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A New Resource Configuring Scheme for Variable Workload in IoT Systems

A New Resource Configuring Scheme for Variable Workload in I...

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2022 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2022

作者： Nam, Sunhwa A. Cho, Kyungwoon Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of Embedded Software Research Center Ewha University Seoul Korea Republic of

ISBN: (纸本)9781665453059

With the growing data size and the increased amount of computational load in machine learning workloads, configuring the resources of IoT (Internet-of-Things) systems in an energy-saving way is becoming important. For example, the workload of an unmanned aerial vehicle (UAV) depends on weather and obstacle conditions, and reconfiguring the processor voltage and memory state for these conditions is critical to battery life. However, resource configuring in traditional IoT systems is fixed in advance to meet real-time constraints. In this paper, we suggest a new resource configuring scheme for real-time jobs that can handle workload variations in emerging IoT systems. The goal of our scheme is to minimize the energy consumed by proper resource configurations in response to workload fluctuations and eliminate the tardiness of real-time jobs. To handle workload fluctuations, we categorize real-time jobs into primary jobs and additional jobs, and pre-plan the resource configuring for various workload situations. Based on this, we start the IoT system with the configuration for the primary jobs, and when additional jobs are activated, we update the resource configurations promptly for the new situation. In particular, our resource configuring scheme optimizes the supplied voltage of the processor and memory configuration for all real-time job combinations, and reflects to the system instantly as additional jobs are activated. Based on simulation experiments under various workload conditions, we show that the suggested scheme saves the battery power by 32.1% without the tardiness of real-time jobs. © 2022 IEEE.

关键词： Machine learning

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Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment 14

Ontological View-driven Intensional Semantic Integration for...

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14th International Conference on Knowledge engineering and Ontology Development, KEOD 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Ali Adhnouss, Fateh Mohamed Ali El-Asfour, Husam M. McIsaac, Kenneth El-Feghia, Idris Aburukba, Raafat Wahaishi, Abdulmutalib Dept. of Electrical & Computer Engineering University of Western London Canada Faculty of Information Technology Misurata University Libya Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Software Engineering Rochester Institute of Technology New York United States

ISBN: (纸本)9789897586149

Ontologies are an essential component of semantic integration approaches for information systems . In a decentralized environment, each specification of the domain reflects an Ontological view. However, the semantics characterization of information systems in such decentralized environment poses a significant issue related to their integration. Information systems are viewed as independent intensional entities with their own beliefs, distinct from those held by others. Such autonomy is distorted by traditional extensional semantics. Other entities’ beliefs are introduced into a given entity, thus affecting their beliefs. Additionally, the information that one entity provides to another entity may not be consistent with the information known by the latter. We need an alternative semantics for information integration, which is not dependent on the extension, but rather on the underlying conceptualization. This paper proposes a classification of the environment where the information systems lives and a novel modelling paradigm for information integration using intensional logic to model the ontological views. The model comprises a formal modeling approach for the conceptualization as well as for the semantic integration process. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Semantics

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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-depth 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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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare 19

Cloud-Based Framework for Data Exchange to Enhance Global He...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

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Information Technology (ACIT)

作者： Suhail Odeh Anas Samara Nicola Zreineh Seif Obeid Maen Ibrieghieth Mahmoud Obaid Software Engineering Dept. Bethlehem University Palestine Computer System Engineering Arab American University Jenin Palestine

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care.

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A survey of Detecting Flakiness in Automated Test Regression Suite

A survey of Detecting Flakiness in Automated Test Regression...

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Learning and Technology Conference (L&T)

作者： Mohamed ElGazzar Eman Hossny Fatma A. Omara Software Engineering Dept Faculty of Computers and Informaiton Cairo Egypt Computer Science Dept Faculty of Computers and Informaiton Cairo Egypt

Recently, Flaky Tests have gotten the attention of the research community. These tests not only waste time and resources, but also undermine the reliability of the automated tests and hence the software systems are affected. So, the detection of Flaky Tests is very important and has a wide audience from both industry and academia. In addition, the research work is very promising having a great potential for the contribution that maps to a true value added to the up-to-date industry projects. Although lots of effort has been done to detect Flakiness in the software systems, the majority of the existed works focuses on different techniques in isolation. The aim of the work in this paper is to overlook the existed flakiness detection techniques, try to analyze them, and hold a comparison, indicating the proper context for each one. Most of the existed flakiness detection techniques are done on unit tests and library/technical projects. According to the comparative study among these techniques, it is found gaps and wide potential for contribution. On the other hand, flakiness detection techniques can be clustered under three main headers; First, study the phenomena under different testing levels by trying to focus on projects with integration, system, and end-toend tests. Second, study the phenomena under different Project types and data sets by studying business projects with user interfaces and mobile applications and so on. Third, study the phenomena toward a faster delivery cycle by studying flake tests in the context of parallel execution and Continuous Integration and Continuous Deployment (CI/CD). We can conclude that not all detection techniques have the same potential where some techniques are very competent while others still struggle to achieve considerable efficiency.

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Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML

Ensemble Method For Fault Detection & Classification in Tran...

引用

Annual IEEE Systems Conference

作者： Muhammad Hayyan Bin Shahid Akramul Azim Dept. Of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada

Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.

关键词： Training Power transmission lines Systematics Fault detection Mean square error methods Maintenance engineering Safety

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An Effective Recurrent Neural Network (RNN) based Intrusion Detection via Bi-directional Long Short-Term Memory

An Effective Recurrent Neural Network (RNN) based Intrusion ...

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Sivamohan, S. Sridhar, S.S. Krishnaveni, S. Dept of Computer Science and Engineering SRMIST Kattankulathur Chennai India Dept of Software Engineering SRMIST Kattankulathur Chennai India

ISBN: (纸本)9781728185835

The evolution of communication and information systems has raised the volume of data distributed through the internet. As an effect, a majority of digital resources have been increased, so does the challenge of cybersecurity. Intrusion detection systems (IDSs) are closely connected to a holistic approach for preventing cyberattacks. Due to the high utilization of network traffic in the cyber world, conventional machine learning approaches used in intrusion detection systems are becomes ineffective. Recently evolved deep learning techniques are successfully applied in the detection and classification of threats at both the network and host levels, with a focus on deep learning. This study proposed an efficient IDS based on Recurrent Neural Network (RNN) via Bi-directional Long Short- Term Memory (RNN BiLSTM). The strategy uses a two-step mechanism to develop the expertise of the suggested solution to address network problems. This research aims to determine the algorithm's processing time and increase attack classification accuracy. The proposed model was evaluated on the CICIDS2017 intrusion detection dataset. The Random forest and Principal component analysis algorithms were used to detecting the valuable features and eliminating the unwanted features from the given dataset. The findings revealed that BLSTM outperform all other RNN architectures in terms of classification accura 98.48%. © 2021 IEEE.

关键词： Long short-term memory

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Deep Word Embedding and Language Model based Sentimental Analysis of COVID-19 Tweets

Deep Word Embedding and Language Model based Sentimental Ana...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Sobia Qaiser M. Usman Akram Dept. of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The great Covid-19 pandemic affected billions of people's lives personally and socially. The research involves the analysis of public's views and opinions shared on Twitter social media platform related to Covid-19 pandemic and its detrimental or non-detrimental effects on public's mental health by using machine learning algorithm. The main purpose of this research includes analyzing public views related to Covid-19 pandemic by classifying the Tweets collected from the Twitter social platform. The proposed approach combines deep word embedding with MiniLM as an encoder to produce word vectors of high-dimensionality to preserve the words' semantic information. The resultant word vectors were used to train the model for the classification of the tweet in five sentiments i.e. Positive, Extremely Positive, Negative, Extremely Negative, and Neutral. The methodology is tested using publicly available Kaggle dataset as well as privately collected tweets. The comparative evaluation of the models revealed that MiniLM outperformed existing BERT based counterparts and attained highest accuracy of 93% with the Kaggle dataset. This analysis can assist the medical health authorities to monitor health information, conduct, and plan interventions to lower the pandemic effect and can help government to take precautionary measures.

关键词： COVID-19 Machine learning algorithms Pandemics Social networking (online) Blogs Semantics Government Mental health Vectors Monitoring

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AMN: Attention-based Multimodal Network for Android Malware Classification

AMN: Attention-based Multimodal Network for Android Malware ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Yi Yang Huyu Wu Yanzhe Wang Peng Wang Dept. Software Engineering Sichuan University Chengdu China Dept. Computer Science Sichuan University Chengdu China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

With the rapid evolution of malware, android malware classification has become increasingly important and remains an active area of research. Traditional machine learning methods rely heavily on hand-crafted features based on expert domain knowledge, making the feature engineering process time-consuming. In recent years, deep learning-based Android malware classification has attracted considerable attention, inspired by the remarkable success of deep learning techniques in the fields of vision and natural language processing. Deep learning methods replace the manual process of feature engineering with a framework capable of simultaneous feature extraction and classification directly from raw data. However, existing approaches do not fully exploit hybrid and multimodal features, which limits their ability to consider multiple disparate sources of information during classification. In this paper, we present attention-based multimodal network(AMN), a multimodal approach to malware classification based on the attention mechanism. AMN combines two modalities of data: 1) grayscale images generated by malware binary files, and 2) network traffic feature sequences. The model employs a cross-modal attention mechanism that combines the features of both modalities by compensating for their limitations. Furthermore, a transfer learning method is proposed to improve the generalization performance of the model, especially in scenarios with limited sample size. The proposed solution surpasses the state-of-the-art precision, achieving 97.73%, along with a recall of 96.51%, as demonstrated through experiments conducted on the CICInvesAndMal2017 dataset. Additionally, to show the effectiveness of our model, ablation experiments were conducted.

关键词： Deep learning Attention mechanisms Mechatronics Transfer learning Random access memory Telecommunication traffic Feature extraction

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