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Multi-sensor data Fusion for Accurate Prediction of Suspicious Activities in Video 3rd

Multi-sensor Data Fusion for Accurate Prediction of Suspicio...

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3rd international conference on Advancements in smart computing and Information Security, ASCIS 2024

作者： Radhika, R. Muthukumaravel, A. Chennai India Chennai India

ISBN: (纸本)9783031863011

Video analytics for surveillance enhances safety of the public across a range of public spaces, such as shopping centers, railroad stations, and daycares. Conventional closed-circuit television systems have proven to be inefficient and prone to errors as they rely heavily on human operators. The proposed intelligent video surveillance system utilizes deep learning algorithms to promptly notify the authorities of any suspicious activity. The system has been trained to identify a wide range of human actions and potential hazards. It achieves this by utilizing datasets like UCF-Crime, HMDB51, and UCF101. The architecture combines LSTM (Long Short Term Memory) with CNN (Convolution Neural Network). The CNN component effectively identifies suspicious activity by extracting important features from video frames using the Inception V3 model. The LSTM examines these characteristics to establish connections over time. The system demonstrated exceptional performance on UCF-Crime, with an F1 score of 99.1%, precision of 99.8%, recall of 98.5%, and an impressive accuracy of 99.6%, as observed during the evaluation across datasets. HMDB51 achieves an impressive F1 score of 99.1%, with precision at 98.7%, recall at 97.9%, and accuracy at 99.4%. On the other hand, UCF101 achieves a commendable F1 score of 98.6%, with accuracy at 99.2%, precision at 98.7%, and recall at 97.9%. The detection durations for UCF-Crime, HMDB51, and UCF101 are 0.25 s, 0.30 s, and 0.28 s, respectively. The proposed model streamlines the procedure of improving safety by utilizing intelligent video analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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RPCC: A Replica Placement Method to Alleviate the Replica Consistency under Dynamic Cloud 13

RPCC: A Replica Placement Method to Alleviate the Replica Co...

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2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on smart data, smartdata 2020

作者： Sun, ShenYao Wang, XianJi Zuo, Fang School of information science and technology Zhengzhou Normal University Zhengzhou450044 China School of economics Changjiang University JingZhou434023 China Henan International Joint Laboratory of Theories Key Technologieson Intelligence Networks Institute of Intelligent Network System Henan University KaiFeng475000 China

ISBN: (纸本)9781728176475

In dynamic cloud, more replicas can reduce access delay and balance the node load, but more difficulty of replica consistency and vice versa. To deal with this problem, this paper designed and implemented a dynamic replica placement method based on the node's credit risk, named RPCC, aiming to use replica placement to balance the relationship between system performance and replica consistency. To achieve its goal, RPCC uses the decentralized adaptive manner to dynamically create and place replicas. This method selects files with high popularity and low update rate to create replicas when creating new replicas. Meanwhile, it plots the credit risk map according to node load, node degree and access delay. RPCC selects optimal nodes to place replicas according to the credit map. Extensive experiments show that RPCC can obtain superior performances in consistency update probability increase around 5% on average, in access latency decrease around 10% and higher load balance than other similar methods. © 2020 IEEE.

关键词： Risk assessment

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Spatial pattern of talents and the evolution of the situation of chinese "Heihe-Tengchong Line" under the background of big data

Spatial pattern of talents and the evolution of the situatio...

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Yao, Hui Zhang, Lei Faculty of Education Yunnan Normal University Kunming China School of Tourism and Geographical Science Yunnan Normal University Kunming China

ISBN: (纸本)9789811525674

In this paper, the education level and education structure of the population of the whole country and the provinces on the northwest and southeast sides of the Heihe-Tengchong Line are systematically analyzed based on the national census data of 1964, 2000 and 2010 and the relevant data of economic and educational development of provinces. The correlation between population education level and education structure of the provinces on the northwest and southeast sides of the Heihe-Tengchong Line and regional economic development and regional higher education development is analyzed, and the factor matching and structural similarity are evaluated, so as to provide a basis for scientific regulation and control of the evolution of regional talent spatial pattern. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Population statistics

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Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support

Feature Selection for Learning to Predict Outcomes of Comput...

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2020 international conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Adedolapo, Okanlawon. Huichen, Yang. Avishek, Bose. William, Hsu. Dan, Andresen. Mohammed, Tanash. Kansas State University Department of Computer Science ManhattanKA United States

ISBN: (纸本)9781728176246

We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud. This task was cast as both supervised classification and regression learning, specifically, sequential problem solving suitable for reinforcement learning. Selecting relevant features can improve training accuracy, reduce training time, and produce a more comprehensible model, with an intelligent system that can explain predictions and inferences. We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features: linear regression, lasso, and ridge regression. Our data set represented both HPC jobs that failed and those that succeeded, so our model was reliable, less likely to overfit, and generalizable. Our model achieved an R2 of 95% with 99% accuracy. We identified five predictors for both CPU and memory properties. © 2020 IEEE.

关键词： Predictive analytics

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Sequential Recommendation with a Pre-trained Module Learning Multi-modal Information 13

Sequential Recommendation with a Pre-trained Module Learning...

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作者： Han, Tengyue Tian, Yu Zhang, Jiwei Niu, Shaozhang Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications BeiJing China Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728176475

Recommendation has already attracted increased attention in various industries, including community of scholars. Especially sequential recommendation considering time series information is improved to satisfy sequential user's needs better. Most efforts have been made in capturing users' dynamic preferences, however, the multi-modal information of items will also contribute to making recommendation. A user may be interested in a pair of shoes because of its color and style, or because of its function that is suitable for dating, so multi-modal(image, text) contains different aspects of information for items. A new three-stream framework is designed to capture user's different aspect preferences using the self-attention mechanism. The image-stream will capture user's interests about what an item looks like, the description-stream will capture user's preference about the function of an item, and the item-stream will capture the sequential preference. The key point in our method lies in that a pre-trained item presentation is utilized which has learned information from multi-modalities. Experiments are conducted by us on public datasets and the results prove that our model obtains good performance improvement over most existing sequential recommendation models. © 2020 IEEE.

关键词： multi-modal pre-trained self-attention sequential recommendation

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State Grid Yichang Power Supply Company’s Young Employee Training Plan Based on "Big data Profile"

State Grid Yichang Power Supply Company’s Young Employee Tr...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Liu, Xianjun Ji, ChengChen Zhang, Yi Yang, Sisi State Grid Hubei Electric Power Co. Ltd. Yichang Power Supply Company YichangHubei44300 China

ISBN: (纸本)9783030627423

In the 21st century, with the advent of the training model for young employees based on the "big data profile" and the development of information technology, modern enterprises have brought both opportunities and challenges. To build an excellent modern company, State Grid Yichang Power Supply Company must implement the "people-oriented" and "talent-powered enterprise" management ideas, and strengthen the training of young employees to achieve the healthy growth of new young employees and the common development of the company. This paper uses the "one person, three files" data of young employees to construct an analysis model to portray the "personal portraits" and "organizational portraits" of young employees and experts, and develop software to automatically generate training maps, and build on the basis of training maps. A two-level organization system, implementation of a number of assessment and incentive measures, and preparation of a "training navigation manual" to enhance the control and effectiveness of training management work, and to stimulate the internal driving force of young employees to participate in training. This article studies the "State Grid Yichang Power Supply Company" young staff training plan and implements a new training model, but the purpose is to modify and supplement this basic framework, and finally design a relatively complete training model. Including effective training system, internal training mechanism, triple training incentive mechanism. This article is based on the principle of "being a person first, doing things later", innovating training methods, extending training time limits, vigorously implementing a professional mentor system, and planning training projects with innovative ideas and methods. Research shows that managers and young employees respectively believe that about 73% and 64% of companies have formulated corresponding training plans before the implementation of training, accounting for the vast majority of the to

关键词： Personnel training

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Application of Computer data Processing Technology in Economic Field

Application of Computer Data Processing Technology in Econom...

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international conference on Cyber Security Intelligence and analytics, CSIA 2020

作者： Gong, Yijun School of Economics Sichuan University ChengduSichuan China

ISBN: (纸本)9783030433086

Computer technology in the development of modern society is becoming increasingly prominent. Computer data processing technology, as an important computer technology, is particularly widely used in economic field. This article analyzes the application value of data processing in the economic field. Based on this, in view of the cloud storage problem, a storage task allocation mechanism based on improved genetic algorithms and a cloud storage data allocation based on improved dynamic programming are proposed. These two algorithms have greatly improved the utilization of storage nodes and optimized system load balancing. They also expounded the computer data processing to economic field, and strengthened the analysis and research on computer processing technology. © Springer Nature Switzerland AG 2020.

关键词： Dynamic programming

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Security Issues for smart Grid EC System 4th

Security Issues for Smart Grid EC System

引用

4th international conference on intelligent, Interactive Systems and Applications, IISA2019

作者： Xu, Aidong Zhang, Yunan Jiang, Yixin Chen, Jie Li, Yushan Dong, Lian Yi, Yujun Pan, Fei Wen, Hong EPRI China Southern Power Grid Co. Ltd. Guangzhou China National Key Lab of Communication University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783030343866

As a typical industry auto control system, security issue is a necessary task in order to guarantee safe and secure grid operation. The edge devices the local data processing and storage that provide a real time control, a low latency and also can play a role of the security gate, in which the terminal authentication, data privacy and data security can be provide. Due to the terminals of the SG are resource and computing constrained devices, the encryption security measurements are hard to perform on these devices. Physical-layer (PHY) security methods use the characteristics of the channel or the device fingerprints, which can run on the edge devices. Therefore, the terminals almost do nothing, which means these kinds of PHY security methods are light weight to the terminals. This paper introduced several PHY light weight authentication methods for the smart grid based on the edge computing and illustration their efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Edge computing

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Paleomagnetic and Sedimentary Evidence for the Multi-stage Paleoenvironmental Evolution of Prydz Bay Based on Big data analytics

Paleomagnetic and Sedimentary Evidence for the Multi-stage P...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Lin, Zhongyang Han, Xibin Jin, Xianglong Yi, Liang Li, Zhenggang China Univ Geosci Coll Marine Sci & Technol Wuhan 430074 Peoples R China State Ocean Adm Inst Oceanog 2 Hangzhou 310012 Zhejiang Peoples R China Tongji Univ State Key Lab Marine Geol Shanghai 200092 Peoples R China Zhejiang Inst Geol Survey Hangzhou 311203 Zhejiang Peoples R China

ISBN: (纸本)9783030152352;9783030152345

A 324-cm-long sediment core was obtained from Prydz Bay, Antarctica, during the 29th Chinese National Antarctic Research Expedition. Based upon sediment grain size, C-14, TOC, delta C-13, and magnetism data, we show that the major magnetic minerals are ferrimagnetic pseudo single domain (PSD)-multi domain (MD) magnetite. Variations in the paleoenvironmental proxy records allow us to define four zones in the core. These zones broadly delineate climatic variations in the region since the late Early Pleistocene, with a warm period, a transitional period, and a cold period. The magnetic particle assemblage varies with glacial-interglacial cycles. Abrupt changes in particle size, TOC content, and magnetism occur at 102-90 cm depth in the core, indicating a sudden warming in the Antarctic region, signaling the onset of the Holocene. We identified three additional climatic signals in the middle part of the core (232-162 cm) that show unexpected cooling events during the warm period in Prydz Bay.

关键词： Paleomagnetism Paleoenvironment Climate change Late quaternary Prydz Bay Antarctica

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Blockchain-based Trust Management in Social Internet of Things 18

Blockchain-based Trust Management in Social Internet of Thin...

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18th IEEE international conference on Pervasive intelligent and computing (PICom)

作者： Amiri-Zarandi, Mohammad Dara, Rozita A. Univ Guelph Sch Comp Sci Guelph ON Canada

ISBN: (纸本)9781728166094

Social Internet of Things (social IoT) is a paradigm that integrates the social network concepts with IoT in which objects in the network are able to establish social relationships with each other. In these networks, to preserve privacy and security, the access to IoT resources can be handled based on social trust evaluation of the devices. This paper examines a blockchain-based trust management system for edge-enhanced IoT that uses social information of the system to strengthen the trust evaluation. Moreover, this system leverages information entropy to enhance security. We also developed a proof of concept system to show that the proposed solution can effectively assess trust factors in social IoT.

关键词： Social IoT Trust Security Privacy Blockchain smart Contract Entropy

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