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2012 International Conference on computer science and Service System, CSSS 2012

作者： Liu, Shuxian Li, Xiaohua Information Science and Engineering Institution Xinjiang University Urumqi Xinjiang China Computer Science and Technology Institution Xinjiang Normal University Urumqi Xinjiang China

ISBN: (纸本)9780769547190

Xinjiang Uygur's flower hat is famous all over the world, it leads the world of headwear of variou nationalities within China by the variety of styles, the various ways of how to emroider pattern design, and exquisite manufacture. It is unusual even in national costumes art of the world. The poupose of this paper is to analyze Uygur's headwear, study the rules of Uygur's headwear pattern, analyze the characteristic of the basic pattern, research on the key technologies for computer-aided pattern design of Uygur's headwear, explore the application of compuer-aided design in traditional costume's pattern design, and to realize application exploration of compuer-aided design in designing of costume and dress adornment. © 2012 IEEE.

关键词： computer aided design

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Exploring the Potential of IoT, Machine Learning, and Cloud Computing in Smart Home Applications

Exploring the Potential of IoT, Machine Learning, and Cloud ...

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2024 International Conference on Advances in Computing Research on science Engineering and technology, ACROSET 2024

作者： Mathur, Garima Savita, Pankaj Gaur, Neeraj Vishwakarma, Bhavana Tripathi, Vaibhav Sagar Institution of Science and Technology Computer Science & Engineering Bhopal India Itm University Computer Science & Engineering Gwalior India Oriental Institute of Science & Technology CSE-AIML Bhopal India

ISBN: (纸本)9798350388800

This study investigates using machine learning (ML), the Internet of Things (IoT), and cloud computing to predict cardiovascular diseases. Integrating these advanced technologies in healthcare enables the collection, analysis, and real-time response to patient data, enhancing early diagnosis and personalised treatment plans. This paper presents various case studies demonstrating the successful application of ML algorithms, IoT devices, and cloud platforms in cardiovascular disease prediction. The findings highlight significant improvements in prediction accuracy, patient monitoring, and overall healthcare efficiency. Additionally, the study addresses the challenges, opportunities, and future research directions in the field, aiming to provide a comprehensive understanding of the potential of these technologies in transforming cardiovascular healthcare. © 2024 IEEE.

关键词： Cloud Computing Intelligent Homes IoT Machine Learning

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A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

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International Journal of Automation and computing 2021年第1期18卷 18-44页

作者： Wei Jia Jian Gao Wei Xia Yang Zhao Hai Min Jing-Ting Lu School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230009China Institution of Industry and Equipment Technology Hefei University of TechnologyHefei 230009China

Palmprint recognition and palm vein recognition are two emerging biometrics *** the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have achieved impressive ***,the research on deep learningbased palmprint recognition and palm vein recognition is still very *** this paper,in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition indepth,we conduct performance evaluation of seventeen representative and classic convolutional neural networks(CNNs)on one 3D palmprint database,five 2D palmprint databases and two palm vein databases.A lot of experiments have been carried out in the conditions of different network structures,different learning rates,and different numbers of network *** have also conducted experiments on both separate data mode and mixed data *** results show that these classic CNNs can achieve promising recognition results,and the recognition performance of recently proposed CNNs is ***,among classic CNNs,one of the recently proposed classic CNNs,i.e.,EfficientNet achieves the best recognition ***,the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

关键词： Performance evaluation convolutional neural network(CNN) biometrics palmprint palm vein deep learning

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A Scheme for Robust Federated Learning with Privacy-preserving Based on Krum AGR

A Scheme for Robust Federated Learning with Privacy-preservi...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Li, Xiumin Wen, Mi He, Siying Lu, Rongxing Wang, Liangliang Shanghai University of Electric Power Institution of Computer Science and Technology Shanghai China University of New Brunswick Faculty of Computer Science NB Canada

ISBN: (纸本)9798350345384

The sensitive information of participants would be leaked to an untrustworthy server through gradients in federated learning. Encrypted aggregation of uploaded parameters could resolve this issue. However, it brings challenges to the defense of model poisoning attacks in federated learning while solving the privacy problem. To address this issue, a robust federated learning scheme with privacy-preserving (RFLP) is proposed to eliminate the impact of model poisoning attacks while protecting the privacy of participants against untrusted servers. Specifically, an abnormal gradients detecting method is designed to achieve robust federated learning under encrypted aggregation using Pailliar homomorphic encryption. It is based on the concept of Krum aggregation algorithm (AGR), but utilizes privacy-preserving data features, thereby ensuring privacy. To reduce the rounds of communication in robust aggregation, a multidimensional homomorphic encryption approach is constructed. Besides, an aggregated signature authentication method is also constructed to ensure data integrity during transmission. The experiment results show that the training accuracy of RFLP with 10% malicious participants is 11.9% and 15.3% higher than that without robust aggregation. © 2023 IEEE.

关键词： Authentication

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Cost as performance: VNF placement at the edge

IEEE Networking Letters

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IEEE Networking Letters 2021年第2期3卷 70-74页

作者： Tao, Xiaoyi Ota, Kaoru Dong, Mianxiong Qi, Heng Li, Keqiu School of Information Science and Technology Dalian Maritime University Dalian116023 China Department of Information and Electronic Engineering Muroran Institution of Technology Muroran050-8585 Japan School of Computer Science and Technology Dalian University of Technology Dalian116023 China

This letter studies the virtualized network function (VNF) placement problem for edge systems, where the edge nodes can provide services that are originally deployed in cloud datacenters. As related to the end-users, edge supports low latency when the energy consumption of edge nodes and end users are the main concerns. Therefore, we formulate this problem with two performance metrics: latency and energy consumption. We present a joint VNF Placement optimization problem at the Edge, and propose the method of VPE. The numerical results show that our proposed VPE guarantees the energy consumption cost without damaging the performance on latency. © 2019 IEEE.

关键词： Energy utilization

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Diabetic retinopathy detection using a well-calibrated uncertainty aware convolutional neural network

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Multimedia Tools and Applications 2024年 1-23页

作者： Verma, Preeti Elango, Sivasankar Singh, Kunwar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India KIET Group of Institution Uttar Pradesh Delhi-NCR Ghaziabad201206 India

The early detection of diabetic retinopathy is crucial in preventing irreversible vision loss, making it a critical concern in healthcare. While deep learning models have shown advancements in categorization tasks, they can sometimes exhibit overconfidence in incorrect outcomes, posing significant risks in the medical field. To tackle this challenge, a novel model is proposed, which leverages a CNN (Convolutional neural network) architecture to predict softmax probabilities. The model incorporates train time augmentation to enhance performance and subsequently calibrates the projected probabilities using multiple calibration techniques, including Platt scaling, temperature scaling, isotonic regression, histogram binning, and Platt histogram. Furthermore, Test Time Data Augmentation (TTAUG) is employed to improve prediction accuracy by presenting the same data in multiple variations. The proposed technique is rigorously evaluated on benchmark datasets, namely Kaggle and Messidor. Notably, TTAUG yields remarkable results, achieving 98% accuracy on the Kaggle dataset, a significant improvement compared to the 93% achieved without augmentation. The calibration of probabilities further enhances the performance, as indicated by the reduction of Expected Calibration Error (ECE) from 0.52 to an impressive 0.019. The results underscore the significance of quantifying uncertainty in predictions, which enhances the interpretability of the proposed method as a diagnostic aid tool. By effectively addressing overconfidence and providing calibrated probabilities, the model proves its potential in improving both accuracy and reliability in diabetic retinopathy detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Calibration

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Soliton excitation in the pass band of the transmission line based on modulation

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Chinese Physics B 2016年第4期25卷 115-120页

作者：赵帼英陶锋陈伟中 School of Computer Science and Technology Anhui University of Technology Ma' anshan 243002 China School of Electrical and Information Engineering Anhui University of Technology Ma' anshan 243002 China Key Laboratory of Modern Acoustics Ministry of Education and Institution of Acoustics Nanjing University Nanjing 210093 China

We numerically investigate the excitation of soliton waves in the nonlinear electrical transmission line formed by many cells. When the periodic driving voltage with frequency in the pass band closing to the cutoff frequency is applied to the endpoint of the whole line, the soliton wave can be generated. The numerical results show that the soliton wave generation mainly depends on the self modulation associated with the nonlinear effect. In this study, the lower subharmonic component is also observed in the frequency spectrum. To further understand this phenomenon, we study the dependence of the subharmonic power spectrum and frequency on the forcing amplitude and frequency numerically, and find that the subharmonic frequency increases with the gradual growth of the driving amplitude.

关键词： soliton nonlinear transmission line modulation

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Implementation of 3D sound effect modeling technology based on vehicle driving simulator

Implementation of 3D sound effect modeling technology based ...

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9th International Conference on computer-Aided Industrial Design and Conceptual Design: Multicultural Creation and Design -CAIDCD 2008

作者： Xun-Xiang, Li Duo, Zhan Dongyan, Cheng Dingfang, Chen Modern Industrial Design Institution College of Computer Science Zhejiang University Hangzhou 310027 China College of Computer Science and Technology Wuhan University of Technology Wuhan 430063 China

ISBN: (纸本)9781424432912

Sound simulation system of virtual driving environment of auditory perception module, can make up for the lack of a single visual information to enhance the driver of the interactive virtual driving environment and the judgement of the spot realistic driving simulator, in order to better complete the training and driving training mission. This article discusses the virtual 3D positioning in the voice of principle, sound attenuation distance model, the Doppler effect and sound formula, and other key technologies on the two voices in the virtual space development kits Open AL, DirectSound;under simulated driving For the practical application, given some applications and examples of relevant research which should be focus on in the field of staff have a certain value. © 2008 IEEE.

关键词： Three dimensional computer graphics

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Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System

Dynamic Causality Knowledge Graph Generation for Supporting ...

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Future Technologies Conference, FTC 2020

作者： Yu, Hong Qing Institution for Research in Applicable Computing School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9783030630911

With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions. © 2021, Springer Nature Switzerland AG.

关键词： Viruses

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AcKnowledge: Acquired Knowledge Representation by Small Language Model Without Pre-training 1

AcKnowledge: Acquired Knowledge Representation by Small Lang...

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1st Workshop on Towards Knowledgeable Language Models, KnowLLM 2024 - Co-located with ACL 2024

作者： Das, Sourav Chatterji, Sanjay Mukherjee, Imon Department of Computer Science and Engineering Indian Institution of Information Technology Kalyani West Bengal Kalyani India

ISBN: (纸本)9798891761544

Large language models (LLMs) are pre-trained on enormous amounts of text data and show acclaimed success in knowledge representation. However, there are two bottlenecks with this approach. (1) Pre-training data cannot be regularly updated once the models are deployed, and it is not very fruitful if the model cannot represent updated knowledge. (2) The consistently increasing size and computational resources make it difficult for noncommercial and individual researchers to fine-tune and scale these language models. Major LLMs with external knowledge are also proprietary. In this paper, we propose AcKnowledge, a framework wrapped around a small, non-pre-trained language model for an open-domain question-answering (QA) experiment. AcKnowledge learns relevant knowledge from the internet via meta-learning based on user questions, and re-learns from user feedback if knowledge is misrepresented. Our efficient knowledge representation framework avoids pre-training overhead while enabling updated information. Benchmarking shows competitive performance against similarly sized state-of-the-art (SoTA) LLMs on gold standard QA datasets, demonstrating the potential of integrating internet search and user feedback for improved performance and general-izability. The repository of the work is available at https://***/SouravD-Me/AcKnowledge-KnowledgeLM-ACL-2024. © 2024 Association for Computational Linguistics.

关键词： Knowledge representation

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