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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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On the Scalability Analysis of computing-Offloading Edge computing 10th

On the Scalability Analysis of Computing-Offloading Edge Com...

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10th international conference on Frontier computing, FC 2020

作者： Wang, Sikai School of Economics Shanghai University Shanghai China

ISBN: (纸本)9789811601149

Edge computing and fog computing are promising technology to allow IoT devices and mobile devices offload complex computing to powerful infrastructure, reducing service latency and improving battery performance. Practical system architecture is primary challenge due to heterogeneity and huge quantity of edge devices. However, many of prior systems rely on computing-intensive optimization procedure and global state information of devices, leading to poor scalability potentially. In this paper, we propose a metric to indicate the coupling degree of edge computing platform and study how it affects platform performance. this model can be viewed as an attempt to study the scalability of edge computing. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Enabling Delay-Sensitive IoT Application by Programmable Local 5G Edge 10

Enabling Delay-Sensitive IoT Application by Programmable Loc...

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10th IEEE international conference on Cloud Networking (IEEE CloudNet)

作者： Amemiya, Koichiro Nakao, Akihiro Fujitsu Ltd Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665435383

IoT services collect sensor and multimedia data from edge devices for capturing the status of the physical world. Delay-sensitive traffic, especially for monitoring and controlling the edge devices, should be transferred and processed in a priority manner even if congestion in the network occurs because of the system resource sharing with data-intensive and delay-tolerant traffic. A local 5G system is promising for achieving delay-sensitive IoT services because it enables the local 5G operator to control the programmable local 5G system and service level by themselves. But it isn't easy to install novel congestion control protocols to the non-programmable system components other than the local 5G system, such as proprietary IoT devices and wide-area networks operated by network carriers. Our contribution is three-fold: First, we propose a traffic control method for delay-sensitive IoT services installed only in 5G UPF or edge routers in DN without modifying IoT devices or controlling the wide-area network. It controls the latency of delay-sensitive traffic by classifying the delay-sensitive and delay-tolerant traffic, adding delays to, and modifying the receive window size in the packets of the delay-tolerant traffic. Second, we propose an implementation architecture for the programmable Whitebox switches utilizing BPF/XDP functionality. Finally, we evaluate our proposed method. the evaluation result shows that our proposed method keeps the latency of delay-sensitive traffic within the required latency for single and multiple Local 5G locations that share the obscure wide-area network.

关键词： edge computing IoT congestion control

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Studying the role of data quality on statistical and neural machine translation 10

Studying the role of data quality on statistical and neural ...

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10th IEEE international conference on Communication Systems and Network Technologies, CSNT 2021

作者： Arora, Karunesh Kumar Tomar, Geetam S. Agrawal, Shyam S. Speech and Natural Language Processing Centre for Development of Advanced Computing Sector 62 Noida India Rajkiya Engineering College UP Sonbhadra India KIIT Group of Institutions Sohna Road Bhondsi Gurugram India

ISBN: (纸本)9780738105239

Statistical and Neural Machine translation techniques are based on the parallel data used for training models. the general belief is that more training data would result in better models. We studied the available corpora and ambient noises present in them. It revealed that the available data is highly noisy. the paper describes various types of noises present in there and how these are identified. Different types of noise filters are developed and normalization processes have been applied on the corpora. Statistical and neural machine translation models are trained to study the impact of cleaning of noisy data. We performed experiments with noisy data and with cleaned data after discarding noisy data from the training corpus. Standard test set WMT-14 has been used for performing evaluation. the quality of machine translation has been measured through BLEU scores. It was observed that even after discarding a significant volume of noisy data, the models without noisy data performed better than the corpus containing noises. It proves that quality of data has significant impact and mere having huge piles of uncleaned data in not a good choice. the test case presented here is for English-Hindi language pair. It also shows a path that for low resource language pairs, paying attention to the quality of data would bring returns in form of better translation performance. As the noises discussed in paper are general in nature, the findings should be true for any other Indian language pair also, due to inherent similarity among Indian languages. © 2021 IEEE.

关键词： Neural machine translation

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Flamingo Search Algorithm and Its Application to Path Planning Problem 4

Flamingo Search Algorithm and Its Application to Path Planni...

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2021

作者： Wang, Zhiheng Liu, Jianhua School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA. © 2021 ACM.

关键词： Constrained optimization

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DECH: A Novel Attack pattern of Cloud Environment and Its Countermeasures 5

DECH: A Novel Attack Pattern of Cloud Environment and Its Co...

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5th IEEE international conference on Cryptography, Security and Privacy (CSP)

作者： Gao, Haoyu Li, Leixiao Lin, Hao Wan, Jianxiong Deng, Dan Li, Jie Inner Mongolia Univ Technol Coll Data Sci & Applicat Inner Mongolia Autonomous Reg Engn & Technol Res Hohhot Peoples R China

ISBN: (纸本)9781728186214

Cloud computing has become more and more vital in digital world as a basic technique of many services. As the foundation of cloud computing, the cloud platform hosts various cloud services running on it and providing interfaces to cloud users. thus, it becomes a high-value target in attacker's eyes. this paper proposes a novel attack pattern named DECH for intruding and controlling a cloud platform standing on the side of attackers. From the defenders' perspective, this paper points out the key measures to enforce cloud service security are to restrict the Trust computing Base (TCB) size and strengthen the isolation between cloud components. then we provide countermeasures against "Detect Escape Control and Harvest" (DECH) based on the combination of Software Guard Extensions (SGX) and Trust Domain Extensions (TDE). this research provisions references for building secure cloud services.

关键词： cloud security attack pattern sandbox escape software guard extension trust domain execution

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Mask R-CNN-Based System for Automated Reindeer recognition and Counting from Aerial Photographs 1

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15th international conference on pattern recognition and Information Processing, PRIP 2021

作者： Mikhailov, Vladimir Valentinovich Sobolevskii, Vladislav Alekseevich Kolpaschikov, Leonid Aleksandrovich St. Petersburg Federal Research Center of the Russian Academy of Sciences 39 14th line V.O. Saint-Petersburg199178 Russia Directorate of Taimyr Nature Reserves Talnakhskaia 22 Entrance 2 Norilsk663300 Russia

ISBN: (数字)9783030988838

ISBN: (纸本)9783030988821

the complexity of recognition and counting of reindeer in the natural environment from photographs is determined by the variability of recognition factors. the aim of the study is to develop a methodology and software implementation, which can solve the problem of recognition and counting of reindeer as high variability objects. To date, there exist two main approaches: feature-based recognition based on a binary pixel classification and reference-based recognition using convolutional neural networks (CNN). In this case, solving the problem of feature space recognition by pixels requires a lot of specialists’ time to analyze the images, select the features and criteria for separating images and background, and choose efficient algorithms for pixel classification and deer counting. In contrast, CNN solves the task of classification directly from images, using learned neural network architectures, without any manual processing. therefore, CNN was used to solve the task. the methodology and recognition programs have been developed using CNN of the Mask R-CNN architecture. In this case, first the network is trained to recognize animals as a class from an array of MS COCO dataset images and then re-trained on an array of aerial photographs of reindeer herds. On average, in the test sample, 82% of reindeer were recognized. At the same time, there were no false positives at all. A software prototype of the recognition system based on the CNN with a web interface has now been created, and the software is operated with limited functionality. © 2022, Springer Nature Switzerland AG.

关键词： Convolution

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Explaining GBDT by Probabilistic Finite-State Automata 2021

Explaining GBDT by Probabilistic Finite-State Automata

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2021 10th international conference on computing and pattern recognition

作者： Yinkai Chen Rui Zhang Xin Qiu Xin Li Yuxin Deng Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781450390439

Explainable artificial intelligence becomes vital for human users to understand and trust the decision-making process and results of machine learning methods. Unfortunately, most machine learning models are black-box and the algorithms running behind are opaque. In this work, we propose an approach to interpreting GBDT (Gradient Boosting Decision Tree Explanation) by extracting probabilistic finite-state automata from the trained model. Our method is inspired by and built upon a previous work that extracts probabilistic automata from RNN (Recurrent Neural Networks). To adapt the approach to our situation, we propose a series of techniques to ensure that the extracted probabilistic automaton approximates the GBDT model as accurately as possible. We conduct experiments on real-world datasets and our experimental results show that our method maintains a high level of fidelity of the extracted model as the size of the given GBDT model grows.

关键词： Gradient Boosting Decision Tree Interpretable machine learning Probabilistic finite-state automata

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Facial Expression recognition Using Residual Masking Network

Facial Expression Recognition Using Residual Masking Network

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25th international conference on pattern recognition (ICPR)

作者： Luan Pham the Huynh Vu Tuan Anh Tran Res Dept Cinnamon AI Hanoi Vietnam Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam

ISBN: (纸本)9781728188089

Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking Idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. the proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets.

关键词： Facial Expression recognition Masking Idea Residual Masking Network

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Prediction research of cervical cancer clinical events based on recurrent neural network 10

Prediction research of cervical cancer clinical events based...

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10th Annual international conference of Information and Communication Technology (ICICT)

作者： Yan, Yufang Zhao, Kui Cao, Jilong Ma, Huimin Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Med Univ Affiliated Hosp 4 Shenyang 110168 Peoples R China Neusoft Corp Shenyang 110000 Peoples R China

through the research on the existing time series prediction technology, most researchers mainly make predictions based on clinical events, without thinking about whether the previous clinical process is standard or not. this paper proposes a two-stage prediction model, RNN-2-DT, based on word vector representation and integrated into the standardized judgment of the diagnosis and treatment process. the model mines standardized clinical mode which is a standard of clinical process, using the method of binary K-means. Meantime, the clinical events prediction model using gated recurrent units (GRU) based on recurrent neural network (RNN) is constructed. the experimental results indicate that, compared with the clinical processes no considering standardized judgments, our model's recall rate and mean average precision are increased by 7.2% and 4.3%, respectively. (C) 2021 the Authors. Published by Elsevier B. V.

关键词： Electronic Health Record Word Vector Representation pattern Mining Gated Recurrent Units Neural Network Cervical Cancer

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2021, 6th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2021, Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021, Workshop on Defense Applications of AI, DAAI 2021, Distributed AI for Resource-Constrained Platforms Workshop, DARE 2021, Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021, and 10th Mining Humanistic Data Workshop, MHDW 2021

17th IFIP WG 12.5 International Conference on Artificial Int...

引用

17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2021, 6th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2021, Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021, Workshop on Defense Applications of AI, DAAI 2021, Distributed AI for Resource-Constrained Platforms Workshop, DARE 2021, Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021, and 10th Mining Humanistic Data Workshop, MHDW 2021

ISBN: (纸本)9783030791568

the proceedings contain 40 papers. the special focus in this conference is on Artificial Intelligence Applications and Innovations. the topics include: Top Challenges in 5G Densification;TYPHON: Hybrid Data Lakes for Real-Time Big Data Analytics – An Evaluation Framework in the Telecom Industry;V2X Communications for the Support of GLOSA and Intelligent Intersection Applications;a Machine Learning Approach for recognition of Elders’ Activities Using Passive Sensors;analyzing Collective Knowledge Towards Public Health Policy Making;evaluating Mental Patients Utilizing Video Analysis of Facial Expressions;an Inception-Based Architecture for Haemodialysis Time Series Classification;a Lipschitz - Shapley Explainable Defense Methodology Against Adversarial Attacks;A Multimodal AI-Leveraged Counter-UAV Framework for Diverse Environments;5G-VICTORI: Future Railway Communications Requirements Driving 5G Deployments in Railways;cyber Attack Detection and Trust Management Toolkit for Defence-Related Microgrids;On the Potential of SDN Enabled Network Deployment in Tactical Environments;a First Sensitivity Study of Multi-object Multi-camera Tracking Performance;An Initial Analysis of the Shortcomings of Conventional AI and the Benefits of Distributed AI Approaches in Industrial Use Cases;distributed Data Compression for Edge Devices;PFilter: Privacy-Aware and Secure Data Filtering at the Edge for Distributed Edge Analytics;Towards a Distributed Learning Architecture for Securing ISP Home Customers;a Recommendation Specific Human Activity recognition Dataset with Mobile Device’s Sensor Data;Explainable Needn’t Be (Much) Less Accurate: Evaluating an Explainable AI Dashboard for Energy Forecasting;improving Energy Efficiency in Tertiary Buildings through User-Driven Recommendations Delivered on Optimal Micro-moments;5G-VICTORI: Optimizing Media Streaming in Mobile Environments Using mmWave, NBMP and 5G Edge computing.

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