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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Fotios Kalioras Gabriel Filios Stylianos Karatzas Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Computer Technology Institute and Press “Diophantus” Greece Department of Civil Engineering University of Patras Greece

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution.

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning 3

Predictive Design for Quality Assessment Employing Cloud Com...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jindal, Gaurav Tiwari, Vidhika Mahomad, Riyaz Gehlot, Anita Jindal, Mimoh Bordoloi, Dibyhash G L Bajaj College of Technology and Management Department of Master of Computer Applications Gr. Noida India DPGITM Mechanical Engineering Department Gurugram India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India National Informatics Centre Delhi India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure. © 2023 IEEE.

关键词： Cloud computing

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Towards Federated Ontology-Driven Data Integration in Continuous Software Engineering 23

Towards Federated Ontology-Driven Data Integration in Contin...

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37th Brazilian Symposium on Software Engineering, SBES 2023, held in conjunction with the Brazilian Conference on Software: Theory and Practice, CBSoft 2023

作者： Júnior, Paulo Sérgio Santos Almeida, João Paulo A. Barcellos, Monalessa Leds Research Department of Informatics Federal Institute of Education Science and Technology of Espírito Santo ES Serra Brazil Computer Science Department Federal University of Espírito Santo ES Vitória Brazil

ISBN: (纸本)9798400707872

Organizations have adopted Continuous Software Engineering (CSE) practices aiming at making software development faster, iterative, integrated, continuous, and aligned with the business. In this context, they often use different applications (e.g., project management tools, source repositories, and quality assessment tools) that store valuable data to support daily activities and decision-making. However, data items often remain spread in different applications that adopt different data and behavioral models, posing a barrier to integrated data usage. As a consequence, data-driven software development is uncommon, missing valuable opportunities for product and process improvement. In this paper, we explore an ontology network addressing CSE aspects to develop a data integration solution in which networked ontologies are the basis to build reusable and autonomous software components that work together in a system federation to provide meaningful integrated data. We achieve a comprehensive and flexible solution that can be used as a whole or partially, by extracting only the components related to the subdomains of interest. © 2023 ACM.

关键词： Ontology

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Online Control of Linear Systems with Unbounded and Degenerate Noise

arXiv

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arXiv 2024年

作者： Ito, Kaito Tsuchiya, Taira Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Mathematical Informatics The University of Tokyo Tokyo Japan

This paper investigates the problem of controlling a linear system under possibly unbounded and degenerate noise with unknown cost functions, known as an online control problem. In contrast to the existing work, which assumes the boundedness of noise, we reveal that for convex costs, an Oe(√T) regret bound can be achieved even for unbounded noise, where T denotes the time horizon. Moreover, when the costs are strongly convex, we establish an O(poly(log T)) regret bound without the assumption that noise covariance is non-degenerate, which has been required in the literature. The key ingredient in removing the rank assumption on noise is a system transformation associated with the noise covariance. This simultaneously enables the parameter reduction of an online control algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear systems

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Precision slicing for enhanced defect detection in high-resolution wind turbine blade imagery 1

Precision slicing for enhanced defect detection in high-reso...

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1st International Workshop on Intelligent and CyberPhysical Systems, ICyberPhyS 2024

作者： Svystun, Serhii Melnychenko, Oleksandr Radiuk, Pavlo Savenko, Oleg Sachenko, Anatoliy Khmelnytskyi National University 11 Institutes str. Khmelnytskyi29016 Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The analysis of high-resolution aerial imagery captured by unmanned aerial vehicles (UAVs) presents significant analytical challenges, primarily due to the minuscule size of observable objects and the variability in object scale influenced by UAV altitude and positioning. These factors often lead to diminished data fidelity and complicate the detection of smaller objects, which are critical in applications such as infrastructure monitoring. Traditional image processing techniques, which typically segment images into smaller, randomly cropped sections before analysis, must sufficiently address these challenges. In this work, we propose a novel defect detection framework for identifying minor to medium-sized damages on wind turbine blades (WTBs), a critical component in renewable energy production. The proposed framework, termed 'slice-aided inference,' enhances the existing methodologies by incorporating both traditional patch division and a novel, more advanced technique known as slice-aided hyper-inference. These techniques are rigorously assessed with various advanced deep learning models, emphasizing their efficiency in identifying surface defects. The empirical testing conducted as part of this study demonstrates significant enhancements in detection capabilities, leveraging a dataset of high-resolution UAV images to highlight the practical applications and effectiveness of the proposed framework in real-world scenarios. © 2023 Copyright for this paper by its authors.

关键词： Deep neural networks

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Emotion Recognition using Deep Learning Techniques and Word Embeddings 6

Emotion Recognition using Deep Learning Techniques and Word ...

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6th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021

作者： Kardakis, Spyridon Perikos, Isidoros Grivokostopoulou, Foteini Paraskevas, Michael Computer Technology Institute and Press 'Diophantus' and Computer Engineering University of Patras Informatics Department Patras Greece

ISBN: (纸本)9781728176819

Emotion recognition is a topic of vital importance in the world of Big Data. The accurate classification, analysis and interpretation of emotional content is highly desired in a wide spectrum of applications. In this work we examine the performance of Deep Learning models for an emotion recognition task. These models can also be applied to any classification task as well as text-related tasks in general. We examine the performance of deep learning methods in textual emotion recognition under different feature selection parameters such as one-hot-enconding, custom word2vec and more. An experimental study was designed and various deep learning methods were evaluated in publicly available datasets. The results are quite interesting and indicate the advantages of each method and each examined training approach. © 2021 IEEE.

关键词： Emotion Recognition

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Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers 2

Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder ...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Al Tawil, Arar Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Marzouk, AbdulRahman S. Hamad, Adam L. Zakaria, Khaled Y. Danial, Moshir A. Nassief, Mounir N. Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Faculty of Computers Egypt Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

Autism Spectrum Disorder is a multidimensional neurological developmental disorder that includes a wide plethora of mental disorders targeting one's social and behavioral abilities. Gaining a perceptive understanding of a topic like ASD is essential in one's lifespan, whether the symptoms displayed are mild or severely debilitating. Due to the populous size of clinical terms under the disorder's spectrum and the drastic gap between each symptom's severity, the diagnosis of ASD became a long and tribulating task for experts and professionals alike. The purpose of this paper is to present and compare different Machine Learning Classifiers Categorical Boosting, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and use their Training Algorithms Train-Test Splitting, K-Folds Cross-Validation to develop a quick and accurate Labeler ASD: 'YES' ? 'NO' for afflicted individuals based on multiple Datasets Generic, Adolescent, Children, Young, Adult, ASD aiming for an early diagnosis and an overall better understanding of the disorder. For each dataset, four Performance Metrics are extracted after applying the 7 ML models and the two training algorithms Accuracy, Precision, Recall, F1-Score. The resulting metrics are then displayed and compared to conclude the most accurate trained model for each dataset and with each of the training algorithms. © 2024 IEEE.

关键词： Diseases

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IoT-Fog Computing Sustainable System for Smart Cities: A Queueing-based Approach 1

IoT-Fog Computing Sustainable System for Smart Cities: A Que...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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A trust model that ensures the correctness of computing in grid computing system 5

A trust model that ensures the correctness of computing in g...

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5th International Workshop on Intelligent Information Technologies and Systems of Information Security, IntelITSIS 2024

作者： Rehida, Pavlo Savenko, Oleg Sachenko, Anatoliy Drozd, Andriy Vizhevski, Petro Khmelnytskyi National University Institutska str. 11 Khmelnytskyi29016 Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

The main objective of this article is to present model of a distributed computing system that ensures the correctness of computations performed with connected nodes. The article analyses modern approaches to designing systems and their functioning. It examines the advantages of chosen computing systems and the main challenges that need to be addressed for their successful application. The article describes a model that includes a central control unit and computing sub-systems, as well as the challenges associated with correct computing within this system. To address this problem, the article proposes utilizing the trust model approach, which classifies computing nodes by roles based on their level of trust. The functions of these roles are also discussed. Additionally, the article explores major approaches for designing the trust model, including the utilization of voting, behavioural analysis, and machine learning. Solutions for potential conflicts arising from voting are also presented. Special attention is paid to the process of gathering information and its further processing to form the behavioural portrait for each computing node in the system. Furthermore, the article covers data transformation and optimization to train models for predicting compromised nodes. Details of the performed experiment, including the used dataset, are also considered. The experiments performed show the dependence of prediction accuracy on the value of epochs set during model training. © 2023 Copyright for this paper by its authors.

关键词： Metadata

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