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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

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Job Resource Prediction and Completion Status Prediction on Large-Scale computing Platforms 3

Job Resource Prediction and Completion Status Prediction on ...

引用

3rd international conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： He, Jiale Zhang, Tao Zheng, Yu Zhengzhou University 100 Science Avenue Gaoxin District Henan Zhengzhou450001 China Dawning Information Industry Co. Ltd. Sugon Building No.36 Zhongguancun Software Park No.8 Dongbeiwang West Road Haidian District Beijing100193 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name. © 2024 SPIE.

关键词： Forecasting

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data based decision making: value exertion and scenario conception 3

Data based decision making: value exertion and scenario conc...

引用

3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Xu, Yuanyuan Li, Xiaowen Dept.Ningbo University of Finance & Economics Zhejiang Ningbo China

ISBN: (纸本)9781510661301

The digital transformation of education reshapes the daily practice and meaning generation in the field of education and teaching, promotes teachers' teaching decision-making out of the "experience-based" value judgment mode, and combines the value advantages of "data driven", so as to make the decision-making more sufficient, refined and efficient. The value of data based decision making(DBDM) in the digital age is more to help the continuous improvement of teaching and learning, and help to promote the realization of high-quality teaching objectives in the new era. To promote the implementation of DBDM, we need to improve teachers' data literacy, strengthen the research of data based decision making mode, promote the application of multi-source heterogeneous data fusion based on scenario requirements, and create a culture of data use. © 2023 SPIE.

关键词： Decision making

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A study on 3D multimodal resonance brain tumor image segmentation model 23

A study on 3D multimodal resonance brain tumor image segment...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Dong, Lei Wang, Zu-Min Zhang, Min Gao, Bing College of Information Engineering Dalian University Dalian China School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

Automatic segmentation of tumor regions from 3D multi-modal brain images is of great reality for brain tumor diagnosis. A fully automated dual-path brain tumor MRI image segmentation model, MEMU-Net, is proposed to address the problems of complex network structure and difficulties in multiscale feature extraction in multi-modal brain tumor medical image segmentation. Firstly, the network model is constructed by referring to the U-Net model's dual-path encoding-decoding structure to enhance the network's accuracy in extracting features while retaining the advantages of the original U-Net. Secondly, the M-RepVGG module is used for upsampling and downsampling. The module can extract richer spatial features during training while reducing the number of parameters computed in the network. A simple network architecture for inference aims to improve the speed of the network. Finally, the Expectation-maximization attention is embedded before the upsampling for the better fusion of features. Experiment results on BraTS 2019 validation set can achieve Dice scores of 76.28%, 89.16%, 80.11% for enhancing tumor, whole tumor, and tumor core, respectively. © 2021 IEEE.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Smart cities Magnetic resonance imaging Network architecture

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QHash: An efficient hashing algorithm for low-variance image deduplication 23

QHash: An efficient hashing algorithm for low-variance image...

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作者： Li, Xuan Chang, Liqiong Liu, Xue School of Computer Science McGill University MontrealQCH3A0G4 Canada

ISBN: (纸本)9781665494571

Attributed to the widespread use of general artificial intelligence (AI), large-scale datasets have become a critical component for the success of AI-powered applications. While collecting a larger dataset is desirable in general, studies have revealed that many databases include duplicated images. The accumulation of redundant images will result in excessive resource usage and inefficient cloud storage utilization. To get rid of the duplicates, hashing-based methods have been developed for the problem of image deduplication. However, as demonstrated in our experiments, current approaches fail in dataset with small visual difference, such as medical images. To this end, we propose QHash, which achieves effective image deduplication on the low-variance dataset. QHash leverages Vector Quantized Variational AutoEncoder (VQ-VAE) to learn the data distribution in an unsupervised manner. In addition, hash sequences are implemented using integer-based tensor, enabling distance calculation and deduplication being processed in parallel on either CPU or GPU. Extensive experiments show that QHash outperforms other baseline approaches by at least 50%, while is about 23% memory efficient and 18% deduplication time speedup. © 2021 IEEE.

关键词： Visualization Tensors Smart cities Memory management Graphics processing units Parallel processing Lesions

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Spatial-temporal-spectral transformer for 3D human pose estimation 23

Spatial-temporal-spectral transformer for 3D human pose esti...

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作者： Wu, Yongpeng Kong, Dehui Wang, Shaofan Li, Jinghua Yin, Baocai Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9781665494571

Human motion exhibits a high spatial-temporal correlation, and further exploration of the intrinsic correlation of joint motion trajectories is beneficial to improve the performance of 3D pose estimation. Therefore, we propose a novel spatial-temporal-spectral transformer for high-quality 3D human pose estimation in videos, which mainly includes the spatial-temporal transformer at the joint level and the spectral transformer at the joint trajectory level. The former explores the dependencies of the joint level from the skeleton graphic structure and the sequence to obtain a richer feature representation. The latter explores the dependence of joint motion trajectories in the spectral domain. To obtain a more accurate 3D pose estimation of the center frame, a multi-layer stride convolution module is used to realize the estimation from the full frame to the center frame. In addition, since the 2D and 3D pose sequences have the same motion trajectory in the xy plane, we add the consistency constraint to obtain more accurate estimation results. Extensive experiments show that the proposed framework achieves state-of-the-art performance on Human3.6M. © 2021 IEEE.

关键词： Three-dimensional displays Correlation Smart cities Pose estimation Transformers Feature extraction Trajectory

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Global Cyclic Queuing and Forwarding Mechanism for Large-Scale Deterministic Networks 23

Global Cyclic Queuing and Forwarding Mechanism for Large-Sca...

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作者： Mo, Yijun Yang, Zihan Liu, Huiyu He, Tianliu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665494571

Delay and jitter sensitive industrial control and remote driving application-driven network by best-effort to deterministic service. To meet the deterministic application of delay and jitter demands, industry and academia have conducted detailed research on stream filtering shaping and queue forwarding control. However, the study seldom considers the effects of long-distance topology change, transmission delay fluctuations and short-time traffic overloads, challenging to meet deterministic requirements under large-scale backbone networks. For this challenge, this paper based on Cyclic Queuing and Forwarding, considers source-destination path properties and deterministic traffic intensity. We propose Global Cyclic Queuing and Forwarding 3-Queue. In the deterministic network simulation system based on OMNeT, we constructed the actual network topology NSFNET, which has 14 nodes and 21 links. Experiments compare queue forwarding control mechanisms such as CQF and GCQF-3. The experimental results show that under the traffic matrix with 120% strength, the delay is reduced by 23.6%, the jitter is reduced by 53.6%, and the packet loss rate is reduced by 61%. It shows that GCQF-3 can timely adjust the gate-control and queueing priority according to the deterministic traffic state, achieves the best in delay, jitter and network utilization. © 2021 IEEE.

关键词： Industries Network topology Smart cities Industrial control Packet loss Jitter Logic gates

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Design and Fabrication of Mobile Visual Emergency Command Hall Based on Virtual Reality 3

Design and Fabrication of Mobile Visual Emergency Command Ha...

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2023 IEEE 3rd international conference on data science and Computer Application, IcdsCA 2023

作者： Yong, Jiu Wei, Jianguo Wang, Yangping Dang, Jianwu Sun, Yifan Lanzhou Jiaotong University Lanzhou China Tianjin University Tianjin China Tianjin University College of Intelligence and Computing Tianjin China Lanzhou Jiaotong University Gansu Prov. Engineering Research Center for Artificial Intelligence and Graphics Image Processing Lanzhou China

ISBN: (纸本)9798350341546

Accidents and disasters occur frequently worldwide every year, causing relatively serious losses to personnel and property. Emergency command halls are a necessary response mechanism in the event of accidents and disasters. Traditional emergency command methods have problems such as poor convenience, immobility, and high cost. By combining the advantages of strong immersion, good interactivity, portability and mobility of virtual reality technology in the new generation of artificial intelligence, a mobile visualization emergency command hall is developed based on virtual reality technology. It combines existing virtual reality development platforms, helmets, glasses, handles, and other software and hardware to achieve the goals of real-time rescue, mobile visualization command, and ensuring the safety of the life and property of the public. © 2023 IEEE.

关键词： emergency command multi-person collaboration virtual reality visualization

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SA_3AM: A Novel Sentiment Analysis Approach Integrating Three-Level Attention Mechanism 23

SA_3AM: A Novel Sentiment Analysis Approach Integrating Thre...

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作者： Wang, Qingren Sun, Liang Cui, Jie Zhang, Jing School of Computer Science and Technology Anhui University Hefei China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Nowadays, social networks have become the primary ways for people to communicate in their daily lives. Due to the great value of short texts generated by social networks, sentiment analysis on them has been attracting more attention from academics and business. However, emojis, which have taken the world by storm, have been challenging sentiment analysis because of their flexible ability to convey emotions. Therefore, this paper proposes a novel sentiment analysis approach by integrating three-level attention mechanism, namely SA_3AM. Particularly, SA_3AM can be divided into three phases: i) attention-oriented word sentiment semantics learning, where SA_3AM utilizes Bi-LSTM to capture bidirectional semantic dependencies within various dimensions conveyed in pure words;ii) attention-oriented emoji sentiment seantics mining, where SA_3AM employs column max-pooling to obtain rich sentiment semantics of emojis by fusing the sentiment dependencies of words;and iii) attention-oriented microblog sentiment semantics calculating, where SA_3AM uses attention mechanism to mine implicit intentions conveyed by microblogs. A real-world dataset Weibo_SL containing 6,436 Chinese microblogs from January 2017 to July 2020 was built to evaluate our SA_3AM. Our experimental results not only show that SA_3AM outperforms the peers, but also demonstrate the feasibility of the dataset Weibo SL. © 2021 IEEE.

关键词： Training Deep learning Sentiment analysis Social networking (online) Storms Smart cities Semantics

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Recognition of Traffic Police Gestures Based on Transfer Learning 3

Recognition of Traffic Police Gestures Based on Transfer Lea...

引用

3rd international conference on Electronic Information Engineering and Computer science, EIECS 2023

作者： Lian, Li Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University College of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350316674

At present, the auto-driving technology is becoming more and more mature, but how the auto-driving car recognizes the traffic police gesture information under special road conditions has become an urgent problem to be solved. In this paper, based on transfer learning, eight kinds of gesture signals of Chinese traffic police are recognized. The dataset is "Chinese Traffic Police Gesture dataset"(CTPGD). The total loss value is 0.2174, and good results have been achieved. © 2023 IEEE.

关键词： CTPGD traffic police gesture transfer learning

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DDoS Defense Scheme Based on Machine Learning in Software-Defined Networking 23

DDoS Defense Scheme Based on Machine Learning in Software-De...

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作者： Zhong, Hong Yu, Chao Cui, Jie Sun, Xiuwen Gu, Chengjie Liu, Lu School of Computer Science and Technology Anhui University Hefei China Security Research Institute New H3C Group Hefei China

ISBN: (纸本)9781665494571

Since software-defined networking (SDN) has the ad-vantages of central control programmability and global view, it is suitable for industrial network. However, large-scale security risks such as distributed denial of service (DDoS) attacks endanger the security and reliability of services in the SDN-based system. Therefore, a comprehensive and efficient solution to this issue is crucial. This paper proposes a scheme for mitigating DDoS attack traffic deployed on an SDN application plane. The framework periodically obtains the traffic statistics of the switch ports and flow tables. After obtaining the traffic characteristics, a machine learning model is used for training. The trained model can identify benign and malicious traffic. After the attack is found, the rules are sent to the switch to control the flow and resist DDoS attacks. We use the CICDDoS2019 dataset to perform experiments. The experimental results show that compared to other schemes, the proposed scheme achieves more accurate classification. © 2021 IEEE.

关键词： Training Support vector machines Switches Machine learning Denial-of-service attack Feature extraction Control systems

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