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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States iDEA Lab University of Calabria Rende Italy

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2023, CC BY.

关键词： Supply chains

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Evaluating Actors' Performance Using Machine Learning for Gesture Classification

Evaluating Actors' Performance Using Machine Learning for Ge...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hossam Ahmed Omar Amr Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Helwan University Giza Egypt Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

In the context of advancing technologies in the entertainment industry, this study explores the utilization of machine learning (ML) for assessing actors' performances. The integration of technology in that industry is limited. The methodology encompasses acquiring performance ratings from the model to recognize predefined criteria. These ratings are then subjected to comparative analysis to gauge the system's effectiveness. The system showed 83.2% accuracy using MediaPipe to extract the positions of body part points for each gesture. Then, using One Dollar Py library, positive, mediocre, and negative models are developed using the patterns extracted from MediaPipe. We benchmarked the system against an assessment by a professional director. This work suggests a pathway to the use of Machine Learning tools in the industry and could be viewed as an innovative method for automating performance assessment with real-world applications that extend across the film-making process.

关键词： Accuracy Entertainment industry Machine learning Benchmark testing Activity recognition Ubiquitous computing Libraries

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SocialEdge: Socialized Learning-Based Request Scheduling for Edge-Cloud Systems

SocialEdge: Socialized Learning-Based Request Scheduling for...

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International Conference on Distributed Computing Systems

作者： Ziwei Wang Yunfeng Zhao Chao Qiu Qiang He Xin Wang Xiaofei Wang Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and DigitalEconomy (SZ) Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

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Exploring the Tangible Impact of Artificial intelligence and Machine Learning: Bridging the Gap between Hype and Reality

Exploring the Tangible Impact of Artificial Intelligence and...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Rahul Dattangire Ruchika Vaidya Divya Biradar Ashish Joon Data Engineering Publicis Sapient Houston Texas USA Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA Software Developer Andhus Technologies Inc Pensacola FL

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.

关键词： Technological innovation Ethics Decision making Finance Machine learning Production Pressing Medical services Writing Problem-solving

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： G. Sujatha Manimuthu Ayyannan S. Gayathri Priya V. Arun A. N. Arularasan M Jogendra Kumar Department of Electronics and Communication Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Senior Software Engineering Manager AI/ML and Data Expert San Francisco Bay Area California USA Department of Electronics and Communication R. M. D. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science &Engineering Madanapalle Institute of Technology Madanapalle Andhra Pradesh Department of Artificial Intelligence &Data Science Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.

关键词： Uniform resource locators Smart cities Phishing Biological system modeling Artificial neural networks Feature extraction Classification algorithms

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Rényi Divergence Deep Mutual Learning

arXiv

引用

arXiv 2022年

作者： Huang, Weipeng Huang, Weipeng Fuzzy Tao, Junjie Deng, Changbo Fan, Ming Wan, Wenqiang Xiong, Qi Piao, Guangyuan Tencent Security Big Data Lab Shenzhen China School of Software Engineering Xi’an Jiaotong University Xi’an China The MoEKLINNS Laboratory School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China Department of Computer Science Maynooth University Maynooth Ireland

This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm. We propose using Rényi divergence instead of the Kullback–Leibler divergence, which is more flexible and tunable, to improve vanilla DML. This modification is able to consistently improve performance over vanilla DML with limited additional complexity. The convergence properties of the proposed paradigm are analyzed theoretically, and Stochastic Gradient Descent with a constant learning rate is shown to converge with O(1)-bias in the worst case scenario for nonconvex optimization tasks. That is, learning will reach nearby local optima but continue searching within a bounded scope, which may help mitigate overfitting. Finally, our extensive empirical results demonstrate the advantage of combining DML and the Rényi divergence, leading to further improvement in model generalization. © 2022, CC BY.

关键词： Contrastive Learning

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Expression of Concern for: Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Expression of Concern for: Medical Image Fusion Solid Works ...

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Sustainable Computing and data Communication Systems (ICSCDS), International Conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9781665491990

ISBN: (纸本)9781665492003

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

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