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A secure IoT limited Devices by applying Multi-Round Lightweight Cryptography 25

A secure IoT limited Devices by applying Multi-Round Lightwe...

引用

25th international Arab conference on Information technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Abdulqawi, Mohammad Badarneh, Hazem Jihad Al-Shaikh, Ala'a Asassfeh, Mahmoud Al-Sherideh, Ala'a Saeb Samara, Ghassan Elrashidi, Ali Alazaidah, Raed Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

The Internet of Things (IoT) is considered the base factor, that helps in developing many sectors. Additionally, IoT provides novel applications and services, i.e. smart homes, connected cars, and industrial automation. However, this development aggravates the security and privacy issues. This is because the connected devices may be vulnerable to cyber attacks in addition to generated data. Accordingly, it is of utmost importance to deploy appropriate security measures to alleviate these attacks. To overcome the mentioned problem, this work employs lightweight cryptography on limited processing capabilities of IoT devices i.e. limited memory, computing power, and battery life. These limited capabilities obstruct employing cryptography methods that require nontrivial computational resources. This work presents a multiround lightweight cryptography approach for IoT devices, which defines the security and protection of generated and transferred data. Multi-round cryptography mitigates the required device resources while ensuring robust protection and maintaining the stability of IoT devices. © 2024 IEEE.

关键词： Cyber attacks

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LMSPNet: Improved Lightweight Network for Multi-Person Sitting Posture Recognition 3

LMSPNet: Improved Lightweight Network for Multi-Person Sitti...

引用

3rd IEEE international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Jiao, Shuyang Xiao, Yubin Wu, Xuan Liang, Yanchun Liang, Yi Zhou, You Jilin University Computer Science and Technology Changchun China Zhuhai College of Science and Technology School of Computer Science Zhuhai China

ISBN: (纸本)9798350335262

Incorrect sitting posture may lead to health problems. Therefore, effective sitting posture recognition can remind individuals to maintain correct sitting posture and reduce discomfort. Traditional methods for sitting posture recognition have limitations in terms of high cost and slow inference speed. To address these issues, we propose a novel model called LMSPNet for multi-person sitting posture recognition. This model first employs the Light Convolution Core (LCC) to reduce the complexity of the model and then introduces Convolutional Block Attention Module (CBAM) to adaptively adjust the receptive field in the neural network to capture global contextual information, thereby enabling the model to better learn relationships between different channels. We construct the first human sitting posture dataset to evaluate the performance of LMSPNet. Experimental results demonstrate that, compared to the baseline models, our LMSPNet achieves state-of-the-art results with an accuracy of 99.57%. Therefore, our model is expected to become a powerful tool for multi-person sitting posture recognition. © 2023 IEEE.

关键词： Convolution

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Semantic Knowledge Base for the Emotional Coloring Analysis of Kazakh Texts 9

Semantic Knowledge Base for the Emotional Coloring Analysis ...

引用

9th international conference on computer Science and Engineering, UBMK 2024

作者： Yergesh, Banu Kabdylova, Dinara Mukhamet, Tilekbergen L.N. Gumilyov Eurasian National University Faculty of Information Technologies Astana Kazakhstan L.N. Gumilyov Eurasian National University Digital development and online learning department Astana Kazakhstan L.N. Gumilyov Eurasian National University Faculty of Information Technology Astana Kazakhstan

ISBN: (纸本)9798350365887

Lexical resources such as dictionaries and thesauri are crucial for solving various tasks in the field of natural language processing (NLP). This paper presents the development and application of a semantic knowledge base specifically designed for sentiment analysis of Kazakh texts. Given the low-resource nature of the Kazakh language, there has been a significant gap in available lexical resources with annotated sentiment. Our work addresses this gap by creating a comprehensive semantic base consisting of over 16,000 words and phrases, annotated on a 5-point sentiment scale ranging from -2 (very negative) to 2 (very positive). This base includes various parts of speech such as adjectives, nouns, verbs, adverbs, interjections, and emoticons, and considers contextual variability in word sentiment. We also developed and implemented a hybrid sentiment analysis model that leverages this semantic base to accurately determine the sentiment orientation of Kazakh texts at both the sentence and document levels. The effectiveness of this model is demonstrated through its application in various domains, including social media monitoring, marketing, healthcare, and education. Our approach opens new opportunities for research and practical applications in the field of natural language processing for low-resource languages. © 2024 IEEE.

关键词： Latent semantic analysis

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Enhancing Ovarian Cancer Diagnosis Through Deep Learning: A VGG19-Based Approach for Subtype Identification

Enhancing Ovarian Cancer Diagnosis Through Deep Learning: A ...

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2024 OPJU international technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Agarwal, Muskan Aggarwal, Priyanshi Sunil, G. Gill, Kanwarpartap Singh Rawat, Ramesh Singh Chitkara University Computer Science & Engineering Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Sr University School of Computer Science & Artificial Intelligence Warangal India Graphic Era Deemed to Be University Computer Science& Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350373783

The abstract of this research paper outlines the overarching objectives, methodologies, findings, and implications of the study, focusing on the development of a robust machine learning model for precise classification of various ovarian cancer subtypes. The primary goal was to leverage the VGG19 architecture, a deep convolutional neural network, to accurately identify subtypes including endometrioid carcinoma (EC), clear cell carcinoma (CC), mucinous carcinoma (MC), high-grade serous carcinoma (HGSC), and low-grade serous carcinoma (LGSC). Employing advanced image processing algorithms and feature extraction techniques, the model was trained on a dataset comprising histology images of ovarian tissue samples. The achieved overall accuracy of 75% signifies the effectiveness of the proposed strategy in subtype identification. A comprehensive evaluation of the model's performance, including accuracy, recall, and F1-score, was conducted to assess its classification skills across diverse subtypes. The study not only presents the model's achievements but also sheds light on potential areas for growth, future research prospects, and an analysis of the model's strengths and weaknesses. The research contributes significantly to the field by demonstrating the ability to differentiate between ovarian cancer subgroups, offering valuable insights that could enhance the precision of diagnosis and, consequently, the formulation of tailored treatment regimens. The abstract concludes by emphasizing the increasing trend in the utilization of computational approaches, particularly machine learning, for enhanced accuracy in ovarian cancer diagnosis and treatment. Overall, the abstract serves as a concise and informative preview of the research, inviting readers to delve deeper into the study's details. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Drug Target Genes Based on PPI and PDI Networks 23

Predicting Drug Target Genes Based on PPI and PDI Networks

引用

7th international conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Zhou, Tie Hua Jin, Tian Yu Wang, Ling Xu, Peng Cheng School of Computer Science Northeast Electric Power University Chuanying District Jilin China

ISBN: (纸本)9781450399883

Bioinformatics applies computer science principles to understand biological processes and derive knowledge from biological data, including the correlation between genetic variations in common cancers. To develop treatments for specific diseases, researchers must identify the genes associated with them, as well as understand the connection between genes associated with multiple diseases. In this study, we demonstrated the biological association between diseases of interest by constructing a sub-pathway based on inter-gene mapping onto protein-protein interaction (PPI) networks. PPI networks, topological properties, protein-drug interaction (PDI) networks, and Genes Co-expression are all useful tools for drug design. By employing bioinformatics techniques, we identified the interconnected genes associated with the group of diseases and constructed pathways based on the genes' functional relationships to validate the effectiveness of the proposed strategy for developing generic drugs. © 2023 ACM.

关键词： Genes

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Eight-bit Quantization for Super-Resolution Networks and Its Hardware Implementation

Eight-bit Quantization for Super-Resolution Networks and Its...

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2023 international conference on Electronics, Information, and Communication, ICEIC 2023

作者： Nguyen, Xuan Truong Ko, Younghoon Nguyen, Tuan Nghia Lee, Hyuk-Jae Lee, Kyujoong Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Sungshin Women's University School of Ai Convergence Seoul Korea Republic of

ISBN: (纸本)9798350320213

This work presents an 8-bit quantization scheme for super-resolution (SR) networks and its hardware implementation. Quantizing SR models to run in 8-bit is a non-trial task, frequently leading to non-negligible restoration performance degradation. To address the problem, we first present a quantization scheme that mitigates accuracy drop by effectively capturing the distributions of both weights and activations. Second, the proposed scheme is implemented using Verilog HDL and the design is synthesized on Synopsys Design Compiler using TSMC 65 nm library. Lastly, experimental results illustrate the effectiveness and efficiency of the proposed method. © 2023 IEEE.

关键词： 8-bit quantization SR accelerator SR networks

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Component-based development Method for Intelligent Robot Systems 23

Component-based Development Method for Intelligent Robot Sys...

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7th international conference on computer Science and Application Engineering, CSAE 2023

作者： Zhang, Jiachuang Cai, Mingxin Cheng, Lianglun Wang, Tao Wu, Heng School of Automation Guangdong University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400700590

With the advent of the intelligent manufacturing era, the demand for industrial robots has increased, and the scenarios and functional requirements of industrial robots have become increasingly complex. Developers need to design robot control systems based on specific functions, environments, and tasks, which often suffer from poor flexibility, high development costs, and long development cycles. To address the issues of low system development efficiency, poor reusability, and limited scalability in traditional code-based robot control system development methods, this paper proposes a component-based development method for robot control systems. By adopting a component-based modeling and integration approach, the robot control system is built based on component models, providing a fast, efficient, and scalable solution for robot control system development. © 2023 ACM.

关键词： Industrial robots

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Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection 31

Ambiguity-aware Multi-level Incongruity Fusion Network for M...

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31st international conference on Computational Linguistics, COLING 2025

作者： Li, Kuntao Chen, Yifan Wu, Qiaofeng Mai, Weixing Li, Fenghuan Xue, Yun South China Normal University China School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9798891761964

Multi-modal sarcasm detection aims to identify whether a given image-text pair is sarcastic. The pivotal factor of the task lies in accurately capturing incongruities from different modalities. Although existing studies have achieved impressive success, they primarily committed to fusing the textual and visual information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information at the text-level and image-level. Furthermore, the utilized fusion strategies of cross-modal information neglected the effect of inherent ambiguity within text and image modalities on multimodal fusion. To overcome these limitations, we propose a novel Ambiguity-aware Multi-level Incongruity Fusion Network (AMIF) for multi-modal sarcasm detection. Our method involves a multi-level incongruity learning module to capture the incongruity information simultaneously at the text-level, image-level and cross-modal-level. Additionally, an ambiguity-based fusion module is developed to dynamically learn reasonable weights and interpretably aggregate incongruity features from different levels. Comprehensive experiments conducted on a publicly available dataset demonstrate the superiority of our proposed model over state-of-the-art methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Blockchain-Powered Solution to Safeguard IoT Devices Against Attacks

Blockchain-Powered Solution to Safeguard IoT Devices Against...

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2023 international conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Maurya, Vinay Rishiwal, Vinay Yadav, Mano Jat, Dharm Singh Agarwal, Udit Verma, Vandna Rani Department of Csit Mjp Rohilkhand University Bareilly India Bareilly College Bareilly Department of Computer Science Bareilly India Namibia University of Science and Technology Department of Computer Science Namibia Galgotias College of Engg. and Tech. Department of Cse Greater Noida India

ISBN: (纸本)9798350314878

In recent years, the development of the Internet of Things (IoT) has gained motion, surpassing previous growth rates. However, safeguarding IoT devices remains a daunting challenge. Blockchain is offering the potential solutions to this quandary. This paper presents a blockchain-powered approach to detect and prevent cyber-attacks targeting IoT devices. The proposed approach aims to establish a secure and user-friendly environment by coupling the immutable and decentralized nature of blockchain technology. This paper begins by providing an overview of IoT and Blockchain (BC) technology, followed by an exploration of the diverse challenges and attacks encountered at different layers of IoT systems. Additionally, related work in the field is reviewed. Moreover, the paper excavate into the proposed framework, which involves employing the Remix IDE to create Smart Contracts for IoT device registration. Algorithms for IoT device registration and IoT Network Monitoring are presented and discussed. Implementations of validation and verification techniques is done to identify and baffle cyber-attacks. The results demonstrate that the proposed solution successfully identifies and detects attacks on any IoT device. © 2023 IEEE.

关键词： Smart contract

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Research Progress of Aquaculture Environmental Factor Prediction Model Based on Machine Learning 9

Research Progress of Aquaculture Environmental Factor Predic...

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9th international conference on Control Science and Systems Engineering, ICCSSE 2023

作者： Qin, Haijing Tian, Yunchen Wang, Hongpo Li, Qingfei Cong, Xueqi Sui, Jinzhu College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

ISBN: (纸本)9798350339055

The accurate aquaculture environmental factors prediction model is established using big data technology and machine learning algorithm. It has become a research hotspot because it can accurately predict water quality, strengthen water quality management and reduce aquaculture risks. This paper introduces the characteristics and innovations of aquaculture water quality prediction models based on shallow machine learning and deep learning. It expounds on the primary process and standard methods of aquaculture environment factor prediction models based on machine learning. Analyzing the model performance index, the integrity of data collection, scientific processing, and the rationality of the model-building method is the basis for establishing a high-quality model. At the same time, it also points out the existing problems of empirical determination model structure and the need to accelerate the collaborative development of water environment monitoring technology and water environment prediction technology in the future to establish an intelligent water environment regulation system and promote the development of intelligent aquaculture. © 2023 IEEE.

关键词： Water quality

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