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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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PAC PREDICTION SETS UNDER LABEL SHIFT 12

PAC PREDICTION SETS UNDER LABEL SHIFT

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12th International Conference on Learning Representations, ICLR 2024

作者： Si, Wenwen Park, Sangdon Lee, Insup Dobriban, Edgar Bastani, Osbert Department of Computer & Information Science University of Pennsylvania United States Graduate School of AI and Computer Science & Engineering POSTECH Korea Republic of Department of Statistics and Data Science University of Pennsylvania United States

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting, where the probabilities of labels can differ between the source and target distributions. Our algorithm relies on constructing confidence intervals for importance weights by propagating uncertainty through a Gaussian elimination algorithm. We evaluate our approach on four datasets: the CIFAR-10 and ChestX-Ray image datasets, the tabular CDC Heart dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller prediction set sizes compared to several baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Forecasting

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation 40

SSDRec: Self-Augmented Sequence Denoising for Sequential Rec...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Chi Han, Qilong Chen, Rui Zhao, Xiangyu Tang, Peng Song, Hongtao College of Computer Science and Technology Harbin Engineering University China School of Data Science City University of Hong Kong Hong Kong School of Cyber Science and Technology Shandong University China

ISBN: (纸本)9798350317152

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework-Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of SSDRec over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available online at https://***/zc-97/SSDRec. © 2024 IEEE.

关键词： User profile

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Smart Agriculture System 4th

Smart Agriculture System

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Kanaujia, Vishal Kumar Sah, Chitranjan Prasad Computer Science and Engineering-Data Science Department ABES Engineering College Uttar Pradesh Ghaziabad India School of Engineering and Technology Noida International University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819777938

Agriculture stands as a linchpin for national prosperity, especially in regions blessed with abundant water and fertile soil, capable of yielding a diverse range of food products crucial for human sustenance. However, escalating global population and diminishing freshwater reserves pose significant challenges to sustainable agriculture. Approximately, 96% of Earth's water is saline, leaving a mere 3% as freshwater resources, intensifying the need for advanced techniques to conserve water in farming practices. This research addresses the pressing issue by proposing a solution rooted in smart agriculture, leveraging modern technologies like the Internet of Things (IoT) to revolutionize conventional farming methods. The integration of automation and IoT technologies, such as the ESP8266 IoT automatic irrigation system, is presented as a means to enhance crop productivity. This system utilizes cutting-edge technologies like Arduino and wireless sensor networks, offering a comprehensive solution for monitoring environmental conditions crucial for maximizing crop yield. The paper underscores the paramount importance of connecting agriculture to the Internet, emphasizing the role of IoT in facilitating applications like crop monitoring and automated irrigation decisions. The proposed smart agriculture system, featuring energy autonomy and cost-effectiveness, aims to address persistent agricultural challenges that impede national development. The research introduces a novel approach to agriculture, offering a tangible pathway toward sustainable farming practices in the face of water scarcity and environmental uncertainties. Through the integration of sensors and live data feeds, accessible via mobile devices, this system represents a promising step toward a technologically advanced and efficient agricultural landscape, especially beneficial for water-scarce and geographically isolated regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Smart agriculture

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SMAKAP: Secure Mutual Authentication and Key Agreement Protocol for RFID Systems 17

SMAKAP: Secure Mutual Authentication and Key Agreement Proto...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Naziri, Shayesta Wang, Xu Yu, Guangsheng Xu, Jian Shrestha, Sudhir Liang, Christy Jie School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia School of Computer Science University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9798331509736

Radio Frequency Identification (RFID) is a crucial technology in the Internet of Things (IoT), enabling seamless wireless communication and data exchange. However, these technologies can pose significant security chal-lenges if not implemented with proper attention to security protocols-especially in communication, where pre-shared keys are not used between active tags and readers for device authentication. Some recent authentication protocols rely solely on a hash function, nonce, and single public kay agreement, which can lead to failure to implement robust security and proper authentication or ineffective for high security application environments. To effectively address these challenges this paper proposes a secure Elliptic Curve Cryptography (ECC) based lightweight mutual authentication protocol utilizing a hybrid key agreement protocol between active tag and reader for secure communication in RFID-enabled devices in the IoT environments. The informal analysis demonstrates a secure communication environment for data privacy and flexibility through effective key management. This protocol is adaptable to various applications by addressing specific requirements and limitations. © 2024 IEEE.

关键词： Authentication Protocol

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Analysis of Bitcoin Prediction using Transparent and Opaque Machine Learning Algorithms 15

Analysis of Bitcoin Prediction using Transparent and Opaque ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Singh, Kavita R. Jamar, Anushri V. Khadgi, Pracheta P. Kapse, Smita Kumar, Ajay Department of Computer Technology Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India School of Big Data and Computer Science Guizhou Province Guiyang City China

ISBN: (纸本)9798331300579

The unpredictable nature of cryptocurrency markets, particularly Bitcoin, has attracted significant attention from investors, researchers, and financial institutions seeking to understand and predict price movements. This study aims to analyse the effectiveness of both transparent and opaque machine learning algorithms in predicting Bitcoin prices. Transparent algorithms, such as linear regression and decision trees, provide clear insights into the factors influencing predictions. On the other hand, opaque algorithms, like ensemble methods, offer complex, non-linear models that may capture intricate patterns in the data. The main analysis is to lower the mean absolute error and enhance the r2 square, both key indicators of forecast accuracy. The study presents that linear regression and random forest outperformed others, achieving the smallest error and highest variance explanation, suggesting their effectiveness in Bitcoin price prediction with and without outliers respectively. In contrast, the support vector machine underperforms compared to all other machine learning algorithms under consideration. This paper sheds light on the most capable machine-learning techniques for Bitcoin forecasting, aiding trader investment choices and guiding academic study in the field of prediction. © Grenze Scientific Society, 2024.

关键词： Prediction models

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