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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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8th international Iranian conference on Signal Processing and Intelligent systems, ICSPIS 2022

作者： Kazemzadeh, Farzaneh Afsharian, Sanaz Safaei, Ali Asghar Mirzarezaee, Mitra Islamic Azad University Department of Computer Engineering Science and Research branch Tehran Iran Islamic Azad University English Department Damavand Branch Tehran Iran Tarbiat Modares University Faculty of Medical Sciences Department of Medical Informatics Tehran Iran

ISBN: (纸本)9781665476232

Following the invention of the Internet and its expansion among all the people of the world, many software products, sites, games, and even hardware was presented by scientists, programmers, and manufacturers, whose use today is one of the essential and irreplaceable needs. It has become part of people's daily lives. Along with all the ways and means of using the Internet, social networks also spread among people with great power and speed. Today, most people are familiar with social networks and use them differently according to their needs. One of the ways to spread information is using social networks. through social networks, many users can be informed about information such as news, advertisement, and many other things. this diffusion of information among users has been investigated by the problem of influence maximization to provide a solution to spread the diffusion in the social network. JACCIM algorithm, after detecting communities and selecting candidate nodes from overlapping and non-overlapping nodes based on topology criteria including Jaccard coefficient, has selected a set of k nodes. this collection has been able to maximize the diffusion of information in the network. With experiments on the proposed algorithm, comparing it with some other algorithms, and checking the results, the proposed algorithm has shown a significant impact on its execution time. © 2022 ieee.

关键词： Topology

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Real Time Virtual Yoga Tutor

Real Time Virtual Yoga Tutor

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international conference for Convergence of Technology (I2CT)

作者： Anuradha M Adithya Rao Shreyas S Sanjaya K C ECE Department PES University Bangalore India Electronics and Communications Engineering PES University Bangalore India

Articulated pose estimation in computer vision is the field of research that deals with the development of algorithms and systems that can determine the position and orientation of an articulated body, such as a human body, in 3D space. It has a wide range of applications, including motion capture, human-computer interaction, and robotics. In the context of the project, the goal is to estimate the position of human body parts and joints in each image, using a pre-trained pose estimation model, MediaPipe. the data obtained from this model is then used to explore various applications, with the focus being on building a yoga monitoring system. the system analyzes and tracks the user’s movements and postures, identifying any errors in their yoga routine. this allows the system to provide real-time feedback to the user, pointing out any inaccuracies in their body pose, so they can correct their mistakes.

关键词： computer vision three-dimensional displays Webcams Tracking Biological system modeling Pose estimation Real-time systems

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Single Bean Region of Interest Auto-Detection in a Coffee Beans Image Towards thorough Quality Inspection 8

Single Bean Region of Interest Auto-Detection in a Coffee Be...

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8th international conference on Instrumentation, Control, and Automation, ICA 2023

作者： Utomo, Gema Nuran Rakhmawati, Amalia Nadhira, Vebi Juliastuti, Endang Suprijanto, Suprijanto Institut Teknologi Bandung Master's Program in Instrumentation and Control Bandung40132 Indonesia Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung40132 Indonesia Institut Teknologi Bandung Instrumentation and Control Reaserch Group Bandung40132 Indonesia

ISBN: (纸本)9798350301274

Indonesia was declared the world's fourth-largest coffee bean producer in 2020 and yields consistently increase each year. Indonesia also has the Indonesian National Standard (SNI) 01-2907-2008, which specifies various defects with the defect scores of coffee beans that determine their quality. Common coffee bean defects listed in SNI 01-2907-2008 include black beans, broken black beans, partly black beans, and brown coffee beans. these defects degrade the quality and affect the market value. Previous studies have proposed computer vision systems as a non-invasive testing method for grading coffee beans based on trained images. However, obtaining the detail in a single bean image based on the region of interest could have been more comprehensively explained in those studies, making them less informative and requiring a more detailed conceptual breakdown. this study describes several coffee beans captured as images using a digital camera with a specific field of view, F/25 f-number, 1/60s exposure time, ISO400, and a lighting system reaching 12,000 lux. these images are processed based on the region of interest and centroid, resulting in single bean images with a resolution of 220 x 220 pixels ready for training. the samples used in this study are Robusta Lampung coffee beans, which are commonly found in Indonesia and exhibit various types of defects based on the criteria in SNI 01-2907-2008, such as black beans, broken black beans, partly black beans, and brown coffee beans. © 2023 ieee.

关键词： Defects

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High Precision Method of Federated Learning Based on Cosine Similarity and Differential Privacy 15

High Precision Method of Federated Learning Based on Cosine ...

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2022 ieee Congress on Cybermatics: 15th ieee international conferences on Internet of things, ithings 2022, 18th ieee international conferences on Green Computing and Communications, GreenCom 2022, 2022 ieee international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th ieee international conference on Smart Data, SmartData 2022

作者： Wang, Jia Li, Yazheng Ye, Ronghang Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Zhengzhou University of Industrial Technology College of Computer Science and Software Engineering Zhengzhou China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Federated learning has emerged as an efficient way to exploit distributed data in recent years. It allows multiple client nodes to collaboratively train an optimized machine learning model without revealing the participants' data. However, there are some shortcomings in the existing federated learning algorithms: the existence of poor quality nodes leads to undesirable effects on the overall gradient descent direction of the model. And the typical iterative training and noising way also make the privacy loss of the model reaches the privacy budget quickly. Hence, the model training may stop before convergence. In this paper, we propose a federated learning gradient adaptive aggregation method based on cosine similarity and a central node privacy protection method based on random differential privacy to solve the above-mentioned problems respectively. the proposed scheme was implemented on two public general datasets (MNIST and SVHN) and a medical dataset (UCI diabetes). Experimental results show that the federated learning adaptive method based on cosine similarity could effectively eliminate the negative influence of nodes with poor data quality on the overall model and maintain stable performance. the central node privacy protection method based on random differential privacy could also improve the performance of the training model under the same privacy budget and privacy loss threshold. © 2022 ieee.

关键词： Learning algorithms

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On the Influence of Grid Cell Size on Taxi Demand Prediction 8th

On the Influence of Grid Cell Size on Taxi Demand Predict...

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8th EAI international conference on Smart Objects and Technologies for social Goods, GOODTECHS 2022

作者： Korth, Merlin Schleibaum, Sören Müller, Jörg P. Ehlers, Rüdiger Department of Informatics Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany Institute for Software and Systems Engineering Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9783031288128

Accurate taxi demand prediction has the potential to increase customer satisfaction and hence the usage of ride-sharing by predicting the number of taxis needed at a certain place and time. When reviewing the related work on demand prediction, we observed that in taxi demand prediction different grid topologies – e.g. rectangular subdivisions of an area – and sizes are applied. However, it is not clear how and why the grid cells are configured the way they are and a systematic comparison of different topologies and sizes as regards their influence on urban demand prediction is lacking. In this paper, we compare the influence of different grid cell sizes – 250 m, 500 m, and 1000 m – on the prediction accuracy of different types of deep learning-based taxi demand prediction approaches, such as convolutional neural networks, recurrent neural networks, and graph neural networks. therefore, we select five deep learning-based approaches from related work and evaluate their performance on the New York City TLC taxi trip dataset and three different evaluation metrics. Our results show that approaches with a grid cell of size 1000 m and 500 m achieve a higher prediction accuracy. Furthermore, we propose to consider the grid cell size as a tunable parameter in demand prediction models. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Recurrent neural networks

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Data Availability Optimization for Cyber-Physical systems 15

Data Availability Optimization for Cyber-Physical Systems

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作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. the appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. the proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 ieee.

关键词： Iterative methods

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Distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

this paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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Demo: P-Fall: Personalization Pipeline for Fall Detection 23

Demo: P-Fall: Personalization Pipeline for Fall Detection

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Proceedings of the 8th ACM/ieee international conference on Connected Health: Applications, systems and engineering Technologies

作者： Anne H. Ngu Awatif Yasmin Tarek Mahmud Adnan Mahmood Quan Z. Sheng Computer science Texas State University San Marcos TX USA Computer Science Texas State University San Marcos TX USA Scholl of Computing Macquarie University Sydney NSW Australia School of Computing Macquarie University Sydney NSW Australia

ISBN: (纸本)9798400701023

We present an edge-cloud collaborative personalized fall detection pipeline called P-Fall. A personalized fall detection model requires real-time adaptation of a pre-trained model using real-time feedback data provided by the user. We, herein, highlight the design of the software architecture for a collaborative framework, the smart-watch's UI for the ease of collecting a user's feedback data, and the automation of the personalization process.

关键词： fall detection

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Outbound Innovation Strategic Plan for Malaysian SMEs in the software Industry: An Operational Research Framework

Outbound Innovation Strategic Plan for Malaysian SMEs in the...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Soffri Yussof Syed Nasirin Syed Zainol Abidin Decision Support Systems Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan Malaysia

the role of Small and Medium Enterprises (SMEs) in Malaysia plays a significant component in driving the nation’s economic growth. the present analysis focuses on a limited number of studies investigating SMEs' systematic methods for pursuing outbound innovation strategies. Hence, the focus is to evaluate the imperative need for outbound innovation strategies that can effectively contribute to a comprehensive strategic approach. this study aims to develop a comprehensive operational research framework that will be incorporated into the formulation of the strategic plan. the strategic plan will encompass external technology commercialization strategies specifically designed for SMEs. the operational research framework comprises three distinct phases. Phase 1 involves conducting semi-structured interviews. Phase 2 focuses on formulating the Outbound Innovation Strategic Plan (OISP). Lastly, Phase 3 entails evaluating the suitability of the OISP. the outcome of this research involves the development of an OISP specifically tailored for Malaysian SMEs in the software industry.

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Goal-Oriented Prioritized Non-Functional Testing with Stakeholders' Priorities

Goal-Oriented Prioritized Non-Functional Testing with Stakeh...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Ibtesam Gwasem Weichang Du Andrew McAllister Computer Science University of New Brunswick Fredericton Canada

Non-functional testing is crucial in software product line engineering to ensure high-quality end products. However, software complexity, limited testing resources, and time constraints pose challenges in conducting comprehensive testing. Additionally, non-functional testing can be seen as a multi-criteria decision problem since it involves multiple quality attribute requirements. Stakeholders often prioritize certain quality attributes over others based on their specific needs, goals, interests, and the value they perceive each quality aspect holds for them. therefore, incorporating these priorities during test planning is essential for guiding decision-making and aligning the testing process with their needs. this paper presents a methodology for addressing non-functional testing in goal-driven software product lines. the methodology employs a semi-automated process for non-functional testing that utilizes the domain goal model as a testing basis to identify areas (features) within the application within the test space of quality attribute of the application and employs a multi-criteria decision method for prioritizing those features based on stakeholders' assigned priority values. Our goal-oriented methodology optimizes testing efforts by focusing on critical areas, aligning priorities with stakeholders' expectations to enhance software quality and reliability. the methodology supports three key testing activities: capturing of testable non-functional requirements and use case definition for early testing, effective testing scopes design with stakeholders' priorities, and creation of standardized test cases using pre-designed templates. A prototype toolkit was implemented to support the proposed methodology.

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