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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Parulian, Onesinus Saut Aji, Indra Budi Patria, Omega Joel Nugroho, Ariyanto Adi Susanto, Nanang Mariyanto, Totox Salsabila, Nurul Jannah Haris, Muhammad Manik, Lindung Parningotan Universitas Nusa Mandiri Jakarta Computer Science Department Indonesia Universitas Nusa Mandiri Jakarta Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Computer Science Department Indonesia

ISBN: (纸本)9798350376210

Rapid development of software significantly facilitates developers in all phases of software development. Developers can easily leverage frameworks, content management systems (CMS), and libraries available in each programming language. With the large and rapid addition of frameworks, CMS, and libraries, developers must be able to choose precisely which ones to use based on the requirements of their software development projects. The utilization of machine learning for segmentation or clustering enables developers to identify groups of frameworks, CMS, and libraries by leveraging features obtained from static analysis, such as the size of codebases, complexities, structures, and other code characteristics. One algorithm that can be used for segmentation is K-means clustering. However, the availability of datasets is crucial as it can offer information about the code characteristics of each software. The use of machine learning on datasets can be an ongoing research area in software development related to utilizing code characteristics in software analysis. This study provides a novel dataset for the domains of software development and machine learning. The dataset we created contains 30 instances and 68 features that can be utilized for related or future research. This research utilizes the Silhouette score to determine the optimal k value. © 2024 IEEE.

关键词： Software design

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DCNN and LSTM based Plant Leaf Disease Detection Model For Edge IoT Device 5

DCNN and LSTM based Plant Leaf Disease Detection Model For E...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Selvam, L. Mary, S. Roseline Manimuthu, A. Pandian, M. Faculty of Science and Humanities Srmist Department of Computer Applications chengalpattu Kattankulathur India Loyola College Department of Mathematics Chennai India Loyola College Department of Data Science Chennai India

ISBN: (纸本)9798350383522

Edge Computing is an incredibly practical and affordable alternative IoT device for cloud computing that uses a revolutionary methodology. Not only does edge processing reduce the cost of systems administration frameworks, but it also improves edge-cloud latency, which is essential for all critical applications. Plant diseases have a catastrophic impact on food production security and can lead to a sharp decline in the quantity and quality of agricultural products. In severe cases, plant diseases may continue to be the cause of partial grain failure. As a result, an automated method for diagnosing and detecting plant diseases is vitally needed for all agricultural data. In this paper, we have proposed a plant disease detection method based on transfer learning and tested the model's performance on IoT edge device. Long-Short Term Memory (LSTM) is used in the proposed system to classify plant leaf diseases based on data extracted by CNN. An internal memory within the LSTM network allows it to learn from critical experiences with long-term conditions. Therefore, the combination of CNN and LSTM layout features significantly enhances classification. It is suggested to use a Deep Convolutional Neural Network based on a pre-trained MobileNetV1 feature extractor and an LSTM classifier. The proposed model's performance is contrasted with that of other pre-trained models, including NASNetMobile and MobileNetV2. The proposed model outperformed other models, achieving a validation accuracy of 0.9892. © 2024 IEEE.

关键词： Edge Computing MobileNet Plant Disease Detection Transfer Learning

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mann, Aleesa Jadhav, Ajinkya P. Matovu, Richard Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States Gannon University Department of Computing and Information Science EriePA United States

ISBN: (纸本)9798350362480

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible. © 2024 IEEE.

关键词： COVID-19

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Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions 37

Adversarial Training for Graph Neural Networks: Pitfalls, So...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Gosch, Lukas Geisler, Simon Sturm, Daniel Charpentier, Bertrand Zügner, Daniel Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Microsoft Research United States

ISBN: (纸本)9781713899921

Despite its success in the image domain, adversarial training did not (yet) stand out as an effective defense for Graph Neural Networks (GNNs) against graph structure perturbations. In the pursuit of fixing adversarial training (1) we show and overcome fundamental theoretical as well as practical limitations of the adopted graph learning setting in prior work;(2) we reveal that flexible GNNs based on learnable graph diffusion are able to adjust to adversarial perturbations, while the learned message passing scheme is naturally interpretable;(3) we introduce the first attack for structure perturbations that, while targeting multiple nodes at once, is capable of handling global (graph-level) as well as local (node-level) constraints. Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Adversarial machine learning

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QuMIN: quantum multi-modal data fusion for humor detection

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Multimedia Tools and Applications 2025年第18期84卷 18855-18872页

作者： Phukan, Arpan Haq Khan, Anas Anwarul Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801106 India Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India School of AI and Data Science Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Humour detection has attracted considerable attention due to its significance in interpreting dialogues across text, visual, and acoustic modalities. However, effective methods to map correlations among different modalities remain an active area of research. In this study, we go beyond traditional machine learning techniques by introducing a Variational Quantum Circuit (VQC) that capitalizes on the inherent quantum properties of superposition, entanglement, and interference. Our proposed model, Quantum Multi-Modal data Fusion (QuMIN), is designed to better capture and reproduce the interaction across modalities, as well as the internal correlations within each modality. Our introduction of the novel VQC, which augments the DialogueRNN baseline with only an additional 4,809 parameters, signifies a substantial advancement in multi-modal humor detection with improvements of 12.34% in precision, 8.84% in recall and 10.57% in F1 score compared to the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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LLM4Decompile: Decompiling Binary Code with Large Language Models

LLM4Decompile: Decompiling Binary Code with Large Language M...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tan, Hanzhuo Luo, Qi Li, Jing Zhang, Yuqun Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9798891761643

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4DecompileRef models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and executability while complementing conventional tools for optimal results. © 2024 Association for Computational Linguistics.

关键词： Binary codes

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Knowledge Graphs for Competency-Based Education

Knowledge Graphs for Competency-Based Education

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Linxen, Andrea Endel, Florian Opel, Simone Beecks, Christian FernUniversität in Hagen Chair of Data Science Hagen Germany FernUniversitäat in Hagen Department for Computer Science Hagen Germany

ISBN: (纸本)9798350324457

The project Knowledge Graphs for competency-based Education (KG4CBE) conducts educational data science research to establish competency-based instruction in higher-education programs. In this paper, we propose the design of a knowledge graph to examine the impact of instructional design on student-teacher interaction to facilitate complex learning. For this purpose, the knowledge graph will incorporate the components of an online introductory data science course, including educational materials and learning tasks created with the Four Component Instructional Design (4C/ID) model. Furthermore, the knowledge graph will incorporate data recording the behaviors, interactions and assessments of participating students. To study the competency-based instruction process, the proposed knowledge graph must be scalable to the big data quantities common in educational settings. Therefore, the knowledge graph will be deployed as a tool with accompanying routines to acquire, simulate and load educational data. Furthermore, this tool will provide methods to interact with and visualize the stored information. As future research, we aim to evaluate the proposed knowledge graph in a large-scale educational design research study, to examine the impact of monitoring, forecasting and recommendations in complex learning settings. © 2023 IEEE.

关键词： educational big data educational data science instructional design knowledge graph

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CT-Scan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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