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A Semantic Segmentation Algorithm for Distributed Energy data Storage Optimization based on Neural Networks 7

A Semantic Segmentation Algorithm for Distributed Energy Dat...

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7th IEEE international conference on Smart Cloud, SmartCloud 2022

作者： Mao, Dong Li, Zhongxu Chen, Zuge Rao, Hanyu Zhang, Jiuding Liu, Zehan New Technology Research Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China State Grid Zhejiang Electric Power Company Internet Department Hangzhou China Hangzhou Innovation Institute of Beihang University The Research Center of Big Data and Computational Intelligence Hangzhou China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

There are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. The storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. Therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. This paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. The results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space. © 2022 IEEE.

关键词： Semantic Segmentation

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Artificial intelligence Meets Tactical Autonomy: Challenges and Perspectives 4

Artificial Intelligence Meets Tactical Autonomy: Challenges ...

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IEEE 4th international conference on Cognitive Machine intelligence (CogMI)

作者： Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci EECS DoD Ctr Excellence Artificial Intelligence & Mach Washington DC 20059 USA

ISBN: (纸本)9781665474061

Artificial intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of Things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.

关键词： Tactical autonomy artificial intelligence MDO MDB Advanced Battle Management Systems (ABMS) TEV/V

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An Improved Slime Mould Algorithm with Quasi Reflection-based Learning for Global Optimization Problems 23

An Improved Slime Mould Algorithm with Quasi Reflection-base...

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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： Liu, Qingxin Qi, Qi Jia, Heming Li, Ni School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Intelligence Education Ministry of Education Haikou China

ISBN: (纸本)9781665494571

The Slime Mould Algorithm (SMA) is a swarm-based method inspired by hunting behaviors of slime mould. However, SMA often suffers from slow convergence and local minima. In this paper, we propose an improved variant of SMA, coined as QRSMA, which is enhanced with the quasi reflection-based learning approach. The new algorithm involves two major components. First, the tent map is used to increase the population diversity in the initial phase. Second, the quasi reflection-based learning is introduced to increase the ability of exploitation. Finally, the performance of QRSMA is evaluated through assorted tests including thirteen benchmark functions and one engineering design problem against other four well-known optimization algorithms. The experimental results demonstrate better performances of the proposed QRSMA in exploring capability and convergence quality than other algorithms. © 2021 IEEE.

关键词： Image segmentation Sociology Benchmark testing Behavioral sciences Statistics Optimization Convergence

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Future of Artificial intelligence and Machine Learning in Marketing 4.0 22

Future of Artificial Intelligence and Machine Learning in Ma...

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7th international conference on big data and computing, ICBDC 2022

作者： Sundararajan, Rajeswari Menon, Preetha Jayakrishnan, Balaji Vellore Institute of Technology - Business School India FLAME University Pune India

ISBN: (纸本)9781450396097

The ultimate aim of Marketing 4.0 is to use Machine-to-Machine learning for a superior Human-to-Human experience. Artificial intelligence (AI) and Machine Learning (ML) play an important role in delivering this experience. This paper addresses the transformation in marketing from the traditional method to Marketing 4.0. using AI and ML. A systematic review has been conducted in this paper that brings together the diverse topics of AI and ML and marketing4.0. The literature has been classified based on the AI and ML applications and algorithms. Based on the review, a research gap has been identified and agenda for future work has also been given in this study. Findings reveal that digital transformation in marketing affects micro/small business enterprises adversely due to a lack of funds for such investments. Surviving in these times of technological advancement may prove to be a threat for SMEs in this competitive environment. © 2022 ACM.

关键词： Marketing

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Latency Analysis of Cloud Infrastructure for Time-Critical IoT Use Cases

Latency Analysis of Cloud Infrastructure for Time-Critical I...

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2022 Applied Informatics international conference, AiIC 2022

作者： Zen, Kartinah Mohanan, Saju Tarmizi, Seleviawati Annuar, Noralifah Sama, Najm Us Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawak Kota Samarahan Malaysia University of Technology and Applied Sciences Department of Information Technology Muscat Oman

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

The time-critical Internet of Things (IoT) use cases such as driverless cars and robotic surgical arms need high bandwidth and low latency for real-time intelligent data processing and trained machine learning inference. Latency in real-time processing is influenced by many factors such as artificial intelligence (AI) computing algorithm, device processing capabilities, the frameworks, and also the distance from the cloud infrastructure. However, the geographical distance between the data origin and data processing is one of the major factors contributing to the network latency for time-critical IoT use cases. In this paper, we analyzed the latency from a particular client point based on the live data generated by their cloud data centers. The experiments were done through the big three cloud vendors, which are Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). As a result, a time-critical IoT low latency approach is proposed in this paper. © 2022 IEEE.

关键词： Edge computing

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18th international conference on Services computing, SCC 2021, Held as Part of the Services conference Federation, SCF 2021

18th International Conference on Services Computing, SCC 202...

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18th international conference on Services computing, SCC 2021, Held as Part of the Services conference Federation, SCF 2021

ISBN: (纸本)9783030965655

The proceedings contain 7 papers. The special focus in this conference is on Services computing. The topics include: A Survey: The State-of-the-Art Development of B-Learning Services;research on the New Model of ShenShan Special Cooperation Zone of Shenzhen Coordinated Development of Regional Economy;design of IoT Health Pension Scheme Based on Physiological and Behavioral Indicators for Elderly;Innovation of Teaching Mode of Comprehensive Engineering Cost Training Course Based on OBE Concept in the Era of big data;the Construction and Improvement of Digital Library Service.

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Double-Check Soft Teacher for Semi-Supervised Object Detection 31

Double-Check Soft Teacher for Semi-Supervised Object Detecti...

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31st international Joint conference on Artificial intelligence (IJCAI)

作者： Wang, Kuo Nie, Yuxiang Fang, Chaowei Han, Chengzhi Wu, Xuewen Wang, Xiaohui Wang Lin, Liang Zhou, Fan Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China Huawei Technol Co China Shenzhen Peoples R China

ISBN: (纸本)9781956792003

In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which severely restricts the performance of semi-supervised object detection. In this paper, we revisit the pseudo-labeling based Teacher-Student mutual learning framework for semi-supervised object detection and identify that the inconsistency of the location and feature of the candidate object proposals between the Teacher and the Student branches are the fatal cause of the low quality of the pseudo labels. To address this issue, we propose a simple yet effective technique within the mainstream teacher-student framework, called Double Check Soft Teacher, to overcome the harm caused by insufficient quality of pseudo labels. Specifically, our proposed method leverages teacher model to generate pseudo labels for the student model. Especially, the candidate boxes generated by the student model based on the pseudo label will be sent to the teacher model for "double check", and then the teacher model will output probabilistic soft label with background class for those candidate boxes, which will be used to train the student model. Together with a pseudo labeling mechanism based on the sum of the TOP-K prediction score, which improves the recall rate of pseudo labels, Double Check Soft Teacher consistently surpasses state-of-the-art methods by significant margins on the MS-COCO benchmark, pushing the new state-of-the-art. Source codes are available at https://***/wkfdb/DCST.

关键词： Object detection

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Proceedings - 2022 8th Annual international conference on Network and Information Systems for Computers, ICNISC 2022

Proceedings - 2022 8th Annual International Conference on Ne...

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8th Annual international conference on Network and Information Systems for Computers, ICNISC 2022

ISBN: (纸本)9781665453516

The proceedings contain 191 papers. The topics discussed include: satellite-ground integrated network architecture and key enabling technologies for 6G;research on web application anomaly intrusion detection model based on massive logs;design of a radiosonde based on the WSN technology;design and development of the cultural tourism big data platform based on internet + cloud computing;construction of precision teaching mode and teaching practice application based on big data analysis;research on the construction path of network marketing wisdom classroom in application-oriented undergraduate under the background of ‘Internet +’;implementation of cache timing attack based on present algorithm;application of UAV remote sensing technology based on GIS in the execution of tasks of the people’ s armed police;end-to-end object detection with location-sensitive cues;a 5G mmWave communication framework in vehicle-road cooperative system;design of 5G vehicle-road-cloud collaborative architecture for vehicle platooning;discussion on network information security based on cloud computing environment;a non-contact active sensor for detecting or monitoring electricity transmission state of high voltage AC power grid;and on interpretable algorithm of artificial intelligence based on information network analysis.

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Research on the end-game of DouDizhu: A method to improve AI of imperfect information game

Research on the end-game of DouDizhu: A method to improve AI...

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2022 international conference on Cloud computing, big data and Internet of Things, 3CBIT 2022

作者： Cao, Hengyang Li, Shuqin Gao, Ming Zheng, Lanzhou Beijing Information & Science and Technology University School of Computer Beijing China Beijing Information Science & Technology University Sensing & Computational Intelligence Joint Lab Beijing China Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665492256

The imperfect information game has too much hidden information in the game process, which leads to the huge information set in the game process. It is difficult to solve the problem through the mature game search algorithm. This paper takes the game guessing as the carrier, imitates the game behavior of human beings in the game remnant state, and gives a method to transform the imperfect information game into the perfect information game. Through this method, the other players' hands in the game are guessed, and the imperfect information is transformed into perfect information, so as to solve the problem that the data dimension brought by the hidden information of the game is too large Questions. As for the game of DouDizhu, in the end of game, there are 133 kinds of ending states. In this paper, the game information generated in the process of DouDizhu is studied, the appropriate representation method of game information is given, and a reasonable 3D time series convolution model is designed to extract features, and then a classification model with high accuracy of guessing cards is obtained. The model has a high accuracy of 92.1% for the final game. The perfect game search algorithm is used to solve the game's hand out strategy to improve the game AI's playing strength. © 2022 IEEE.

关键词： Behavioral research

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Artificial intelligence (AI), Blockchain, and Cryptocurrency in Finance: Current Scenario and Future Direction 2nd

Artificial Intelligence (AI), Blockchain, and Cryptocurrency...

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2nd international conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Hosen, Mosharrof Thaker, Hassanudin Mohd Thas Subramaniam, Vasanthan Eaw, Hooi-Cheng Cham, Tat-Huei Department of Accounting and Finance UCSI University Kuala Lumpur Malaysia UCSI Graduate Business School UCSI University Kuala Lumpur Malaysia

ISBN: (纸本)9783031252730

By combining artificial intelligence (AI), big data, and cloud computing, the blockchain revolutionises the production of both ascendable information technology systems and diverse applications. The combination of AI and blockchain has enormous potential for developing new financial service models that are enabled by digitalization. Although there are a few investigations on the application and conjunction of AI and blockchain, our extant literature findings on the financial services’ value of their integration are still fragmented. To close this gap, the goal of this research is to evaluate the uses and benefits of integrated AI and blockchain platforms across many financial service platforms. We further discussed the impact of decentralized cryptocurrency on financial sectors. This study revealed that AI and Blockchain have the potential of causing an increase in banking services, lending efficiency, collection and payment competency, and asset management. Furthermore, we highlight cryptocurrencies are encountering cybersecurity, mining, privacy, anonymity, and regulatory challenges that policymakers need to act on. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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