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Campus safety monitoring system based on deep learning

Campus safety monitoring system based on deep learning

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Qianying Zhang Jin Hang Boxuan Zhang Mengzhong Wei Quanyin Zhu Xiang Li Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (纸本)9781665428903

In view of the safety hazards existing on campus, a campus safety detection system based on deep learning behavior recognition, license plate recognition and speed detection is proposed and designed. The design divides the campus safety monitoring system into three functional modules: hazard behavior identification, license plate tracking and vehicle speed measurement, and on this basis, designs the database of storage information and the warning system of speed too fast to realize the monitoring function of campus safety.

关键词： Deep learning Databases Licenses Alarm systems Hazards Velocity measurement Optical character recognition software

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SWEEP: Adaptive Task Scheduling for Exploring Energy Performance Trade-offs

SWEEP: Adaptive Task Scheduling for Exploring Energy Perform...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Jing Chen Madhavan Manivannan Bhavishya Goel Miquel Pericàs Department of Computer Science and Engineering Chalmers University of Technology

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Energy efficiency is becoming a major concern when running parallel computing systems owing to its impact on system reliability and operating cost. Recent works, that focus on energy efficient execution of task-based parallel applications on multi-core systems, leverage a subset of architectural features (core asymmetry, CPU DVFS and memory DVFS) and application attributes (inter-task parallelism, intra-task parallelism and task characteristics) to achieve this goal. More importantly, they have a fixed target metric and do not provide the flexibility to explore energy performance trade-offs (EPTO). We propose SWEEP, a task scheduler that leverages all the aforementioned architectural knobs and application attributes to facilitate EPTO exploration. SWEEP, at a high level, uses a combination of models and heuristics and works by splitting application execution into high parallelism and low parallelism phases. It then uses an adaptive task distribution algorithm, specific to the phase type, that leverages model-based predictions to determine the best task schedule and the DVFS settings for the phase. Moreover, SWEEP is able to flexibly target various EPTO metrics, a feature that is not supported by other proposals. Our evaluation shows that SWEEP achieves 19.9%, 36.4% and 9.5% reduction on average in terms of EDP, ED 2 P and E 2 DP compared to the best performing state-of-the-art.

关键词： Measurement Adaptation models Schedules Multicore processing Parallel processing Predictive models Prediction algorithms

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An Intelligent Identification and Detection Method for Forest Pests Based on YOLOv5s Algorithm

An Intelligent Identification and Detection Method for Fores...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Zhihong Wang Zhanhao Shi Honglei Wang Fengju Bu Tian Liang Xingbo Zhang Shandong Agriculture and Engineering University Jinan Landscape and Forestry Science Research Institute Jinan China

ISBN: (纸本)9781665428903

Accurate identification of pests is the basis of forestry pests prevention and management. This paper uses the YOLOv5s algorithm to train the pest intelligent identification model by using the cosine annealing learning rate to adjust the learning rate during the training process. The model can identify 15 common forestry pests. When their different physiological forms such as eggs, larvae, pupae and adults are concluded, the model can identify a total of 39 categories accurately.

关键词： Training Performance evaluation Heuristic algorithms Transfer learning Training data Forestry Physiology

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A Review of DDoS Attacks and its Countermeasures in Cloud computing 5

A Review of DDoS Attacks and its Countermeasures in Cloud Co...

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5th international Conference on Information Systems and Computer Networks, ISCON 2021

作者： Varma, S.Asha Reddy, Karri.Ganesh VIT-AP University School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9781665403412

In the present scenario, cloud computing has become a marketable technology that allows consumers to easily access resources through the internet, based on pay per use model. These resources are provided as services to the customer. Infrastructure as a Service, Platform as a Service, and Software as a Service are the 3 service models provided by the cloud. With the huge advancement in cloud computing technologies, it is widely used by many business applications, industry applications, Enterprise organizations that are committing to multi-cloud architectures, and finally, most IT expenditures are based on the cloud. However, security is the key challenge that needs more concern. According to recent studies, the most critical security concern that is confronted by cloud users is resource availability. This availability issue is mostly affected by the distributed Denial of service attack. The distributed Denial of service attack is an enhanced model of Denial of service. The main intention behind the distributed Denial of Service attack is to bring down a specific service by flooding the servers and network with malformed packets which leads to the unavailability of resources to legitimate users in a cloud. Recently, cloud infrastructures also have been severely impacted by denial of service attacks. The attacker always tries to break the security at the infrastructure level as it provides the basic computing infrastructure for all cloud delivery models. This paper frames the cloud computing services and distributed Denial of Service attack scenarios and their different defense mechanisms. © 2021 IEEE.

关键词： Denial-of-service attack

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Quantile Regression Method of House Price Index for Newly Building Condominiums in China

Quantile Regression Method of House Price Index for Newly Bu...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Ai Lirong He Saiqi Jin Shengping Wuhan Institute of Shipbuilding Technology Wuhan China School of Science Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665428903

The house price index plays a very important role in the real estate economy due to it is an indicator of real estate price changing. Aiming at the compilation model of China's new ordinary residential housing price index based on the matching set of upright adjacent floors, the parameter estimation method is improved, and the ordinary least square regression (OLS) is replaced by quantile regression to eliminate the extreme sensitivity of least square regression to outliers. This paper puts forward the quantitative index to evaluate the advantages and disadvantages of different methods to compile house price index, and makes an empirical analysis of the above ideas based on the loan data of commercial banks. The conclusions we got are more consistent with the actual results, more stable, and in line with the requirements of the third generation of housing price index compilation methods.

关键词： Sensitivity Parameter estimation Buildings Indexes distributed computing Floors business

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Transform Your Network with an Elastic Cloud Platform for Enhanced Connectivity and Scalability

Transform Your Network with an Elastic Cloud Platform for En...

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Technological engineering and its applications in Sustainable Development (ICTEASD), international Conference for

作者： Bhupendra Malviya Hassan M. Al-Jawahry Vinithra Banu. T Rajesh Pant N. Esanmurodova Neeraj Varshney Gummagatta Yajaman Vybhavi Department of Computer Science & Engineering IES College of Technology Bhopal Madhya Pradesh India The Islamic University Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Uttaranchal Institute of Management Uttaranchal University Uttarakhand India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Computer Engineering and Applications GLA University Mathura India GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

In the present dynamic and carefully determined scene, the change of organization foundation is imperative for associations trying to stay serious, lithe, and responsive. This theoretical investigates the urgent job of flexible cloud stages in reshaping network design, zeroing in on their effect on availability and adaptability. It [15] likewise digs into the benefits these stages get and their importance contemporary business environments. The crucial job of an organization testbed with high adaptability couldn't possibly be more significant with regards to testing and assessing new organization innovations. Scientists and designers the same end up needing an organization testbed that offers the capacity to redo network probes request. While some current distributed computing stages miss the mark as far as organization customization, others, which are committed organization trial stages, frequently need helpful virtual machine the executives capabilities. In this examination paper, we present a primer execution of the Flexible Tweaked Cloud Organization (ECCN) stage, which is based upon the OpenStack project. ECCN gives distributed computing the board as well as supports the OpenFlow convention. This novel testbed addresses the basic requirement for quick, on request organization of organization assets inside a server farm. These assets incorporate virtual machines, load balancers, and switches.

关键词： Cloud computing Technological innovation Force Organizations Transforms Virtual machining Security

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on engineering Service-Oriented applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD symposium, ESOCC 2020 EU Projects Track which took place at 8th European Conference on Service-Oriented and Cloud computing, ESOCC 2020

1st International Workshop on Edge Adoption and Migration, E...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on engineering Service-Oriented applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD symposium, ESOCC 2020 EU Projects Track which took place at 8th European Conference on Service-Oriented and Cloud computing, ESOCC 2020

ISBN: (纸本)9783030719050

The proceedings contain 18 papers. The special focus in this conference is on Edge Adoption and Migration. The topics include: Towards Resolving Security Smells in Microservice-Based applications;towards Citizen-Centric Marketplaces for Urban Sensed Data;DevOps and Quality Management in Serverless computing: The RADON Approach;5G-CARMEN: Service Continuity in 5G-Enabled Edge Clouds;Services computing for Cyber-Threat Intelligence: The ANITA Approach;Quality Assurance of Heterogeneous applications: The SODALITE Approach;fogProtect: Protecting Sensitive Data in the computing Continuum;intelligent Monitoring of Virtualized Services;edge computing Simulation Platforms: A Technology Survey;platforms for Serverless at the Edge: A Review;formal Modeling and Simulation of Collaborative Intelligent Robots;virtual Machine Placement for Edge and Cloud computing;Cloud-Edge Microservice Architecture for DNN-based distributed Multimedia Event Processing;modelling Service-Oriented Systems and Cloud Services with Heraklit;an Evaluation of Frameworks for Microservices Development;mining the Architecture of Microservice-Based applications from their Kubernetes Deployment.

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A study of the influence of different opinion leaders on the evolution of followers' views in social e-commerce networks

A study of the influence of different opinion leaders on the...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Jianhong Jiang Li Wang School of Business Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665428903

In the recently emerged social e-commerce platforms, online opinion leaders play a crucial role. Based on the finite trust model in opinion dynamics and combined with the actual situation of social e-commerce platforms and research literature, this paper divides opinion leaders into ‘Big-V’ type opinion leaders and authoritative opinion leaders, and divides followers into ordinary followers and fans, and constructs different opinion leaders in social e-commerce networks based on bounded trust and self-confidence degrees - the follower opinion evolution model. The influence of different opinion leaders on the evolution of followers' views is explored through multi-agent simulation. It is found that opinion leaders play an important role in the aggregation of views, and the breadth of follower coverage of ‘Big-V’ type opinion leaders makes them more influential and able to gain more strong supporters than authority-type opinion leaders.

关键词： Fans Emulation Electronic commerce distributed computing business

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ST-GCN: A Spatiotemporal Graph Convolution Neural Network for EEG Motor Imagery Signal Decoding

ST-GCN: A Spatiotemporal Graph Convolution Neural Network fo...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Jingzhou Xu Jun Qi Junqing Zhang Yong Yue Tingting Zhang Jianjun Chen Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK School of Future Technology Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Motor imagery (MI) is a mental process extensively used in the experimental paradigm for brain-computer interfaces (BCIs) across various basic science and clinical research studies. Despite its widespread use, accurately decoding intentions from MI poses significant challenges due to the complex nature of brain patterns and the limited sample sizes typically available for machine learning. This paper introduces a Spatiotemporal Graph Neural Network (ST-GCN) designed for MI classification. First, the spatial-temporal convolution layer is used to extract features from raw EEG data, where mixed depthwise convolution extracts temporal features, followed by spatial filtering convolution that decomposes the EEG signal. A graph convolution module employing the max relative aggregator is then utilized to explore the relationships between the spatially decomposed EEG components. In the final step, under the combined supervision of cross-entropy and our proposed channel selection loss, the ST-GCN achieves feature extraction that enhances interclass dispersion and intraclass compactness. We compare ST-GCN with several benchmark EEG decoding methods on two MI datasets: the BCI Competition III Dataset IVa and the BCI Competition IV Dataset 1. ST-GCN outperforms the deep learning benchmark methods by achieving an accuracy of 78.11% and 71.94%, respectively, in 10-fold cross-validation.

关键词： Convolution Benchmark testing Feature extraction Motors Electroencephalography Graph neural networks Decoding Spatiotemporal phenomena Data mining Biological neural networks

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Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks

Efficient Detection of DDoS Attacks in E-Government Clouds U...

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international Conference on ICT in business Industry & Government (ICTBIG)

作者： A. Bixapathi H. Mohammed Ali R Maranan Sudhakar AVV Beschi I S M. Ramya Department of Computer Science Engineering -Data Science Geetanjali College of Engineering and Technology Department Mechanical Engineering SRM Institute of Science and Technology-Ramapuram Campus Chennai Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Electrical and Electronics Engineering SR University Warangal Telangana Department of Computer Applications St. Joseph's College (Arts & Science) Chennai Division of Research and Development Lovely Professional University Jalandhar Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. These services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. The selected features are selected using the Energy Valley Optimizer. These optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. This approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud computing Neural networks Machine learning Denial-of-service attack Feature extraction Information and communication technology Reliability Computer crime Optimization Electronic government

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