检索结果-内蒙古大学图书馆

2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Tang, Xiuwen Gan, Jiazhen Jiang, Zigui Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy. © 2022 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Differentially private locality sensitive hashing based federated recommender system

Differentially private locality sensitive hashing based fede...

引用

作者： Hu, Hongsheng Dobbie, Gillian Salcic, Zoran Liu, Meng Zhang, Jianbing Lyu, Lingjuan Zhang, Xuyun Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Computer Science The University of Auckland Auckland New Zealand Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand School of Mechanical Electrical and Information Engineering Shandong University Weihai Shandong China Department of Computer Science and Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore Singapore Department of Computing Macquarie University Sydney Australia

Recommender systems are important applications in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy-preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets. © 2021 John Wiley & Sons, Ltd.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

引用

computer Systems science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Approach to Identify Stroke based on Real-time Medical Data 1

A Comprehensive Approach to Identify Stroke based on Real-ti...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Maragatharajan, M. Shanmugapriya, S. Sudhan, M.B. Deeptha, R. Paramasivam, Sankar Muthu Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramTamilnadu Chennai India Srm Institute of Science and Technology Department of Information Technology RamapuramTamilnadu Chennai India Palo Alto Networks Inc Distinguished Software Engineer 3000 Tannery Way Santa ClaraCA United States

ISBN: (纸本)9798350372748

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person's risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology's rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model's performance using evaluation measures including accuracy, precision, and recall. The model's insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs 37

Scalable Edge Blocking Algorithms for Defending Active Direc...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Guo, Mingyu Ward, Max Neumann, Aneta Neumann, Frank Nguyen, Hung School of Computer and Mathematical Sciences University of Adelaide Australia School of Physics Maths and Computing Computer Science and Software Engineering University of Western Australia Australia Department of Molecular and Cellular Biology Harvard University CambridgeMA United States

ISBN: (纸本)9781577358800

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender's aim is to minimize the attacker's success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Toward Privacy-Preserving Spatial Crowdsourcing: From Offline to Online

引用

IEEE Network 2024年第3期39卷 326-332页

作者： Hengzhi Wang Junjie Mai Lei Zhang Linfeng Shen Laizhong Cui Jiangchuan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Science Simon Fraser University Burnaby BC Canada

Recent years have seen Spatial Crowdsourcing (SC) becoming a promising paradigm of collecting large amounts of location-aware data for various network applications. Its strengths lie in leveraging the power of the crowd to gather data effectively at a low cost. However, during the interaction process between SC participants (e.g., workers and task requestors) and the SC platform, participants’ sensitive information is inevitably exposed, raising severe privacy concerns. Existing studies addressing these privacy concerns have mostly focused on offline SC settings, where the participants’ information is known and accessible in advance. Yet real-world SC participants can be highly dynamic with uncertain behaviors; hence, in practice, their information is largely unknown a priori. In this article, we closely examine the privacy challenges as well as the trade-off between privacy and efficiency in online SC settings. We develop a comprehensive online privacy-preserving SC framework and analyze the critical implementation issues.

关键词： Privacy Costs Crowdsourcing Real-time systems Protection Vehicle dynamics Data privacy

来源：评论

学校读者我要写书评

暂无评论

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

引用

Tsinghua science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

来源：评论

学校读者我要写书评

暂无评论

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstration Data for Imitation Learning 13

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstra...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kim, Juhyung Heo, Sungwon Kim, Songwoo Jung, Ho-Jin Yoon, Han Ul Yonsei University Division of Computer and Telecomm. Engineering Mirae Campus Wonju Korea Republic of Graduate School Yonsei University Department of Computer Science Wonju Korea Republic of Yonsei University Division of Software Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798350373332

Collecting consistent human demonstrations has been a problematic issue in the field of a learning from demonstration. This paper presents a twin dual-arm robot testbed to collect human demonstration data while performing manual tasks in the activities of daily livings such as opening/closing a window, opening-up/closing-down a sauce bottle cap, etc. The testbed consists of the two sets of a dual-arm robot;for each robot, one-side arm has 6-DoF with a crab-gripper as its hand. Two dual-arm robots are mounted at the front and back of a table-type station. A human operator performs actions to complete a given task with the one at the back;the human operator's actions, meanwhile, are duplicated by the other robot at the front simultaneously. The experimental results show the testbed's potential to gather consistent human demonstration data for training a machine learning agent to manipulate the dual-arm robot under an imitation learning framework. © 2024 IEEE.

关键词： Bottles

来源：评论

学校读者我要写书评

暂无评论

Learning Discriminative Representation for Few-Shot Knowledge Graph Completion 7

Learning Discriminative Representation for Few-Shot Knowledg...

引用

7th International Conference on Intelligent Information Processing, ICIIP 2022

作者： Wu, Yanping Tian, Ling Hui, Bei Wang, Zhaokun Song, Lexuan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9781450396714

Few-Shot Knowledge Graph Completion (FKGC) aims to infer the missing facts of a relation with only few corresponding known instances. To accomplish this task, one prominent prerequisite is to fully explore and exploit useful information from these supporting instances. However, many existing methods usually treat the support set as a group of independent contents, and fail to explicitly model the interactions among instances. They also majorly focus on positive instances in the support set while overlooking negative ones. Generally, much useful information in the support set has not been well applied yet. Therefore, this work proposes a novel model FSDR to fully capture the fertile information within supporting sets. Extensive experiments on two benchmark datasets demonstrate our model is indeed effective and outperforms state-of-the-art FKGC approaches. © 2022 ACM.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Secrecy Maximization for Pico Edge Users in 5G Backhaul HWNs: A Quantum RL Approach

Secrecy Maximization for Pico Edge Users in 5G Backhaul HWNs...

引用

2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Sharma, Himanshu Pulkit Sharma, Gitika Kumar, Neeraj Thapar Institute of Engineering and Technology Computer Science and Engineering Department Punjab Patiala India MAQ Software Hyderabad India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9781665473408

Picocells can enhance the capacity and spectral efficiency of heterogeneous wireless networks (HWNs) and reduce user equipment (UE) power consumption. Also, the wireless backhaul technology has emerged as an efficient way to boost the data rate in dense 5G HWNs. However, due to the dynamic and distributed nature of 5G HWNs, pico edge users (PEUs) cannot resist the eavesdropping attempts. Moreover, the PEUs are located far away from the PBS and receive considerably low signal-to-interference-plus-noise ratio (SINR) values. Thus, it is critical to improve the achievable secrecy rate (SR) of PEUs for reliable and secure transmission of confidential data in the existence of eavesdroppers. Aiming to enhance the SR of PEUs, this paper formulates a joint optimization problem of beamforming and power allocation of millimeter-wave (mmWave)enabled pico base station (PBS) having backhaul wireless links with macro base station (MBS). As the system is diverse and complex, addressing this non-convex optimization problem directly is challenging. Thus, we have used the Markov decision process (MDP) to transform this optimization problem into a multi-agent reinforcement learning (MARL) problem. Then, to determine the optimal policy for MARL problem of beamforming and power allocation, we propose a Quantum reinforcement learning (QRL) approach. It uses quantum parallelism and state superposition principles to maximize the SR of PEUs. Simulation results indicate that the proposed scheme achieves 28.12% and 7.9% better secrecy rate than MAQL and MADQL schemes, respectively. © 2022 IEEE.

关键词： Markov processes

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：