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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Simple Multi-Resolution Representation Learning for Human Pose Estimation

Simple Multi-Resolution Representation Learning for Human Po...

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international conference on pattern recognition

作者： Trung Q. Tran Giang V. Nguyen Daeyoung Kim School of Computing KAIST Daejeon South Korea

Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. the accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. the first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset. the code is made publicly available at https://***/tqtrunghnvn/SimMRPose.

关键词： Heating systems Image segmentation Image resolution Image recognition Surveillance Pose estimation Computer architecture

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Network Traffic Prediction Method Based on Time Series Characteristics 10th

Network Traffic Prediction Method Based on Time Series Chara...

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： Huo, Yonghua Song, Chunxiao Gao, Sheng Yang, Haodong Yan, Yu Yang, Yang The 54th Research Institute of CETC Shijiazhuang050000 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811584619

With the continuous development of computer networks in recent years, the scale and types of services carried by the network are increasing. Accurate traffic prediction results provide the main support and reference basis for network operation and maintenance functions such as network attack detection. Since network traffic has certain dynamics, continuity, and long correlation and self-similar characteristics, the artificial intelligence method is generally used for network traffic prediction. Among them, the recurrent neural network has a short-term memory performance and has a good prediction effect for time series data such as network traffic. However, when the time series span is relatively long, the problem of gradient disappearance or gradient explosion may occur, so further optimization is required. In this paper, we propose a network traffic prediction method based on parameter pre-training of clockwork neural network. this method is first based on CW-RNN, and then introduces the differential evolution algorithm to pre-train the clock parameters. At the same time, the differential evolution algorithm is further improved by changing its crossover factor and mutation factor to improve the accuracy of its convergence. through computer simulation, the flow prediction method proposed in this paper can obtain accurate prediction results. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Blockchain: A Healthcare Perspective

Blockchain: A Healthcare Perspective

引用

international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Barkha Kakkar Prashant Johri School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665439718

Blockchain can be considered chain connecting blocks with a hash, grouped. Each contains its independent data and reference of the previous linked data. It creates a network controlled by the participants as to what they want to store and with whom they want to share. Blockchain technology can restructure the healthcare sector and upgrade the current situation, as it is secure, transparent, and reliable and stores real-time data. It can benefit healthcare by transforming the documentation of health records to electronic health records and remote patient monitoring sharing of these records to the concerned authorities (like government, hospitals, pharmaceutical industry, medical clinics, and insurance companies) using smart contracts. Blockchain provides an environment to keep medical data safe secure. It can keep data incorruptible, decentralized, and transparent log, allowing Blockchain’s extensive use for security applications. In this paper, the authors had discussed the working and features of Blockchain, implementation of Blockchain in healthcare, various Blockchain enabled healthcare system, blockchain model and EHR management, and various challenges in Blockchain.

关键词： Industries Hospitals Smart contracts Redundancy Medical services Real-time systems Blockchains

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Uses of Drones In Fighting COVID-19 Pandemic

Uses of Drones In Fighting COVID-19 Pandemic

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Kavita Gupta Sandhya Bansal Rajiv Goel University Institute of Computing Chandigarh University Chandigarh India MMEC MMDU India Government College Naraingarh India

ISBN: (纸本)9781665439718

COVID-19 outbreak has been faced by every country across the globe. Its affects transmit through direct or indirect contact of infected person with a healthy person. So, isolation is one of the mechanism for prevention from it. Now a day’s advancement in technology is playing a great role in fighting against COVID-19. Uses of drone is one of them. Drones can offer a large number of services in this era. Drone or UAV’s not only minimizes the risk of contact, they had been used for aerial monitoring of containment or curfews areas, for evaluation of post-massive epidemic contagious diseases, for reaching inaccessible areas and many mores. this study, presents the utilization of drones in various areas in fighting against COVID-19 pandemic and various technologies that assists these drones. Various challenges in the implementation of contactless deliveries using drones have been presented.

关键词： COVID-19 Pandemics Market research Viruses (medical) Monitoring Drones

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Risk Management of Fog computing for Improving IoT Security

Risk Management of Fog Computing for Improving IoT Security

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Shigeaki Tanimoto Mari Matsumoto Teruo Endo Hiroyuki Sato Atsushi Kanai Faculty of Social System Science Chiba Institute of Technology Chiba Japan Faculty of Liberal Arts Osaka Shoin Women&#x0027 s University Osaka Japan Information Technology Center The University of Tokyo Tokyo Japan Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9781665424219

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. the first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. the main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. the proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

关键词： Cloud computing Protocols Electric breakdown Delays Internet of things Security Risk management

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Cloud Advisor Bot: Towards a Chatbot to Enable Efficient Cloud Assessments

Cloud Advisor Bot: Towards a Chatbot to Enable Efficient Clo...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Emanuel Hofer Stella Gatziu Grivas Claudio Giovanoli School of Business FHNW University of Applied Sciences and Arts Northwestern Switzerland

ISBN: (纸本)9781665424219

Increased consciousness of cloud computing capabilities leads to greater adoption of the cloud. For many businesses, cloud computing represents an attractive cost-saving option for the establishment and maintenance of IT resources. Decision-makers are often confronted with multi-criteria decision issues regarding cloud service and deployment model selection and do not have the required technological and business-related background. this work aimed at developing a conversational decisions advisor in the form of the Cloud Advisor Bot to support complex assessment of cloud needs. the literature review revealed a gap in this domain as there is no pre-existing research in the area of chatbots that support the cloud assessment process. Based on the literature reviews as well as further research, it was investigated which elements needed to be developed in order to implement a prototype of the Cloud Advisor Bot. To achieve this, design science research was applied to iteratively develop the solution. the evaluation has shown that the prototype of the Cloud Advisor Bot can be successfully used in order to conduct an assessment regarding a possible cloud deployment scenario. the paper also proposes further developments in order to improve the solution and make it viable for the future

关键词： Measurement Cloud computing Solid modeling Analytical models Bibliographies Prototypes Oral communication

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Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Open-World Group Retrieval with Ambiguity Removal: A Benchma...

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international conference on pattern recognition

作者： Ling Mei Jianhuang Lai Zhanxiang Feng Xiaohua Xie School of Electronics and Information Technology Sun Yat-sen University China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China School of Data and Computer Science Sun Yat-Sen University China

ISBN: (纸本)9781728188089;9781728188096

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. this paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. the open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. the open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

关键词： Visualization Benchmark testing Cameras Feature extraction Robustness pattern recognition Probes

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ESACR: An Energy Saving Approach from Cloud Resources for Green Cloud Environment

ESACR: An Energy Saving Approach from Cloud Resources for Gr...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Shailesh Saxena Mohammad Zubair Khan Ravendra Singh MJP Rohilkhand University Bareilly India Taibah University Medina KSA

ISBN: (纸本)9781665439718

Cloud is most recognized environment of computing due to the widely availability of its services to the users. Cloud data centers have adopted virtualization of resources technically known as virtual machines (VMs) for the effective and efficient computation of services. But power consumption through the cloud data centers (CDCs) highlighted as one of the major issues responsible to reduce the quality of services of Cloud computing. Because a large number of electronic resources like processing nodes, servers, disk storage, and networking nodes (i.e. switch/routers) in CDC consume a high degree of energy during computation. Energy is also consumed by cooling plants to cool the data centers as it produces a huge amount of heat (CO 2 ) surrounding it. Due to the high energy consumption, the cloud providers pay a high cost of energy computation and it also contributes more carbon emissions to the atmosphere. Reducing energy consumption and CO 2 emission is a major challenge in turning the computation from cloud computing to green computing. So, our main focus in this paper is to develop an energy-saving approach for cloud computing based on resource management. Due to variations in the request of services by the users, the demand for resources also varies during the computation. the amount of users’ request are not always same for all 24 hours in a day. Some period of time it is very low and at that period of time, many resources become idle and consume some fixed amount of energy that is a wastage of energy. In this manner, the proposed ESACR approach first define active (required) and idle (non-required) resources as per the users’ request for services at any instance; after that, it converts the idle resources into an energy-saving mode (i.e., turn OFF) unless it is not required for the service of users’ request. So that the saving of energy will increased, if at any instance all non-useable resources is define in OFF mode.

关键词： Cloud computing Data centers Energy consumption Switches Quality of service Turning Virtual machining

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3D Dental Biometrics: Automatic Pose-invariant Dental Arch Extraction and Matching

3D Dental Biometrics: Automatic Pose-invariant Dental Arch E...

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international conference on pattern recognition

作者： Xin Zhong Zhiyuan Zhang Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Fusionopolis Singapore Digital Manufacturing and Design Centre Singapore University of Technology and Design Singapore

ISBN: (纸本)9781728188089;9781728188096

A novel automatic pose-invariant dental arch extraction and matching framework is developed for 3D dental identification using laser-scanned dental plasters. In our previous attempt [1]-[5], 3D point-based algorithms have been developed and they have shown a few advantages over existing 2D dental identifications. this study is a continuous effort in developing arch-based algorithms to extract and match dental arch feature in an automatic and pose-invariant way. As best as we know, this is the first attempt at automatic dental arch extraction and matching for 3D dental identification. A Radial Ray Algorithm (RRA) is proposed by projecting dental arch shape from 3D to 2D. this algorithm is fully automatic and fast. Preliminary identification result is obtained by matching 11 postmortem (PM) samples against 200 ante-mortem (AM) samples. 72.7% samples achieved top 5% accuracy. 90.9% samples achieved top 10% accuracy and all 11 samples (100%) achieved top 15.5% accuracy out of the 200-rank list. In addition, the time for identifying a single subject from 200 subjects has been significantly reduced from 45 minutes to 5 minutes by matching the extracted 2D dental arch. Although the extracted 2D arch feature is not as accurate and discriminative as the full 3D arch, it may serve as an important filter feature to improve the identification speed in future investigations.

关键词： Matched filters three-dimensional displays Biometrics (access control) Shape Filtering algorithms Feature extraction Dentistry

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Attentive Part-aware Networks for Partial Person Re- identification

Attentive Part-aware Networks for Partial Person Re- identif...

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international conference on pattern recognition

作者： Lijuan Huo Chunfeng Song Zhengyi Liu Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Artificial Intelligence ResearchChinese Academy of Sciences Jiaozhou Qingdao China School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences (UCAS)

Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce a part-aware learning method for partial person re-identification. On the one hand, we adopt data augmentation operation to enrich the training data and improve the robustness of the model. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-ID task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. through calculating feature distances between the partial image and the cropped holistic gallery images, the model can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re- ID benchmarks and achieve state-of-the-art performance.

关键词： Training Learning systems Image recognition Semantics Training data Robustness Data models

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