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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Zhu, Boqi Lu, Kezhong Tao, Tao School of Computer Science and Technology Anhui University of Technology Maanshan China School of Big Data and Artificial Chizhou University Chizhou China

ISBN: (纸本)9798350337181

Smart home devices generate a substantial amount of local data, and finding effective ways to utilize this data while ensuring privacy has become an increasingly pressing concern. Technologies such as Smart Homes, Federated Learning and Blockchain offer promising solutions to address this challenge. We introduce a blockchain-based federated learning approach that leverages edge nodes to maintain a decentralized blockchain, thus mitigating the risks associated with single points of failure. Furthermore, this method utilizes local data from home IoT devices for model training, ensuring efficient learning while preserving data privacy. To address the challenges posed by non-independent and homogeneous data distribution, we propose a clustering method. This strategy effectively tackles the issues arising from non-homogeneous data distribution, consequently improving model accuracy. Finally, experimental results demonstrate that our proposed approach significantly enhances model accuracy and generalization while safeguarding user privacy. © 2023 IEEE.

关键词： Blockchain

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A Survey on Energy Saving Technology of Wireless Sensor Network 4th

A Survey on Energy Saving Technology of Wireless Sensor Netw...

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4th International Conference on Wireless Communications and Applications, ICWCA 2020

作者： Zhang, Huanan School of Data and Computer Science Guangdong Peizheng College Guangzhou China

ISBN: (纸本)9789811651670

Wireless sensor networks (WSNs) have important applications in many fields such as medical treatment and industry. A WSN is typically consists of a large number of sensor nodes that rely on a limited supply of power in many applications. Therefore, improving the energy efficiency of wireless sensor networks has become an important research topic. This paper analyzes the principle of energy consumption of each component unit of wireless sensor node, and studies the energy saving technology and energy saving design idea of MAC protocol from the data link layer and network layer of network model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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An Improved Algorithm of Detecting Abnormal data in Geological Disaster System 6

An Improved Algorithm of Detecting Abnormal Data in Geologic...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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StudentCoin Price Prediction and Relation to Blockchain Education 10

StudentCoin Price Prediction and Relation to Blockchain Educ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Rashmeet Sonia Iorio, Carmela University of Naples Federico Ii Department of Economics Science and Statistics Naples Italy Yogananda School of AI Computer and Data Science Shoolini University Solan India

ISBN: (纸本)9789380544472

The newly introduced cryptocurrency called StudentCoin which is identified by the acronym STC is a blockchain-based project. It is the first platform in the world for easy tokenization. It allows the users to learn about cryptocurrencies, blockchain technology and their use cases. Users can create their own tokens, coins and also trade them on the same terminal. This paper uses easy-to-understand modeling and simulation techniques to make price predictions for blockchain education-oriented cryptocurrencies like StudentCoin. We have used the Monte Carlo simulation technique to determine if the price of StudentCoin is expected to increase within a month, and that combining education with blockchain and cryptocurrency platforms is beneficial. The study also aims to find out if college students are aware of cryptocurrencies and see if it's possible to combine education and cryptocurrency. The paper contributes to promoting awareness among university students about responsible investment practices in the cryptocurrency market. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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Adaptive weighted broad learning system for imbalanced classification 6

Adaptive weighted broad learning system for imbalanced class...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Yanting Gao, Yiping Jin, Junwei Chen, C. L. Philip Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350316520

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods. © 2024 IEEE.

关键词： Classification (of information)

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Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars 38

Prompt Optimization with EASE? Efficient Ordering-aware Auto...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Zhaoxuan Lin, Xiaoqiang Dai, Zhongxiang Hu, Wenyang Shu, Yao Ng, See-Kiong Jaillet, Patrick Low, Bryan Kian Hsiang Institute of Data Science National University of Singapore Singapore Singapore-MIT Alliance for Research and Technology Singapore Dept. of Computer Science National University of Singapore Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China LIDS EECS Massachusetts Institute of Technology United States China

Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream tasks by including input-label exemplars in the prompt without model fine-tuning. However, the quality of these exemplars in the prompt greatly impacts performance, highlighting the need for an effective automated exemplar selection method. Recent studies have explored retrieval-based approaches to select exemplars tailored to individual test queries, which can be undesirable due to extra test-time computation and an increased risk of data exposure. Moreover, existing methods fail to adequately account for the impact of exemplar ordering on the performance. On the other hand, the impact of the instruction, another essential component in the prompt given to the LLM, is often overlooked in existing exemplar selection methods. To address these challenges, we propose a novel method named EASE, which leverages the hidden embedding from a pretrained language model to represent ordered sets of exemplars and uses a neural bandit algorithm to optimize the sets of exemplars while accounting for exemplar ordering. Our EASE can efficiently find an ordered set of exemplars that performs well for all test queries from a given task, thereby eliminating test-time computation. Importantly, EASE can be readily extended to jointly optimize both the exemplars and the instruction. Through extensive empirical evaluations (including novel tasks), we demonstrate the superiority of EASE over existing methods, and reveal practical insights about the impact of exemplar selection on ICL performance, which may be of independent interest. Our code is available at https://***/ZhaoxuanWu/EASE-Prompt-Optimization. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving the Accuracy of Iris Recognition Based on Attention Mechanism 6

Improving the Accuracy of Iris Recognition Based on Attentio...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Wang, Zike Wang, Jianguo Zhong, Lei Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

Iris recognition algorithms are becoming more and more widely used in daily life, and the accuracy requirements for iris recognition algorithms are getting higher and higher. Therefore, in this paper, an improved Shufflenetv2 algorithm is proposed as a way to improve the speed and accuracy of current iris recognition algorithms in daily life. In this paper, deep and shallow features are filtered by using the channel attention mechanism, which enables the model to acquire feature representations of both underlying and high-level semantics. In addition, in the loss function, this paper uses the focal loss function instead of the cross entropy loss function to achieve a solution to the imbalance between the number of positive and negative samples and prevent too much learning to negative sample features. By validating on the CASIA-Iris-Thousand dataset, which is commonly used in iris recognition tasks, the proposed model achieves an equal error rate (EER) of 1.66% compared to the Shufflenetv2 model, which has an EER of 5.3%, and optimizes by 3.64%. The model proposed in this paper can effectively improve the accuracy of iris recognition. © 2023 IEEE.

关键词： Convolutional neural networks

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CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

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Journal of computer science & Technology 2023年第5期38卷 1113-1131页

作者：胡世红罗渠元李光辉施巍松叶保留 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Houhai UniversityNanjing 210098China School of Computer and Information Houhai UniversityNanjing 210098China School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuci 214122China Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing *** continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this ***,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized *** this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov *** that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of *** reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple *** evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed *** with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

关键词： edge computing deep reinforcement learning task scheduling vehicular edge computing

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

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