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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling software

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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

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IEEE International Conference on Big Data

作者： Michael O. Chen Julian Hodge Peter L. Jin Ella Protz Elizabeth Wong Ruth Obe Ehsan Shakeri Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Vijay R. Sharma Sina Mossahebi Lei Ren Stephen W. Peterson Jerimy C. Polf Departments of Mathematics Dartmouth College USA Dept. of Mathematics and Statistics U. of Maryland USA James M. Bennett High School Salisbury MD USA Department of Mathematics and Sciences Florida Atlantic University USA Department of Mathematics Brookdale Community College USA Dept. of Computer Science and of Software Engineering U. of Houston Clear Lake USA Dept. of Information Systems U. of Maryland USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa M3D Inc. USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel ‘Big-data REU Integrated Development and Experimentation’ (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research.

关键词： Particle beams Recurrent neural networks Accuracy Pipelines Phantoms Scattering Cameras Data models software Radiation therapy

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Navigating Adverse Weather: An Exploration of YOLOv8's Object Detection in Fog

Navigating Adverse Weather: An Exploration of YOLOv8's Objec...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Muhammad Haris Mansoor Iqbal Muhammad Osama Malik Syed Ahmad Saleem Bokhari Saad Alahmari Farooq Azam Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia USA Department of Computer Science University of Wah Wah Cantt Pakistan Department of Business Analytics University of Tulsa Oklahoma USA Harvard Extension School Harvard University Cambridge MA USA Department of Computer Science Northern Border University Arar Saudi Arabia Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation due to light scattering and absorption diminishes the performance of even the most advanced models trained on clear-weather datasets. This paper uses the Foggy Cityscapes dataset to evaluate YOLOv8, a cutting-edge object detection model, under extreme foggy conditions. We utilize YOLOv8's sophisticated architecture, featuring the CSPDarknet backbone and Path Aggregation Network (PANet), for efficient feature extraction and multi-scale detection. Our approach incorporates image enhancement techniques and data augmentation to mitigate the effects of fog on feature visibility. We conduct a comprehensive performance assessment using metrics like mean average precision (mAP) precision-recall curves to evaluate the model's robustness and reliability. Additionally, we benchmark YOLOv8 against previous YOLO versions, detailing improvements and trade-offs in detection speed and accuracy. We also propose a hybrid framework combining multi-task learning to improve image quality and object detection in low-visibility scenarios.

关键词： YOLO Image quality Computational modeling computer architecture Multitasking Feature extraction Robustness Real-time systems Image enhancement Meteorology

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Modeling the Productivity of a Sugar Factory using Machine Learning Methods

Modeling the Productivity of a Sugar Factory using Machine L...

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International computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joe Y. Mambu Rismayani Jay Idoan Sihotang Vivi Peggy Rantung Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dept. of Software Engineering Dipa Makassar University Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia

ISBN: (纸本)9781665453967

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as “good” therefore its usability is acceptable.

关键词： Headphones COVID-19 Pandemics Optimized production technology Virtual reality Games Usability

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Combating Deepfakes: Multi-LSTM and Blockchain as Proof of Authenticity for Digital Media

Combating Deepfakes: Multi-LSTM and Blockchain as Proof of A...

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2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020

作者： Ki Chan, Christopher Chun Kumar, Vimal Delaney, Steven Gochoo, Munkhjargal Chaoyang University of Technology Dept. of Information Management Taichung Taiwan Ryerson University and Capital Blockchain Inc. Dept. of Computer Science Toronto Canada Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781728170312

Malicious use of deep learning algorithms has allowed the proliferation of high realism fake digital content such as text, images, and videos, to exist on the internet as readily available and accessible consumable content. False information provided through algorithmically modified footage, images, audios, and videos (known as deepfakes), coupled with the virality of social networks, may cause major social unrest. The emergence of misinformation from fabricated digital content suggests the necessity for anti-disinformation methods such as deepfake detection algorithms or immutable metadata in order to verify the validity of digital content. Permissioned blockchain, notably Hyperledger Fabric 2.0, coupled with LSTMs for audio/video/descriptive captioning is a step towards providing a feasible tool for combating deepfake media. Original content would require the original artist attestation of untampered data. The smart contract combines a varied multiple LSTM networks into a process that allows for the tracing and tracking of a digital content's historical provenance. The result is a theoretical framework that enables proof of authenticity (PoA) for digital media using a decentralized blockchain using multiple LSTMs as a deep encoder for creating unique discriminative features;which is then compressed and hashed into a transaction. Our work assumes we trust the video at the point of reception. Our contribution is a decentralized blockchain framework of deep discriminative digital media to combat deepfakes. © 2020 IEEE.

关键词： Smart contract

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Technical Debt Management: The Road Ahead for Successful software Delivery

Technical Debt Management: The Road Ahead for Successful Sof...

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software engineering: Future of software engineering (ICSE-FoSE), IEEE/ACM International Conference on

作者： Paris Avgeriou Ipek Ozkaya Alexander Chatzigeorgiou Marcus Ciolkowski Neil A. Ernst Ronald J. Koontz Eltjo Poort Forrest Shull Dept. of Computing Science University of Groningen Groningen The Netherlands Software Engineering institute Carnegie Mellon University Pittsburgh PA USA Dept. of Applied informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company Mesa AZ USA CGI Rotterdam The Netherlands

Technical Debt, considered by many to be the ‘silent killer’ of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to “bankrupt” those systems. software engineers and organizations that develop software-intensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt; we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence.

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Predicting Students' Academic Performance Through Supervised Machine Learning 2

Predicting Students' Academic Performance Through Supervised...

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2nd International Conference on Information Science and Communication Technology, ICISCT 2020

作者： Bhutto, Sana Siddiqui, Isma Farah Arain, Qasim Ali Anwar, Maleeha Mehran University of Engineering and Technology Department of Software Eengineering Jamshoro Pakistan University of Karachi Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9781728168999

There are many supervised and unsupervised types of machine learning approaches that are used to extract hidden information and relationship between data, which will eventually, helps decision-makers in the future to take proper interventions. The variety of powerful algorithms used in different areas of daily life that includes our educational system as well. This paper introduces students' academic performance prediction model that uses supervised type of machine learning algorithms like support vector machine and logistic regression. The results supported by various experiments using different technologies are compared and it is showed that sequential minimal optimization algorithm outperforms by achieving improved accuracy as compared to logistic regression. And the knowledge found through this research can help educational institutes to predict the future behavior of students so that they can categorize their performance into good or bad. The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features to work on like teacher's performance, student's motivation that will eventually decrease the student's dropout ratio. © 2020 IEEE.

关键词： Students

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CHSH Violations using Dynamic Circuits

arXiv

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arXiv 2025年

作者： Waring, Jean-Baptiste Pere, Christophe Le Beux, Sébastien Dept. of Electrical & Computer Engineering Concordia University MontréalQC Canada Dept. of Computer Science & Software Engineering INTRIQ Laval University QuébecQC Canada PINQ2 Canada

Scalable quantum computing relies on high-quality, long-range entanglement, a challenge on noisy, near-term devices. The need for practical insights for near-term algorithm design calls for trade-offs exploration in implementing dynamic circuits on current hardware. In this work, we experimentally compare three CNOT implementations for generating Bell states across varying qubit separations on a 127-qubit IBM Quantum Eagle processor (ibm_ quebec): a unitary (SWAP-based) approach, a dynamic approach with mid-circuit measurements and classical feedforward, and a post-processed approach. We use Clauser-Horne-Shimony-Holt (CHSH) inequality violations to quantify entanglement quality. We observe that, beyond 10 qubits, dynamic circuits surpass the unitary approach, demonstrating improved distance-dependent entanglement preservation. The post-processed approach consistently yields the highest CHSH values, reaching |S| > 2 up to 13 qubits. Our results underscore the critical need for faster classical feedforward and higher readout fidelity. © 2025, CC BY-NC-SA.

关键词： Qubits

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An Efficient Localization for Indoor Environment using Classification Algorithms

An Efficient Localization for Indoor Environment using Class...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

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