple organizations and ASs,which is maintained by all blockchain nodes and further used for *** applying blockchain,DRRS-BC perfectly solves the problems of identity authentication,behavior authentication as well as the promotion and deployment problem rather than depending on the authentication ***,it resists to prefix and subprefix hijacking attacks and meets the performance and security requirements of route registration.

关键词： Blockchain decentralized routing protocol routing registration

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SMIFIER: A Smart Contract Verifier for Composite Transactions 34

SMIFIER: A Smart Contract Verifier for Composite Transaction...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Dong, Yu Li, Yue Cui, Dongqi Gao, Jianbo Guan, Zhi Chen, Zhong School of Computer Science Peking University China National Engineering Research Center for Software Engineering Peking University China

ISBN: (纸本)1891706543

Ensuring functional correctness of smart contracts is a pressing security concern to blockchain-based systems. With the development of blockchain application, the trading scenarios and function implementation of smart contracts have become increasing complex, containing several interacted contracts or related functions. However, the existing contracts verifiers for proving functional correctness focus on verifying isolated contract or function but ignore the interactions between them, which makes it difficult to verify correctness of composite transactions, i.e., complex transaction scenarios that invoke multiple contracts or trigger a set of transactions. In this paper, we present SMIFIER, a formal verification tool for smart contracts to prove functional properties in composite transactions. SMIFIER defines a set of specifications for composite transactions and can automatically specify properties in these multiple complex transactions. Based on states extraction and mapping, SMIFIER translates annotated Solidity program into Boogie program and verifies relations between functions and properties for interacted contracts. Our experimental evaluation on 12 real-world projects and 65 properties, demonstrates that SMIFIER is practically effective in ensuring functional correctness of properties in composite transactions. © 2022 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Smart contract

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Near-Linear Time Approximation Algorithms for k-means with Outliers 41

Near-Linear Time Approximation Algorithms for k-means with O...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Junyu Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio ∆ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time O(ndk log log n/∊2) with O(k/∊) centers opened and (1 + ∊)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/∊)-approximate solution can be obtained in time O(ndk log log n/∊2 + dpoly(k, 1/∊) log(n∆)) with (1 + ∊)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem. Copyright 2024 by the author(s)

关键词： K-means clustering

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On Efficient Packet Batching and Resource Allocation for GPU based NFV Acceleration 31

On Efficient Packet Batching and Resource Allocation for GPU...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zeng, Deze Zhu, Andong Gu, Lin Chen, Quan Guo, Minyi School of Computer Science China University of Geosciences Hubei Wuhan China Nanjing University State Key Lab. for Novel Software Technology Nanjing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350399738

Network Function Virtualization (NFV) has already become an essential technology for improving the scalability and flexibility of modern computer networks. The performance gap has become the main issue that impedes the development of NFV. GPUs, with massive parallel processors, are advocated to accelerate the Virtualized Network Functions (VNFs). However, the special architecture and workflow of GPUs introduce new challenges, especially on the batched processing, and resource allocation. In this paper, we propose GPU-based NFV Acceleration framework (GNFA) with an efficient packet batching and resource allocation solution. Considering the increased latency caused by the accumulation of the GPU kernel invoking overhead, we first invent a latency reduction mechanism called SM Performance Compensation (SPC). A Partition and Adjustment based Batching and Resource Allocation (PABARA) algorithm that jointly considers batch size tuning and GPU thread allocation is also proposed. We have practically implemented GNFA and extensively evaluated its performance on some well-known VNFs. The experiment results show that GNFA can effectively promote the GPU resource utilization and improve the NFV performance in terms of per-packet latency. © 2023 IEEE.

关键词： Graphics processing unit

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