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Federated Learning with Clustering-Based Participant Selection for IoT Applications

Federated Learning with Clustering-Based Participant Selecti...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Wang, Kevin I-Kai Ye, Xiaozhou Sakurai, Kouichi The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Dept. of Informatics Fukuoka Japan

ISBN: (纸本)9781665480451

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance. © 2022 IEEE.

关键词： Internet of things

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Leveraging Light Intensity Structural Breaks for IoT Context-Awareness 10

Leveraging Light Intensity Structural Breaks for IoT Context...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Violos, John Stavrianos, Konstantinos Voutsas, Fotios Leivadeas, Aris École de technologie supérieure Department of Software and IT Engineering Montreal Canada Harokopio University of Athens Dept. Informatics and Telematics Athens Greece Netdata Inc San Francisco United States

ISBN: (纸本)9798350373011

Sensor data serves as a valuable information source that facilitates context-awareness within an Internet of Things (IoT) environment, thereby augmenting its capacity to adapt intelligently to evolving conditions and user requirements. This paper explores the utilization of structural breaks within time series to enhance context-awareness in the realm of a smart IoT environment, particularly within smart homes. By identifying shifts or disruptions in the underlying sequential measurements of light intensity, this research aims to develop a methodology capable of understanding the action of an entity that interacts with a smart environment. Specifically, by leveraging machine learning and statistical techniques for sensor time series, we delve into the detection of structural breaks, elucidating their significance in capturing relevant context information crucial for providing situational awareness. Through empirical validation and a case study with an IoT device, the paper demonstrates the efficacy of incorporating structural break analysis as a pivotal component in enhancing the sensing and awareness of ambient systems deployed in residential settings. © 2024 IEEE.

关键词： Smart homes

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Al-Safi, Mohammed G. S. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Dept. of Computer Technology Engineering Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Accounting Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Kurdistan Region Iraq

ISBN: (纸本)9798350369359

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper. © 2023 IEEE.

关键词： Vehicles

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

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2023 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Qasim, Haitham B. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Techniques Engineering Dept. of Medical Instruments Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Computer Science Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Iraq

ISBN: (纸本)9798350307771

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques. © 2023 IEEE.

关键词： Washing

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GAN vs. LSTM: Network State Prediction 17

GAN vs. LSTM: Network State Prediction

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

Design and Analysis of Automated Machine Learning (AutoML) i...

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2022 International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2022

作者： Sihombing, Denny Jean Cross Dexius, Jawangi Unedo Manurung, Jonson Aritonang, Mendarissan Adinata, Harni Seven Atma Jaya Catholic University of Indonesia Dept. Information System Jakarta Indonesia Stmik Pelita Nusantara Dept. Software Engineering Medan Indonesia Universitas Methodist Indonesia Dept. Informatics Management Medan Indonesia Universitas Sam Ratulangi Dept. Informatics Engineering Manado Indonesia

ISBN: (纸本)9781665472883

Massively using conventional machine learning requires many people to understand the process. However, the design of an automatic machine learning system using the Pycaret Library can make it easier for users by reducing some of the operations of machine learning in general. This automated machine learning design is managed in the Power BI application so that the display form can be better understood and easily visualized. By carrying out several method stages in the form of literature studies, data collection, designing classification methods using conventional machine learning and AutoML in Power BI, analysis of results, and reporting/publication. The result of comparison of machine learning and Auto ML: In machine learning, 98 lines of line code are used, while in AutoML, eight lines of code are used. Another advantage derived from using AutoML in PowerBI is the feature to be able to visualize processed datasets so that they can obtain information in the form of diagrams, charts, and others. In testing using the breast cancer dataset, the use of Machine Learning is considered better based on the score obtained. Machine Learning received the highest Accuracy using GaussianNB is 0.94. In testing using the Diabetes dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using CatBoost Model is 0.777. In testing using the heart failure dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using Random Forest Model with a score of 0.85. © 2022 IEEE.

关键词： Statistical tests

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An Ensemble Model for the Analysis of Parent-Child Interactions from Text and Audio 22

An Ensemble Model for the Analysis of Parent-Child Interacti...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Nikbakhtbideh, Behnam Duffett-Leger, Linda Letourneau, Nicole Oxford, Monica Tryphonopoulos, Panagiota Moshirpour, Mohammad University of Calgary Dept. of Electrical and Software Engineering Calgary Canada University of Calgary Dept. of Nursing Calgary Canada University of Washington Dept. of Nursing Washington United States Arthur Labatt Family School of Nursing University of Western Ontario Ontario Canada University of California Irvine Dept. of Informatics Irvine United States

ISBN: (纸本)9798350345346

The interaction between parents and their children is foundational for their social and emotional development and well-being. Among measures that assess parent-child interaction quality, the Parent-Child Interaction Teaching Scale (PCI-TS) is an effective and well-established assessment tool that measures the quality of parent-child interactions. One of the significant hurdles in PCI-TS is identifying parent-child behaviors early on to aid parents in addressing initial behavioral issues. However, manual evaluations are resource-intensive and time-consuming, and restrict the accessibility of such assessments. In this research, we present an ensemble model to categorize various behavior types in parent-child interaction scales such as PCI-TS. We have collected over 2,000 minutes of video of parents and their children emerging in specific tasks such as drawing or object manipulation. These videos have been labeled based on PCI-TS by trained healthcare professionals. Our ensemble model has been trained to recognize emotions and psychological elements within parent-child interactions. This model is adjusted by multiple modalities and aspects of these psychological elements. We posit that our study introduces the first model that employs a new framework to integrate numerous behavioral factors to recognize these psychological elements within parent-child interactions using audio and text modalities. Our suggested model can discern and detect the semantic and syntactic features of audio and language in parent-child interactions according to the PCI-TS scale. The results of our evaluation showed an enhancement in performance when implementing this strategy, compared to similar approaches by 16% in F1-score. © 2023 IEEE.

关键词： audio classification behavior analysis e-health parent-child interaction text classification

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Multidisciplinary Hackathons: Towards Developing Practical software engineering Skills 53

Multidisciplinary Hackathons: Towards Developing Practical S...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Moshirpour, Mojgan Shoura, Tariq Al Duffett-Leger, Linda Moshirpour, Mohammad Werklund School of Education University of Calgary CalgaryAB Canada University of Calgary Dept. Electrical and Software Engineering CalgaryAB Canada University of Calgary Faculty of Nursing CalgaryAB Canada University of California Irvine Dept. of Informatics IrvineCA United States

ISBN: (纸本)9798350336429

software engineers often work on multidisciplinary projects as they collaborate with domain experts in a variety of different disciplines to effectively develop software systems. While the focus of software engineering curricula is generally on teaching technical skills, it is highly desirable to provide students with hands-on experience in working on multidisciplinary projects. This research attempts to facilitate the experience of working on projects with stakeholders of diverse expertise through the use of multidisciplinary hackathons. In recent years, hackathons have been considered effective teaching and evaluation tools that can contribute to the development of practical skills for students. Hackathons can be used as an instrument to improve the educational experience through learning by doing in which learners can test their problem-solving, project management, and prioritization skills in a limited time. This research presents a case study of hackathons in the field of health informatics involving students from software engineering and nursing. To facilitate a consistent and supportive multidisciplinary learning experience, the hackathon was introduced as a course component in two different courses in nursing and software engineering. The data presented in this research is from two consecutive executions of the hackathon involving two different cohorts of students in 2022 and 2023. The results of this study show that students believe a multidisciplinary course would be a welcomed addition as it facilitated an exciting collaborative environment to work on real-world problems;providing a unique opportunity for interactions between different disciplines. The addition of hackathons into the courses enhanced the students' understanding of how to work with requirements from gathering to analysis to development, and allowed them the unique opportunity to examine the different challenges, technologies, and tools involved with other disciplines. © 2023 IEEE.

关键词： collaboration hackathon multidisciplinary teams nursing public health software engineering

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