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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Atik, Funda Yesil, Serif Ouarnoughi, Hamza Niar, Smail Ozturk, Ozcan Washington University in St. Louis Computer Science and Engineering Department DoimukhMI United States NVidia UrbanaIL United States Université Polytechnique Hauts-de-France Department of Computer Science Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Sabanci University Computer Science and Engineering Program Electronics Engineering Program Istanbul Turkey

ISBN: (纸本)9798331515966

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to 20 x better performance. © 2024 IEEE.

关键词： Communication Graph Analytics Parallel Frameworks Performance Shared Memory systems

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Secure and Energy-Efficient Framework for Internet of Medical Things (IoMT)-Based Healthcare System

Secure and Energy-Efficient Framework for Internet of Medica...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingaya’s Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engg. and Technology Greater Noida India SILC and Decision Sciences and Modelling Program Victoria University Footscray Australia

ISBN: (纸本)9789819967544

Manufacturing, energy, finance, education, transportation, smart home, and medicine employ IoT technology. IoT solutions can efficiently manage hospital patients and mobile assets to provide high-quality medical services. The Internet of Medical Things (IoMT) integrates IoT with medical equipment to increase patient comfort, cost-effective medical solutions, hospital treatment speed, and personalized healthcare. This work uses Constrained Application Protocol to secure remote patient health data (CoAP). Nevertheless, the CoAP DTLS layer lacks key control, session establishment, and multicast message exchange. Hence, IoMT communication requires an efficient protocol for safe CoAP session formation. Consequently, to address key management and multicast security issues in CoAP, we presented an efficient and secure communication method to establish a secure session key between IoMT devices and distant servers utilizing lightweight, energy efficient, and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). E2SCEC2 can use a smaller key size than Rivest-Shamir-Adleman (RSA) due to its tiny key size. To determine if these algorithms are compatible in limited contexts, the paper examines key creation, signature generation, and verification of E2SCEC2 and RSA algorithms, energy consumption, and radio duty cycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hospitals

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Autoencoder-Enhanced Roommate Recommendation System 6

Autoencoder-Enhanced Roommate Recommendation System

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Zahran, Khaled Amir, Abdallah Elbanhawy, Magy Ghanem, Ingy El-Ghamry, Amir Fouad, Khaled M. Moawad, Ibrahim F. New Mansoura University Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science Mansoura University University of Hertfordshire Hosted by Global Academic Foundation Faculty of Computer and Information Sciences Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Saudi Arabia

ISBN: (纸本)9798350378511

This proposed system is designed for creating a new way of giving personalized recommendations by focusing on people's behaviors and preferences. The system uses traditional machine learning algorithms integrating it with deep learning techniques to get the most out of data and suggest recommendations which are designed according to each individual's unique preferences. A deep learning autoencoder is used to learn a lower-dimensional representation of the data, with an assurance on feature extraction and reconstruction accuracy. The encoded features are passed to be clustered using KMeans, with the effectiveness of clustering estimated through internal validation metrics such as silhouette score, Calinski-Harabasz index, and Davies-Bouldin index. Also, t-Distributed Stochastic Neighbor Embedding (t-SNE) is utilized for visualizing clustered data in a simple manner. Additionally, a silhouette plot is given to provide a visual representation of the silhouette scores across clusters, highlighting the degree of cohesion within clusters and separation between them. Finally, using cosine similarity that identifies similar users within the same cluster. © 2024 IEEE.

关键词： Contrastive Learning

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Channels engineering in Magnetic Recording: From Theory to Practice

IEEE BITS the Information Theory Magazine

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IEEE BITS the Information Theory Magazine 2023年第3期3卷 6-49页

作者： Garani, Shayan Srinivasa Vasic, Bane Indian Institute of Science Department of Electronics Systems Engineering Bengaluru560012 India University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Information theory, coding theory, and signal processing have significantly shaped magnetic read/write channels engineering through a chronological sequence of innovations and research advancements, cognizant of the underlying physical processes. In this magazine article, we provide an overview of magnetic recording technologies leading to the channels engineering aspects, central to this article. We survey important coding and signal processing algorithms along with some design architectures that have made it to practice within hard disk drives for magnetic recording channels. The push toward realizing ultrahigh densities (> 4 Tb/in2>) on magnetic disks with ultrahigh throughput rates (>10 Gb /s) necessitates the development of native two-dimensional (2-D) coding and signal processing algorithms and architectures within the framework of 2-D magnetic recording (TDMR) along with read head engineering and changes to the recording physics. We also provide an overview of novel channels engineering solutions covering all aspects of TDMR channels driven from a systems science perspective. The innovations and research advances described in this article may serve a broad engineering audience in other areas as well. © 2021 IEEE.

关键词： Magnetic recording

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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing 24

TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wu, Jinting Zheng, Haodong Wang, Yu Yue, Tai Zhang, Fengwei Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9798400706011

Hardware-based tracing, being efficient, can be a good alternative to the computationally-expensive software-based instrumentation in binary-only greybox fuzzing. However, it only records all branches within a specified address range, lacking the flexibility to re-filter them. To overcome these limitations, this paper introduces Tatoo, a hardware platform that employs tagged architectures and hardware tracing to enable users to perform instruction-level tagging, which can significantly reduce the volume of traced data and improve fuzzing efficiency. Tatoo also supports recording the dataflow information for smart mutations. Implemented on a real hardware FPGA platform, Tatoo demonstrates a mere 8.7% performance overhead. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Unveiling Memorization in Code Models 24

Unveiling Memorization in Code Models

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Yang, Zhou Zhao, Zhipeng Wang, Chenyu Shi, Jieke Kim, Dongsun Han, Donggyun Lo, David School of Computing and Information Systems Singapore Management University Singapore University of Copenhagen Department of Computer Science Copenhagen Denmark School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of Royal Holloway University of London Department of Computer Science London United Kingdom

ISBN: (纸本)9798400702174

The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of lines of code from both open-source and private repositories. A code model memorizes and produces source code verbatim, which potentially contains vulnerabilities, sensitive information, or code with strict licenses, leading to potential security and privacy issues. This paper investigates an important problem: to what extent do code models memorize their training data? We conduct an empirical study to explore memorization in large pre-trained code models. Our study highlights that simply extracting 20,000 outputs (each having 512 tokens) from a code model can produce over 40,125 code snippets that are memorized from the training data. To provide a better understanding, we build a taxonomy of memorized contents with 3 categories and 14 subcategories. The results show that the prompts sent to the code models affect the distribution of memorized contents. We identify several key factors of memorization. Specifically, given the same architecture, larger models suffer more from memorization problem. A code model produces more memorization when it is allowed to generate longer outputs. We also find a strong positive correlation between the number of an output's occurrences in the training data and that in the generated outputs, which indicates that a potential way to reduce memorization is to remove duplicates in the training data. We then identify effective metrics that infer whether an output contains memorization accurately. We also make suggestions to deal with memorization. © 2024 ACM.

关键词： Open source software

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Network Traffic Classifiers for Internet of Things Intrusion Detection using Machine Learning Classifiers 3

Network Traffic Classifiers for Internet of Things Intrusion...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Uniyal, Anita Mishra, Amit Kumar Manushi Pant, Yogesh Computer Science & Engineering GRD IMT Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India GRD IMT Department of Computer Science & Engineering Dehradun India Software Business Systems Consultanting India Private Limited India

ISBN: (纸本)9798350303360

In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research. © 2023 IEEE.

关键词： Intrusion detection

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An Empirical Study of Machine Learning Techniques for ODI Bowler Performance Evaluation 4

An Empirical Study of Machine Learning Techniques for ODI Bo...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Das, Biswaranjan Das, Nayan Ranjan Mondal, Bappaditya Sur, Ratul Paul, Sushmita Das, Basudha Maity, Sutanu Academy of Technology Department of Engineering Science & Humanities West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Engineering West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Business Systems West Bengal Hooghly712121 India

ISBN: (纸本)9798350389128

The objective of the paper is to construct an innovative ranking system based on various Machine Learning (ML) techniques specifically tailored for One Day International (ODI) male players. It aims to create a recommender system that produces bowler rankings closely aligned with the International Cricket Council (ICC) Rankings, with a focus on enhancing accuracy and reliability. The dataset encompasses a wide range of match parameters, performance metrics, and player statistics, providing a comprehensive foundation for insightful analysis. By employing data-driven techniques, the paper aims to highlight key performance indicators that significantly influence bowler rankings. We performed an analysis among five machine learning models that can produce rankings of bowlers and select the best model whose predictions are closest to the ICC's official rankings. The rankings obtained from the five machine learning algorithms have been compared with ICC rankings utilizing the Spearman Rank Correlation Coefficient. The results highlight that the Artificial Neural Networks (ANN) exhibit the best outcomes with a rank correlation of 0.7550. © 2024 IEEE.

关键词： Adversarial machine learning

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