检索结果-内蒙古大学图书馆

Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Raghav Gupta Manasa Gullapalli G. Suseela Department of Networking and Communications School of Computing Science and Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Networking and Communications School of Computing Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

A key component of cloud computing is cloud storage, which allows data to be stored on remote servers. The result is a reduction in the amount of storage space required on local devices, allowing data to be accessed from anywhere with an internet connection. Furthermore, cloud storage is more secure than local storage due to the use of encryption and other security measures by professional providers. This is a practical and cost-effective solution to store large images required to be safeguarded from malicious black hat hackers. Images are the most valuable yet vulnerable form of data that can be used to track down user activities and taken advantage by hackers, used to commit identity theft, fraud. Issues concerning the security and privacy of images saved in the cloud have arisen as cloud storage has grown in popularity. The proposed method will focus on the encryption of images using a chaotic encryption scheme based on Pascal’s triangle. The user will authenticate their identity before uploading images to cloud storage and likewise confirm their authentication in the process of retrieval of an image. Such a secure system is better than traditional security methods for password settings as it cannot be easily stolen. Additionally, this method is more secure than other methods due to its complex encryption mechanisms, making it impossible to guess or steal the encryption key. In addition to providing extra security for sensitive images, biometric authentication will offer a stronger method of verifying and validating users on the cloud.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LOPE: A Low-Overhead Payment Verification Method for Blockchains

引用

Chinese Journal of Electronics 2021年第2期30卷 349-358页

作者： ZHANG Ying HE Zhengyuan JIA Linpeng DING Donghui ZHANG Hanwen SUN Yi School of Software Engineering Tongji University Blockchain Lab Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

With its characteristics of decentralization,security, data traceability, and tamper-resistance, the blockchain has been widely used in various *** the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However,the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or Io T chips, and therefore the current light client scheme does not work in this situation. We propose LOPE(a Low-overhead payment v Erification method) for poor-capacity nodes in the blockchain *** LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance(PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.

关键词： current light client scheme storage overhead blockchains Low-overhead payment vErification method peer-to-peer computing software fault tolerance electronic money data integrity LOPE low-performance devices cryptographic protocols fault tolerance blockchain transactions

来源：评论

学校读者我要写书评

暂无评论

Directed Brain Network Transformer for Psychiatric Diagnosis 27th

Directed Brain Network Transformer for Psychiatric Diagnosis

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Xu Qi, Zhiwei Yue, Kun Su, Yunshan Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Education Yunnan University Kunming China Department of Radiology Affiliated Hospital of Yunnan University Kunming China

ISBN: (纸本)9783031781940

Human brain is a complex organ that consists of billions of neurons and trillions of connections among the neurons. To describe the correlations among the time series of the brain regions, we model the brain as a functional brain network to diagnose psychosis. However, traditional methods of functional brain network construction are usually noisy and do not consider the causal relationships among brain regions. To obtain the causal relationships and improve diagnosis interpretability, we propose a directed brain network Transformer (DBNT) for psychiatric diagnosis. First, the causal relationships in the blood-oxygen-level-dependent time series of brain regions are extracted to generate a directed brain network. Then, the feature encoding method is proposed to obtain local and global features of the brain networks by using the DBNT. Experimental results demonstrate that the accuracy of DBNT increases by 8.1% and 6.4% compared to state-of-the-art methods on two large-scale brain network datasets. DBNT also highlights the brain regions associated with psychosis and provides interpretation for diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Classification for Stress Release Management using Machine Learning 1

Classification for Stress Release Management using Machine L...

引用

1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Hasan, Yazdani Gupta, Anuj Upadhyay, Diwakar Pandey, Mahima Shankar Malik, Suraj Gochhait, Saikat Noida International University Department of Computer Science India Sharda University School of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Graphic Era Hill University School of Computing Department Haldwani India Galgotia College of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India IIMT University Department of Computer Science & Engineering Uttar Pradesh Meerut India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350356816

Manipulating the words in some useful ways makes the mind refreshed and worth seeking. Instead of the website, the music software is not able to grab the attention of the user in most of the moody cases. Also, the companies which are running this facility are not able to provide the demanded services. As per the opportunity, the providers are also working hard for more user satisfaction. By adding classification to the existing technology, the providers are designing attractive platforms for the users. The common challenge here is to automate the system. There should be automatic identification system instead of manually operated. Machine learning and data mining techniques are best for classification and deployment of any complex category. This is the only advantage of machine learning, as it drives the optimized method as well as the concept of basic probability. To develop the automated system, the mood classification should be identified from the user's point of view according to their own preferences. Hindi and Bollywood songs are taking heavy traffic in the online channel of music. Also, these are the best for calming the mind. When calculating the response through ratio display, it seems like 70% to 75% accuracy with calls. This paper is also showing the percentage and useful features by implementing the system with machine learning method i.e. Random Forest method. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

EPIMR:Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

引用

Big Data Mining and Analytics 2024年第3期7卷 668-681页

作者： Qiaozhen Meng Yinuo Lyu Xiaoqing Peng Junhai Xu Jijun Tang Fei Guo School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Aeronautical Information Service Center of the Civil Aviation Administration of China(AISC.ATMB.CAAC) Beijing 100015China Center for Medical Genetics&Hunan Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410038China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518000China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic *** to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology *** EPI predictors have been developed,but their prediction accuracy still needs to be ***,we design a new model named EPIMR to identify enhancer-promoter ***,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial ***,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction ***,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction *** results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling ***,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction *** conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription *** code and results are available at https://***/guofei-tju/EPIMR.

关键词： enhancer-promoter interactions Hilbert Curve multi-scale residual neural network(ResNet)

来源：评论

学校读者我要写书评

暂无评论

Smart Solar E-Tricycle Design and Fabrication for Especially Abled Persons 5th

Smart Solar E-Tricycle Design and Fabrication for Especially...

引用

5th International Conference on Advances in Distributed computing and Machine Learning, ICADCML 2024

作者： Rathee, Davinder Singh Rathee, Kanta Singh, Ram Sewak Gemeda, Ketema Adere Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Po. Box: 1888 Adama Ethiopia Rohtak India Department of CSE and Center for Electrical System and Electronics Adama Science and Technology University Po. Box: 1888 Adama Ethiopia

ISBN: (纸本)9789819718405

Addressing the pressing issue of transportation for individuals with disabilities, this article aims to develop a customized three-wheeled vehicle equipped with an electric motor and pedals, to cater to the mobility requirements of people with disabilities. The proposal focuses on designing, manufacturing, and deploying a solar-powered E-Tricycle using readily available components from the Adama market, enhancing both sustainability and accessibility. To bolster security and track tricycles, a GPS-GSM system has been also integrated in this design. This innovative approach offers an affordable prototype featuring a solar panel on the roof, providing shade and effortless charging. The project encompasses the creation of a sustainable, safe, and robust structure, assembly of mechanical components on the frame, and integration of electrical circuits, including indicators, on/off switches, lights, motors, batteries, battery management systems (BMS), solar panels, and their controllers. Furthermore, a mathematical model has been developed to calculate the power required for E-Tricycle movement, accounting for the rider and their possessions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar panels

来源：评论

学校读者我要写书评

暂无评论

Enhanced Investment Decision Making with a Reinforcement Learning-Based Multi-Agent Portfolio Management System 2

Enhanced Investment Decision Making with a Reinforcement Lea...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Ram, Kottooru Saaketh Raja Selvaganapathy, M. Nandhini, I. Tamilselvi, M. Kalaivani, M. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India C. K. College of Engineering and Technology Department of Electroncis and Communication Engineering Cuddalore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electron Cis and Communication Engineering Chennai Avadi India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Department of Electron Cis and Communication Engineering Trichy India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

ISBN: (纸本)9798350373110

The portfolio management system using traditional machine learning approaches struggles with data quality and interpretability of models, while needing to adapt to constantly evolving market dynamics. The Reinforcement Learning (RL) algorithms have proven to be a promising approach in handling diverse datasets with unpredictable performance. The proposed approach utilizes multiple RL alogrithms such as Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) to improve investment decision-making, historical data from the Yahoo Finance Dataset. This system incorporates three well-established reinforcement learning algorithms to dynamically adjust portfolio allocations. The A2C algorithm combines actor-critic techniques to optimize long-term portfolio performance, while the PPO algorithm maintains a balance between exploration and exploitation, enhancing returns stability. The proposed DDPG algorithm utilizes a deep neural network to determine the optimal action-value function, enabling informed investment decisions. Tested across various assets in the Yahoo Finance Dataset, it consistently outperforms traditional portfolio management methods. This flexible system can be tailored to suit different investment objectives and risk tolerances. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning Reimagined: Exploring Universality 24

Graph Contrastive Learning Reimagined: Exploring Universalit...

引用

33rd ACM Web Conference, WWW 2024

作者： Zhuo, Jiaming Cui, Can Fu, Kun Niu, Bingxin He, Dongxiao Wang, Chuan Guo, Yuanfang Wang, Zhen Cao, Xiaochun Yang, Liang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus Sun Yat-Sen University Shenzhen China

ISBN: (纸本)9798400701719

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks. © 2024 ACM.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning Consistent and Identifiable Latent Embeddings for EEG 19th

Contrastive Learning Consistent and Identifiable Latent Emb...

引用

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liang, Feng Zhang, Zhen Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Extracting informative EEG data into low-dimension latent embeddings is important for storing and analyzing these neuron signals and applying them to various applications, such as modern human-computer interaction (HCI) techniques. We use the contrastive learning algorithm on time-domain features of EEG in both discovery-driven (self-supervised) and hypothesis (supervised) manners to encode the EEG data into latent embeddings that are proven consistent and identifiable. The self-supervised embeddings have the potential to be used for a range of downstream tasks, while the supervised embeddings have very high decoding accuracy for specific tasks. With embeddings encoded from EEG features collected within every 0.5-s window, the accuracy of recognizing the identities of persons by decoding the self-supervised and supervised embeddings is as high as 96.2% and 99.6%, respectively. Our method and results can promote new HCI techniques, e.g., automatically connecting users to their roles in AR games once they wear EEG-capable devices. The source code is available at: https://***/liangfengsid/timeEegContrastive. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：