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15th ACM/SPEC International Conference on Performance Engineering, ICPE 2024

作者： Traini, Luca Li, Heng Department of Information Engineering Computer Science and Mathematics University of l'Aquila L'Aquila Italy Polytechnique Montréal Montreal Canada

ISBN: (纸本)9798400704451

We are pleased to welcome you to the 9thWorkshop on Challenges in Performance Methods for Software Development - WOSP-C 2024 (https://***/wosp-c-24/). This year's edition continues its tradition of being the forum for discussions on novel and unresolved challenges in the field of software performance engineering. The primary areas of interest for the workshop remain consistent with previous editions, but the scope of this year's edition has been extended to include additional novel topics. WOSP-C 2024 will cover topics such as performance assurance processes, the interplay between performance and privacy, green software, performance modeling, and simulation, among others. © 2024 Copyright held by the owner/author(s).

关键词： Software design

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Extracting the frequent sequential patterns among the factors associated with neonatal birthweight

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Journal of Neonatal Nursing 2023年第4期29卷 639-644页

作者： Omotehinwa, Temidayo Oluwatosin Devine, Samuel Nii Odoi Kolog, Emmanuel Awuni Dada, Oluwaseun Alexander Department of Computer Science and Informatics Faculty of Natural and Agricultural Sciences University of the Free State QwaQwa Campus South Africa Department of Mathematics and Computer Science Federal University of Health Sciences Otukpo Nigeria Department of Information and Communication Technology Presbyterian University College Abetifi Ghana Department of Operations and MIS University of Ghana Accra Ghana Department of Computer Science University of Helsinki Helsinki Finland Centre for Multidisciplinary Research and Innovation Abuja Nigeria The School of Software Lekki-Lagos Nigeria

The objective of this study was to employ the association rules mining technique to find the frequent co-occurrences among maternal factors associated with birthweight. Secondary data consisting of 189 records with predictors that assess the risk factors that influence low neonatal birthweight were employed for association rules mining. The extracted rules show that the different patterns of co-occurrences among these factors: a mother's pre-pregnancy weight, presence/absence of uterine irritability, attendance to antenatal care visits in the first trimester, hypertension history, maternal age, and a history of premature labor often lead to either a low or normal neonatal birthweight. The extracted rules could serve as a reference tool in the education, treatment, and care of pregnant women to ensure positive outcomes related to birthweight. Flowing from these rules, pregnant mothers should be educated about the relationship between their attributes and the probable birthweight of their unborn babies. © 2022 Neonatal Nurses Association

关键词： Association rules Birthweight Co-occurrence of factors Factors of birthweight Pregnant mother

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Thermal Performance Prediction of Metal-Foam Heat Sink Using Genetic Algorithm Driven Neural Network 2

Thermal Performance Prediction of Metal-Foam Heat Sink Using...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Chandan, K. Singh, Rimjhim Padam Nagaraja, K.V. Kumar, Priyanka Amrita School of Engineering Bengaluru Dept. of Mathematics India Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Texas at San Antonio Department of Computer Science United States

ISBN: (纸本)9798350314168

Electronic device temperature management is crucial for maximum performance as well as its longer life and metal foam heat sinks (MFHS) are widely employed in electronic cooling due to their large specific heat transfer surface area and fluid mixing capabilities. However, due to the complicated and random geometrical character of MFHSs, precisely predicting their performance is difficult and costly. Several deep learning approaches have been used to address this difficulty, combining input features such as metal foam shape, heat sink dimensions, and coolant flow conditions to forecast thermal-hydraulic performance. This paper offers a unique approach for parameter estimation that combines Genetic Algorithm with an Artificial Neural Network (ANN) model. The Genetic Algorithm is used to optimize the ANN model's parameters thereby, increasing its accuracy in forecasting the friction rate and Nusselt number of MFHSs. The Genetic Algorithm seeks for the best set of parameters that minimizes prediction errors by iteratively evolving a population of candidate solutions using methods influenced by natural selection and genetic variation. The outperforming findings show that Genetic Algorithm can be used to estimate parameters in complex systems, specifically in predicting the thermal-hydraulic performance of MFHSs. © 2023 IEEE.

关键词： Neural networks

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Image-Based Malware Analysis Using Residual Capsule Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 2022-2038页

作者： El alaoui-Elfels, Omaima Gadi, Taoufiq Bassir, Abdelhak First University of Settat Faculty of Sciences and Techniques. Mathematics Computer Science and Engineering Laboratory Settat26000 Morocco Mathematique Department Hassan First University of Settat Faculty of Sciences and Techniques Laboratory of Mathematics Computer Science and Engineering Settat26000 Morocco

Cyberattacks and malware detection software are in a perpetual race. Researchers have developed antimalware systems that can detect and classify malicious software automatically rather than manually analyzing malware files, which is time-consuming and ineffective. On the other hand, cyber-attackers have developed approaches to avoid signature-based detectors used by antivirus software. In order to improve these detectors, deep learning has been applied by automating feature extraction and malicious pattern identification. Convolutional Neural Networks (CNNs) as a deep learning architecture have proved their effectiveness in malware classification. However, these networks require a large dataset for training, and cannot handle spatial relationships among features effectively;they lose position information on the exact location of a feature, which is critical for a malware file that contains a series of sections. In this paper, we exploit the concept of image classification in the field of computer vision to detect and classify malware images using the ELU Residual Capsule Network (ER-Caps), which goes beyond CNNs’ shortcomings and shows a high performance on a highly imbalanced malware dataset without any enhanced training strategies. The malware files are converted into grayscale images and then processed by an advanced residual subnet to extract the complex features. The resulting feature maps are grouped into capsules and then processed by the dynamic routing algorithm to classify malware. The experimental results indicate that ER-Caps achieves an F1-score of 91.97%, 94.02%, and 98.43% on classifying VIRUS-MNIST, MMCC, and Malimg datasets, respectively, and a malware detection rate of 99.24%, outperforming other deep learning models proposed for malware analysis. © (2025), (International Association of Engineers). All rights reserved.

关键词： computer viruses

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Hyperbolic Punctured Spheres Without Arithmetic Systole Maximizers

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Matematica 2023年第4期2卷 934-961页

作者： Lakeland, Grant S. Young, Clayton Department of Mathematics & amp Computer Science Eastern Illinois University 600 Lincoln Avenue Charleston 61920 IL United States

We find bounds for the length of the systole—the shortest essential, non-peripheral closed curve—for arithmetic punctured spheres with n cusps, for n=4 through n=12, some of which were previously known due to Schmutz. This is shown using a correspondence between such surfaces and planar triangulations. We show that for n=7,10,11, arithmetic surfaces do not achieve the maximal systole length. © The Author(s), under exclusive licence to Springer science+Business Media LLC, part of Springer Nature 2023.

关键词： Arithmetic surface Punctured sphere Systole

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LCOT: LINEAR CIRCULAR OPTIMAL TRANSPORT 12

LCOT: LINEAR CIRCULAR OPTIMAL TRANSPORT

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12th International Conference on Learning Representations, ICLR 2024

作者： Martín, Rocío Díaz Medri, Ivan Bai, Yikun Liu, Xiran Yan, Kangbai Rohde, Gustavo K. Kolouri, Soheil Department of Mathematics Vanderbilt University NashvilleTN37240 United States Department of Computer Science Tennessee State University NashvilleTN37209 United States Department of Computer Science Vanderbilt University NashvilleTN37240 United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22908 United States

The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this paper, we focus on circular probability measures, i.e., probability measures supported on the unit circle, and introduce a new computationally efficient metric for these measures, denoted as Linear Circular Optimal Transport (LCOT). The proposed metric comes with an explicit linear embedding that allows one to apply Machine Learning (ML) algorithms to the embedded measures and seamlessly modify the underlying metric for the ML algorithm to LCOT. We show that the proposed metric is rooted in the Circular Optimal Transport (COT) and can be considered the linearization of the COT metric with respect to a fixed reference measure. We provide a theoretical analysis of the proposed metric and derive the computational complexities for pairwise comparison of circular probability measures. Lastly, through a set of numerical experiments, we demonstrate the benefits of LCOT in learning representations of circular measures. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Machine learning

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A confirmatory factorial analysis of the Chatbot Usability Scale: a multilanguage validation

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Personal and Ubiquitous Computing 2023年第2期27卷 317-330页

作者： Borsci, Simone Schmettow, Martin Malizia, Alessio Chamberlain, Alan van der Velde, Frank Group Faculty of Behavioural Management and Social Sciences University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom

The Bot Usability Scale (BUS) is a standardised tool to assess and compare the satisfaction of users after interacting with chatbots to support the development of usable conversational systems. The English version of the 15-item BUS scale (BUS-15) was the result of an exploratory factorial analysis;a confirmatory factorial analysis tests the replicability of the initial model and further explores the properties of the scale aiming to optimise this tool seeking for the stability of the original model, the potential reduction of items, and testing multiple language versions of the scale. BUS-15 and the usability metrics for user experience (UMUX-LITE), used here for convergent validity purposes, were translated from English to Spanish, German, and Dutch. A total of 1292 questionnaires were completed in multiple languages;these were collected from 209 participants interacting with an overall pool of 26 chatbots. BUS-15 was acceptably reliable;however, a shorter and more reliable solution with 11 items (BUS-11) emerged from the data. The satisfaction ratings obtained with the translated version of BUS-11 were not significantly different from the original version in English, suggesting that the BUS-11 could be used in multiple languages. The results also suggested that the age of participants seems to affect the evaluation when using the scale, with older participants significantly rating the chatbots as less satisfactory, when compared to younger participants. In line with the expectations, based on reliability, BUS-11 positively correlates with UMUX-LITE scale. The new version of the scale (BUS-11) aims to facilitate the evaluation with chatbots, and its diffusion could help practitioners to compare the performances and benchmark chatbots during the product assessment stage. This tool could be a way to harmonise and enable comparability in the field of human and conversational agent interaction. © 2022, The Author(s).

关键词： Surveys

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Challenging Weather for License Plate Detection: Impaired visibility 5

Challenging Weather for License Plate Detection: Impaired vi...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Ibrahim, Mohamed Ahmed El-Saady, Kamal Kenk, Mourad A. South Valley University Faculty of Science Dept. Mathematics Qena Egypt South Valley University Faculty of Computers & Artificial Intelligence Dept. Computer Science Hurghada Egypt

ISBN: (纸本)9798350381030

License plate detection is crucial for automatic toll collection, surveillance systems, and traffic management. This paper presents the findings of our investigation employing the YOLOv8 deep learning framework to detect license plates in challenging scenarios such as heavy haze, rain, snow, and sandstorms that severely limit camera operation and computer vision algorithms for traffic scene perception. Rain flow and ice reflections on roadways can cause enormous detection errors, which harm intelligent visual traffic systems. The major goal should be to achieve a high level of accuracy while maintaining real-time inference speed. To ascertain the dependability and robustness of the YOLOv8 system, a thorough assessment procedure was undertaken. Initially, a synthetic dataset was created to emulate challenging settings, such as the presence of haze and other meteorological phenomena, which are often encountered in real-life contexts. Thereafter, a simulation was conducted to evaluate the performance of the YOLOv8 model under poor weather conditions. Our method's remarkable frame processing speed, reaching up to 70 frames per second on conventional hardware, renders it well-suited for real-time applications that need efficient and quick license plate identification. In comparison to established state-of-the-art methodologies, our approach demonstrates superior performance in terms of both accuracy and frame processing time, thereby representing a substantial advancement in the field of license plate detection. A variety of experiments have been undertaken on the CCPD dataset and its several subsets, including the Base, DB, Blur, FN, Rotate, Tilt, and Challenge datasets, which offer a diverse selection of license plate changes. The relevant CCPD subsets yield exceptional outcomes, with accuracies of 99.62%, 96.8%, 98.9%, 99.72%, 99.71%, 99.5%, and 99.1%, respectively. © 2023 IEEE.

关键词： computer vision

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Externally triggered drug delivery systems

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Smart Materials in Medicine 2024年第3期5卷 386-408页

作者： Hu, Huiyang Busa, Prabhakar Zhao, Yue Zhao, Chao Department of Chemical and Biological Engineering University of Alabama TuscaloosaAL35487 United States School of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China Center for Convergent Biosciences and Medicine University of Alabama TuscaloosaAL35487 United States

Externally triggered drug delivery systems empower patients or healthcare providers to utilize external stimuli to initiate drug release from implanted systems. This approach holds significant potential for clinical disease management, offering appealing features like enhanced patient adherence through the elimination of needles and medication reminders. Additionally, it facilitates personalized medicine by granting patients control over the timing, dosage, and duration of drug release. Moreover, it enables precise drug delivery to targeted locations where external stimuli are applied. Advances in materials science, nanotechnology, chemistry, and biology have been pivotal in driving the development of these systems. This review presents an overview of the progress in research on drug release systems responsive to external stimuli, such as light, ultrasound, magnetic fields, and temperature. It discusses the construction strategies of externally triggered drug delivery systems, the mechanisms governing triggered drug release, and their applications in disease management. © 2024 The Authors

关键词： Nanoclay

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