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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Xiaoyu Long, Jiangxuan Song, Zhao Zhou, Tianyi Stevens Institute of Technology Department of Computer Science HobokenNJ United States South China University of Technology School of Software Engineering Guangzhou China University of California Berkeley The Simons Institute for the Theory of Computing BerkeleyCA United States University of Southern California Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350362480

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n3) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ2) respectively, where m = Ω(n4) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ2). © 2024 IEEE.

关键词： Trees (mathematics)

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A Survey on Vehicular Edge Computing Architectures 13

A Survey on Vehicular Edge Computing Architectures

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lee, Jaehwan Na, Woongsoo Kongju National University Department of Computer Science and Engineering Korea Republic of Kongju National University Department of Software Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the recent development of intelligent transportation system (ITS), vehicular applications are being actively studied including autonomous driving, vehicle platooning, that require real-time and high-performance processing resources. Accordingly, various system architectures are being studied to satisfy the high-performance computing requirements. Although the mobile cloud computing architecture provides large-scale computing resources, it is not suitable for vehicular environment that requires real-time processing due to the structure in which the edge node and the computing resources are geographically distant. The mobile edge computing (MEC) architecture places computing resources around edge devices. However, it does not consider edge devices moving at a high speed in a limited service area. Vehicular edge computing (VEC) and vehicular fog computing paradigm have emerged to address the latency requirement with the development of vehicular communications. In this paper, such a computation offloading architecture is presented and analyzed. © 2022 IEEE.

关键词： computer architecture

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A Roadmap for Artificial Intelligence Augmented software Development Life Cycle: Aspects of Knowledge Vaporization 11

A Roadmap for Artificial Intelligence Augmented Software Dev...

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11th International Conference on Information Technology, ICIT 2023

作者： Alzyadat, Wael Al-Madi, Mohammad Muhairat, Mohammad Al-Madi, Nagham Altarawneh, Feras Alzaytoonah University of Jordan Faculty of Science and IT Department of Software Engineering Amman Jordan Al-Zaytoonah University of Jordan Computer Science Amman Jordan Alzaytoonah University of Jordan Artificial Intelligence Faculty of Science and IT Amman Jordan

ISBN: (纸本)9798350320060

Artificial Intelligence Augmented software Development Life Cycle is a process for developing software that incorporates AI techniques and technologies. One aspect of AIASDLC is knowledge vaporization, which refers to the process of extracting and encapsulating knowledge from the AI model during development and making it available for use in other parts of the software development life cycle. The advantages of AIASDLC include;improved performance, increased efficiency, better decision-making, better user experience, better scalability, better security and compliance, and knowledge vaporization. Requirement engineering is the process of gathering, analyzing, and specifying the requirements for a software system. The context boundary, domain knowledge, application domain, and system boundary are all important concepts in requirement engineering that help define the scope and constraints of the system being developed. Understanding the context boundary is important because it helps identify the external factors that will affect the requirements of the system. Domain knowledge is the understanding and expertise in a specific field or area that is necessary for effective requirement engineering. The application domain is the specific area or field where the system will be used, and it is important to understand the application domain to gather and document the correct requirements for the system © 2023 IEEE.

关键词： Vaporization

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease 5

Identification of Candidate Drugs and Molecular Pathways for...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hosen, Md. Faruk Yasmin, Mst. Farjana Basar, Md. Abul Trisha, Arifa Ferdoushi Shadhin, Khairul Alam Mollah, Md. Sarwar Uddin, Muhammad Shahin Department of Computing and Information System Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Purbachal American CityKanchon 1460 Bangladesh Department of Software Engineering Ashulia Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798350394290

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs. © 2023 IEEE.

关键词： Proteins

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A Data-Driven Approach to Forecasting Bangladesh Next-Generation Economy 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Shohug, Md. Mahfuj Hasan Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Rahaman, Ashikur Department of Software Engineering Daffodil International University Dhaka1216 Bangladesh Department of General Educational Development Daffodil International University Dhaka1216 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (数字)9789819900473

ISBN: (纸本)9789819900466

Bangladesh is the most populous country on the planet. We recognized Bangladesh as an independent sovereign country, yet despite its large population, Bangladesh lags far behind the world’s wealthiest nations. In the developed world, especially in terms of economics, a country’s Gross Domestic Product (GDP) is the monetary value of all completed labor and products produced within its borders during a period. It is concerned with the overall measurement of all financial transactions. In this research, we use a machine learning time series ARIMA model to correctly estimate the Bangladesh GDP Growth Rate with the order of (0, 1, 1). The auto ARIMA function generates a minimal AIC value, which is used to verify the model. The SARIMAX (0, 1,1, 6) model can predict the GDP with 87.51% accuracy. We implement this machine learning time series ARIMA model on the web application;GDP indicator. Users may find the GDP growth rate for Bangladesh in the future here, as well as watch our country’s upcoming economy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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A Survey on Vehicular Communication Methods 13

A Survey on Vehicular Communication Methods

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lee, Jaehwan Na, Woongsoo Kongju National University Department of Computer Science and Engineering Korea Republic of Kongju National University Department of Software Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Recently, interest in vehicular applications has increased such as autonomous driving and vehicle platooning. The importance of supporting driving safety is becoming more prominent. In order to provide driving safety, the vehicle to vehicle (V2V) communication standard stipulates that a vehicle safety message is periodically broadcasted to nearby vehicles every 100 ms. However, as the number of vehicles on the road increases, communication channels become easily congested, making it difficult to transmit and receive safety messages thereby increase the risk of traffic accidents. In order to address the problem, various V2V communication methods that improve the existing channel contention method have been proposed. In this paper, we introduce and analyze the communication methods for vehicular communications. © 2022 IEEE.

关键词： Vehicle safety

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer 26

Identification of Molecular Biomarkers and Key Pathways amon...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Yasmin, Mst. Farjana Faruk Hosen, Md. Basar, Md. Abul Alam Shadhin, Khairul Trisha, Arifa Ferdoushi Shahin Uddin, Muhammad Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh Department of Computing and Information System Dhaka Ashulia Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh Department of Software Engineering Dhaka Ashulia Bangladesh

ISBN: (纸本)9798350359015

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network's top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested. © 2023 IEEE.

关键词： COPD Differential expressed genes Drug compounds enrichment analysis Hub gene IPF Lung cancer Protein-protein network

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Breaking the Vault: A Case Study of the 2022 LastPass Data Breach

arXiv

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arXiv 2025年

作者： Gentles, Jessica Fields, Mason Goodman, Garrett Bhunia, Suman Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States

Managing the security of employee work computers has become increasingly important as today’s work model shifts to remote and hybrid work plans. In this paper, we explore the recent 2022 LastPass data breach, in which the attacker obtained sensitive customer data by exploiting a software vulnerability on a DevSecOps engineer’s computer. We discuss the methodology of the attacker as well as the impact this incident had on LastPass and its customers. Next, we expand upon the impact the breach had on LastPass as well as its customers. From this, we propose solutions for preparing for and mitigating similar attacks in the future. The aim of this paper is to shed light on the LastPass incident and provide methods for companies to secure their employee base, both nationally and internationally. With a strong security structure, companies can vastly reduce the chances of falling victim to a similar attack. © 2025, CC BY.

关键词： Cloud security

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Task Allocation Optimization Strategy in UAV-enabled Mobile Edge Computing System

Task Allocation Optimization Strategy in UAV-enabled Mobile ...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Yang, Sanhuan Su, Zhou Xu, Qichao Xing, Rui Fang, Dongfeng Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China California Polytechnic State University San Luis Obispo Department of Computer Science and Software Engineering United States

ISBN: (纸本)9798350333336

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost. © 2023 IEEE.

关键词： Mobile edge computing

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Systematic Analysis of Several Deep Learning Approaches for COVID-19 Detection Using X-ray Images 3

Systematic Analysis of Several Deep Learning Approaches for ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Shakil, Rashiduzzaman Akter, Bonna Javed Mehedi Shamrat, F.M. Jahan, Nusrat Hasan, Shakibe Khater, Ankit Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Information Technology & Management Dhaka Bangladesh European University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Jadavpur University Department of Computer Science and Engineering India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

COVID-19 is a virus-borne malady. A clinical study of infected COVID-19 patients found that most COVID-19 patients suffered lung infection after contracting the disease. Consequently, chest X-rays are a more effective and lower-cost imaging technique for diagnosing lung-related problems. This study used deep learning models, including MobileNetV2,DenseNet201, ResNet50, and VGG19, for COVID-19 prediction. For the study, we used chest X-ray image data for binary classification of COVID-19. 7207 chest X-ray image data were obtained from the Kaggle repository, with 5761 being utilized for training and 1446 being used for validation. A comparative analysis was conducted among the models and examined their accuracy. It has been determined that the DenseNet201 models achieved the highest accuracy of 93.02% for detecting COVID-19 in the lowest compilation time of 27secs. The models, MobileNetV2, ResNet50, and VGG19 had the accuracy rate of 77.28%, 65.86% and 74.92%, respectively. The research indicates that the DenseNet201 model is the most effective in detecting COVID-19 using x-ray imaging. © 2022 IEEE.

关键词： COVID-19

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