检索结果-内蒙古大学图书馆

A unified user behavior model for trajectory-based tasks with different types of path constraints

science China(Information sciences) 2023年第10期66卷 316-318页

作者： Hao ZHANG Jin HUANG Huawei TU Feng TIAN Guozhong DAI Hongan WANG Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Information Technology La Trobe University

An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicitly perform various trajectory-based interactions, such as navigating through menus [2], entering the boundary of a button,

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid energy efficient deep learning and redactable consortium blockchain-based secure framework for smart parking in smart cities

引用

Multimedia Tools and Applications 2024年第37期83卷 85391-85420页

作者： D, Amu Baskaran, Santhi Department of Computer science and Engineering Puducherry Technological University Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

Management of vehicular parking in the crowded environment is the indispensable requirement for the smart city scenario. The advent and potential development of Information Communication Technologies (ICT) and Internet of things (IoT) helped in proper thinking, operation and planning of parking management in smart city environment. But they suffer from the issue of privacy, security, integrity, energy efficiency, communication bandwidth and centralization. In this paper, Hybrid Deep learning and Redactable consortium blockchain-based Secure Framework (HDLRCBSF) is proposed for achieving the objective of energy-efficient smart parking in smart cities. This HDLRCBSF adopted the merits of Deep Bi-LSTM network for exploring the data associated with parking zones, such that optimal parking zones can be potentially allocated to the drivers with the ideal timing and location. It used the Redactable consortium blockchain network using Road Side Units (RSUs) which helped in attaining data verification and authentication at the security layer. It also implemented Threshold Chameleon Hash (TCH) algorithm for the objective of encrypting and decrypting the data related to the parking zones to establish secure communication. It adopted the significance of Deep Auto Regression Feature Augmented Bi-LSTM Networks in the analysis layer for exploring the cloud data that provides optimal parking lot to the drivers depending on the requirements that include cost, distance, and time. The quantitative and qualitative investigation conducted on the proposed HDLRCBSF confirmed its efficacy in terms of best parking space allocation rate of 21.32%, better than the baseline approaches that offered to the drivers with maximized privacy and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Deep Learning and Blockchain for Detection and Prevention Abuse of Privileges 1

Deep Learning and Blockchain for Detection and Prevention Ab...

引用

1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Algabri, Malek Alsabri, Abdulrahman A. Al-Azzani, Abdoualwase M. Alhrazi, Firdaus Alsabry, Ayman Al-Shalabi, Ahmed Abdullah Department of Computer Science Sana’a University Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen

ISBN: (纸本)9798331533557

Privilege abuse within organizations is a major concern because it can lead to security breaches caused by malicious actions or behavior from employees. It is one of the key sources of insider threats in organizational systems. This paper presents an integrated approach to detecting and preventing privilege abuse by employees, focusing on identifying malicious activities. This is achieved by combining artificial intelligence and blockchain technology. RNN is used to analyze malicious behavior, while blockchain ensures a secure, decentralized ledger to prevent abuse. The goal is to combine the RNN model with blockchain technology to detect and prevent privilege abuse. The LSTM model was implemented as an API to identify any malicious activity by employees, and the classification results were stored on the blockchain to ensure security, trust, and data integrity. Experiments using the CERT r4.2 dataset demonstrate that the proposed approach outperforms current state-of-the-art techniques, achieving a classification accuracy of 98.75% with low false positives and false negatives in detecting insider threats. © 2024 IEEE.

关键词： Blockchain Insider attacks LSTM Privileges abuse Recurrent Neural Network

来源：评论

学校读者我要写书评

暂无评论

Bipolar interval-valued fuzzy set in graph and hypergraph settings

引用

Journal of Intelligent and Fuzzy Systems 2023年第2期44卷 1755-1767页

作者： Gong, Shu Hua, Gang School of Information and Control Engineering China University of Mining and Technology Xuzhou China Department of Computer Science Guangdong University Science and Technology Dongguan China

Graphs and hypergraphs are popular models for data structured representation. For example, traffic data, weather data, and animal skeleton data are all described by graph structures. Interval-valued fuzzy sets change the membership function of general fuzzy sets from single value functions to interval-valued functions, and thus describe the fuzzy attributes of things in terms of fuzzy intervals, which is more in line with the characteristics of fuzzy objectives. This paper aims to define the bipolar interval-valued fuzzy hypergraph to reveal the inner relationship of fuzzy data, and give some characterizations of it. The characteristics of bipolar interval-valued intuitionistic fuzzy hypergraph and bipolar interval-valued Pythagorean fuzzy hypergraph are studied. In addition, we discuss the characteristics of the bipolar interval-valued fuzzy threshold graph. Finally, some instances are presented as the applications of bipolar interval-valued fuzzy hypergraphs. © 2023 - IOS Press. All rights reserved.

关键词： Membership functions

来源：评论

学校读者我要写书评

暂无评论

Cloud Detection in Satellite Imagery Using Deep Convolutional Neural Networks (CNN)

Cloud Detection in Satellite Imagery Using Deep Convolutiona...

引用

2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ngozi Ukamaka, Okonkwo Ozighor Enihe, Raphael Jubril, Abdullahi Monday University of Abuja Department of Computer Science Abuja Nigeria Base University of Abuja Department of Computer Science Abuja Nigeria Airforce Institute of Technology Department of Computer Science Abuja Nigeria

ISBN: (纸本)9798350358155

The advent of Convolutional Neural Networks (CNNs) has significantly impacted the field of object detection in remote sensing. This study explores the efficacy of CNN-based techniques in various everyday items, including electronic object detection. The dataset used in the research was meticulously assembled, incorporating diverse conditions such as scale, orientation, lighting, and background. The dataset was then subjected to data preprocessing, annotation, and model development, ensuring its quality and fidelity. The CNN model underwent an exhaustive 50 epochs of training, achieving an impressive 95% training accuracy and 96% validation accuracy. The model's precision, recall, and precision were also impressive, with a recall score of 95%. The CNN model's performance was compared with other prominent algorithms, highlighting its transformative potential in remote sensing object detection. The Cloud and Non-Cloud Image Dataset, a curated collection of 1600 high-resolution images from Planet Labs satellite data, serves as a valuable resource for advancing remote sensing research and development. The dataset was enriched through data loading and resizing, normalization, data splitting, augmentation techniques, label encoding, and training-validation split. The development of a sophisticated deep learning model, utilizing convolutional neural networks (CNNs), was central to the project's success. The model's training and validation data showed impressive accuracy, recall, and precision rates, surpassing traditional algorithms. The findings have implications for various applications, such as weather forecasting and environmental monitoring, where accurate cloud identification is crucial. Overall, this study contributes valuable insights and methodologies to the fields of remote sensing and image analysis. © 2024 IEEE.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Bug Classification Depend Upon Refactoring Area of Code

引用

Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 61-77页

作者： Singh, Satwinder Jalal, Maddassar Kaur, Sharanpreet Department of Computer Science and Technology Central University of Punjab Punjab Bathinda India Department of Computer Science and Engineering Akal University Punjab Talwandi Sabo India Department of Computer Science Mata Gujri College Punjab Sri Fatehgarh Sahib India

Due to rapid development in the software industry, various software is being developed, which compromises the software quality. As long as time passes, the software which compromises with the quality starts showing bugs which adversely affect the working of the software system. Sometimes software undergo repeated addition of functionality, so various classes and functions in software systems become bulky and cause smells in code. Code smells also degrade the software quality, increasing the software system's maintenance cost. So these bad smells should be appropriately detected and eliminated from the software system well on time. With the identification of smells in code, one could easily map the refactoring area of code to improve the code. Improving the code will help in preventive maintenance to identify the bugs quickly. To solve this problem, the current study will focus on the five types of code smells: Data Class, God Class, Feature Envy, Refuse Parent Request and Brain method for identification of bugs in the code. The data set used for this study includes the smell extraction and identification from two versions of Eclipse, i.e. Eclipse 3.6 and Eclipse 3.7, which are renowned open soured industry size software systems. Later on, supervised machine learning classifiers j48, Random Forest, and Naive Bayes are used to identify the bugs in code at the class level. The results show that j48 performed well and provides high accuracy for the identification of bugs with the help of bad smells. © 2023, The Institution of Engineers (India).

关键词： Program debugging

来源：评论

学校读者我要写书评

暂无评论

HORIZON-FREE REINFORCEMENT LEARNING IN ADVERSARIAL LINEAR MIXTURE MDPS 12

HORIZON-FREE REINFORCEMENT LEARNING IN ADVERSARIAL LINEAR MI...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Ji, Kaixuan Zhao, Qingyue He, Jiafan Zhang, Weitong Gu, Quanquan Department of Computer Science University of California Los Angeles United States Department of Computer Science and Technology Tsinghua University China

Recent studies have shown that the regret of reinforcement learning (RL) can be polylogarithmic in the planning horizon H. However, it remains an open question whether such a result holds for adversarial RL. In this paper, we answer this question affirmatively by proposing the first horizon-free policy search algorithm. To tackle the challenges caused by exploration and adversarially chosen reward over episodes, our algorithm employs (1) a variance-uncertainty-aware weighted least square estimator for the transition kernel;and (2) an occupancy measure-based technique for the online search of a stochastic policy. We show that our algorithm achieves an Õ((d + log |S|)√K + d2) regret with full-information feedback, where d is the dimension of a known feature mapping linearly parametrizing the unknown transition kernel of the MDP, K is the number of episodes, |S| is the cardinality of the state space. We also provide hardness results to justify the near optimality of our algorithm and the inevitability of log |S| in the regret bound. © 2024 12th International Conference on Learning Representations, ICLR 2024. 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

Stress Expression Identification Model for Emotion-Driven Association Calculation over Social Network Blogs 12th

Stress Expression Identification Model for Emotion-Driven A...

引用

12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Zhou, Tie Hua Wang, Jinwei Wang, Ling Hao, Haoyu Bi, Tianshuo Department of Computer Science and Technology School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9789819706686

Emotion and stress expression are deeply intertwined. Analyzing blog data from social network platforms reveals that specific emotions often appear simultaneously with expressions indicating stress, and emotional expressions in different stress scenarios also display unique patterns. This paper introduces the Stress Expression Identification (SEI) model based on Emotion Sequence Pattern Analysis (ESPA), which can identify user stress accurately by exploring the corresponding rules between emotion and stress. We constructed a stress emotion dictionary, mined the pattern rules of emotion sequence under different stress scenarios, and established a stress emotion rule set, aiming at identifying and understanding stress more accurately. The experimental results show that this model achieved a higher accuracy rate in stress identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Passersby-Anonymizer: Safeguard the Privacy of Passersby in Social Videos 18

Passersby-Anonymizer: Safeguard the Privacy of Passersby in ...

引用

18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Ma, Jingzhe Luo, Haoyu Huang, Zixu Jin, Dongyang Wang, Rui Briffa, Johann A. Poh, Norman Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China University of Malta Department of Communications and Computer Engineering Malta

ISBN: (纸本)9798350364132

In the current era of pervasive short video content, the exposure of passersby's data frequently raises privacy concerns. Traditional anonymization techniques for passersby, like blurring and mosaicing, are often used before uploading such videos. However, these methods tend to degrade the informational richness of the visual content, markedly reducing the quality of the anonymized videos. Recent advancements of diffusion models have paved the way for text-guided image and video synthesis, yet applying these models to the anonymization of passersby poses three main challenges: i) bridging the domain gap between specific passersby data and high-quality image/video datasets that are used for pre-training diffusion models, ii) ensuring temporal consistency in the anonymized videos, and iii) preserving the integrity of video subjects' content while exclusively anonymizing passersby-related information. To address these challenges, we propose the Passersby-Anonymizer, a novel diffusion-based framework for anonymizing identity-specific attributes in video content. At its core, our model introduces a spatial content adapter (SCA) to adapt to the visual patterns of passersby image datasets. We introduce a Temporal Content Stabilizer (TCS) to maintain the temporal consistency of the anonymized videos. Furthermore, we design a mask-aware training strategy that specifically targets the anonymization of the mask region while preserving the integrity of other contents. Our experimental evaluations demonstrate that our model effectively addresses the challenge of anonymizing passersby without compromising the informational integrity of the social videos. The source code is available at https://***/HappyDeepLearning/Passersby-Anonymizer. © 2024 IEEE.

关键词： Anonymity

来源：评论

学校读者我要写书评

暂无评论

Multimodal Emotion Recognition in Human–computer Interaction Using MFF-CNN 1

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Ahmad, Akram Singh, Vaishali Upreti, Kamal Department of Computer Science Maharishi University of Information Technology Lucknow India Department of Computer Science Christ University Ghaziabad India

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

The rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing human–computer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the Human–computer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. This paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. This research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. Through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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：