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

The Correlation Between Architecture Smells, Package Stabili...

sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

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

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

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-dep.h 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 dep.ndency and Unstable dep.ndency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable dep.ndency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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Reducing Overconfidence Predictions for Autonomous Driving Perception

arXiv

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arXiv 2022年

作者： Melotti, Gledson Premebida, Cristiano Bird, Jordan J. Faria, Diego R. Gonçalves, Nuno Federal Institute of Espirito Santo Brazil The Dep. of Electrical and Computer Engineering University of Coimbra-Portugal Portugal Department of Computer Science Nottingham Trent University United Kingdom School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Dep. of Electrical and Computer Engineering University of Coimbra-Portugal Portuguese Mint and Official Printing Office Lisbon Portugal

In state-of-the-art deep learning for object recognition, Softmax and Sigmoid layers are most commonly employed as the predictor outputs. Such layers often produce overconfidence predictions rather than proper probabilistic scores, which can thus harm the decision-making of 'critical' perception systems applied in autonomous driving and robotics. Given this, we propose a probabilistic approach based on distributions calculated out of the Logit layer scores of pre-trained networks which are then used to constitute new decision layers based on Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) inference. We demonstrate that the hereafter called ML and MAP layers are more suitable for probabilistic interpretations than Softmax and Sigmoid-based predictions for object recognition. We explore distinct sensor modalities via RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5 datasets, where our approach shows promising performance compared to the usual Softmax and Sigmoid layers, with the benefit of enabling interpretable probabilistic predictions. Another advantage of the approach introduced in this paper is that the so-called ML and MAP layers can be implemented in existing trained networks, that is, the approach benefits from the output of the Logit layer of pre-trained networks. Thus, there is no need to carry out a new training phase since the ML and MAP layers are used in the test/prediction phase. The Classification results are presented using reliability diagrams, while detection results are illustrated using precision-recall *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Real-time Ventilation Detection for Feedback and Guidance During Newborn Resuscitation

Real-time Ventilation Detection for Feedback and Guidance Du...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Øyvind Meinich-Bache Hannah Håland Anita Yeconia Rafal Paprocki Solveig Haukås Haaland Øystein Gomo Thomas Haukland Siren Irene Rettedal Sara Brunner Esto Mduma Hege Ersdal Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Strategic Research Laerdal Medical Stavanger Norway Haydom Lutheran Hospital Tanzania Stavanger University Hospital Norway Faculty of Health Sciences University of Stavanger Norway

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

For a healthcare provider treating a non-breathing newborn baby, timely ventilation of the lungs is the most critical action to save the newborn’s life. However, the guidelines for newborn resuscitation are challenging to follow and a real-time feedback and guidance system may contribute to more optimal treatment. In this study we utilized deep neural networks for detecting ventilation in the audio signal extracted from video recordings of 25 newborn resuscitation episodes. The proposed ventilation detection system utilizes Log Mel Spectrograms for feature generation, audio augmentation for dataset expansion, and 2D Convolutional Neural Networks (CNNs) for ventilation classification. For the task of image classification, we compared two different pretrained backbones suited for real-time inference and a custom-built efficient CNN, called VD-CNN. We found that using MobilenNetV3Large as the feature extractor yielded the best results, achieving a F1-score of 97.21%. Our efficient VD-CNN, with a parameter count one fifth that of MobilenNetV3Large, still achieved a robust F1-score of 95.37%. To demonstrate that the proposed system can perform ventilation detection and provide immediate feedback during newborn resuscitation in real-time, we integrated the ventilation detection system into a newborn resuscitation station called Liveborn Station.

关键词： Pediatrics Computational modeling Tablet computers Medical services Ventilation Feature extraction Real-time systems Convolutional neural networks Video recording Spectrogram

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Performance Monitoring and Controlling of Multicore Shared-Memory Parallel Processing Systems 3

Performance Monitoring and Controlling of Multicore Shared-M...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Haji, Lailan M Zeebaree, Subhi R. M. Ageed, Zainab Salih Ahmed, Omar M. Sadeeq, Mohammed A. M. Shukur, Hanan M. University of Zakho Faculty of Science Computer Science Dep. Duhok Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq Zakho Technical Institute Duhok Polytechnic University Information Technology Dept. Duhok Iraq Technical College of Administration Duhok Polytechnic University ITM Dept. Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq

ISBN: (纸本)9798350334968

The field of research known as 'parallel processing' examines architectural and algorithmic methods for increasing the efficiency and other desirable qualities (such as cost-effectiveness and reliability) of computers by exploiting many forms of concurrency. The development of multi-core CPUs has opened the door to novel strategies for boosting system performance via parallelism. Increasing the number of concurrent threads greatly affects the time cost. While increased parallelism might reduce calculation time, the associated synchronization costs would likely be prohibitive for most applications. In this article, we'll go through the first half of the process, which involves gathering requirements and designing a prototype;the second half, which involves monitoring and managing a program that can run on a wide range of multicore system architectures, including those with (2, 4, and 8 CPUs). A shared-memory parallel processing method was proposed and built to show off the system's full potential. The implications of multi-core systems on overall program execution time and parallel processing in shared-memory systems provide the backbone of this investigation. © 2022 IEEE.

关键词： Cost effectiveness

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Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Analyzing Data Efficiency and Performance of Machine Learnin...

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European Conference on Mobile Robots (ECMR)

作者： Aleksa Marusic Louis Annabi Sao Mai Nguyen Adriana Tapus Autonomous Systems and Robotics Lab Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris Palaiseau France Dep. Informatique IMT Atlantique

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. computer-aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zuwaina Alkhyeli Ayesha Alshehhi Mazna Alhemeiri Salma Aldhanhani Khalil AlBalushi Fatima Ali AlNuaimi Abdelkader Nasreddine Belkacem Dep. Computer Science and Software Engineering CIT UAE University Al Ain UAE Dep. Information Systems and Security CIT UAE University Al Ain UAE Dep. Computer & Network Engineering CIT United Arab Emirates University Al Ain UAE TII Autonomous Robotics Research Center Abu Dhabi UAE

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user’s brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope.

关键词： Privacy Authentication Passwords Feature extraction Brain-computer interfaces Electroencephalography Security

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The search of the path MTU with QUIC

The search of the path MTU with QUIC

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2021 Workshop on the Evolution, Performance and Interoperability of QUIC, EPIQ 2021 - Part of CoNEXT 2021

作者： Völker, Timo Tüxen, Michael Rathgeb, Erwin P. FH Münster Uni. of Appl. Sciences Dep. of Electrical Engineering and Computer Science NRW Steinfurt Germany University of Duisburg-Essen Computer Networking Technology Group NRW Essen Germany

ISBN: (纸本)9781450391351

A data sender in an IP based network is only capable to efficiently use a network path if it knows the packet size limit of the path, i.e., the Path Maximum Transmission Unit (PMTU). The IETF recently specified a PMTU discovery framework for transport protocols like QUIC. This paper complements this specification by presenting a search algorithm. In addition, it defines several metrics and shows results of analyses for the algorithm with various PMTU candidate sequences using these metrics. We integrated the PMTU discovery with our algorithm into a QUIC simulation model. This paper describes the integration and presents measurements obtained by simulations. © 2021 ACM.

关键词： Learning algorithms

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Intelligent Recognition of Immune Cells: Analysis and Research

Intelligent Recognition of Immune Cells: Analysis and Resear...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Chih-En Li Wei-Lun Liu Kun-Yi Lai Yu-Hsiang Chiang Xiaowei Yan Hao-Xiang Chen Hsu-Ching Li Wan-Shu Cheng Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology Seattle Washington U.S.A Dep. of Computer Science and Information Management Providence University Taichung Taiwan

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software.

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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