检索结果-内蒙古大学图书馆

27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An In-Depth Study of Cybersecurity Threats and Intelligent Broadcasting Network Topology

An In-Depth Study of Cybersecurity Threats and Intelligent B...

引用

Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Usha Chauhan Manali Gupta Department of Electrical Electronics and Communication Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Business System Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may be exposed if a component doesn't respond in real time, reducing efficiency and reliability. Superannuated networks create cyber security risks. This article explores a secure cyber infrastructure, related breakthroughs, and cyber security concerns in a complex system. Cybersecurity goals, objectives, and risk assessments are complete. These papers are summarized. Prioritize security. Then, examine smart grid hacks to uncover weaknesses and their repercussions. This research designed a cyber-security plan for breaches, counterattacks, and actions. Future research is discussed.

关键词： Renewable energy sources Privacy Lattices Real-time systems Smart grids Risk management Reliability

来源：评论

学校读者我要写书评

暂无评论

Challenges in internet of things towards the security using deep learning techniques

引用

Measurement: Sensors 2022年 24卷

作者： Ravikumar, K.C. Chiranjeevi, Pandi Manikanda Devarajan, N. Kaur, Chamandeep Taloba, Ahmed I. Department of Computer Science Engineering Sridevi Women's Engineering College India Ace Engineering College Ghatkesar Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College Medchal - Malkajgiri District Telangana 500100 India Computer Science Department Jazan University Jizan Saudi Arabia Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

Securing IoT devices and delivering end-to-end security in an IoT ecosystem presents a variety of issues. Regardless of the way that security concerns are not new with regards to data innovation, the qualities of numerous IoT arrangements give new and special security issues. It's anything but a main concern to resolve these issues and guarantee the security of IoT items and administrations. Clients should have certainty that IoT gadgets and related information administrations are secure, especially as this innovation develops more unavoidable and coordinated into our day by day lives. The goal of this article is to provide an overview of the Internet of Things and to go through all of the known security concerns that the Internet of Things faces today. All of the findings are based on publicly available documentation for essential Internet of Things components. Threats to IoT security, both inherent and newly developed, are discussed, as well as numerous potentials. The attack surfaces of the IoT system are examined, as well as the potential hazards associated with each surface. We then, at that point go over the benefits, weaknesses, and qualities of every Deep Learning approach for IoT security. We talk about the advantages and disadvantages of applying Deep Learning to IoT security. Future exploration headings could be founded on these chances and difficulties. © 2022

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system

A Novel and Accurate Method of Diagnosis via AI and ML for S...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： J Ruby Elizabeth Mathews Emmanuel E Aswini Shriram Pathak M. Bhuvaneswari Abhishek Choubey Department of Computer Science and Business System Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Sathyabhama university of science and technology chennai Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering IES Insititute of Technology and Management IES University Bhopal M.P. India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore Sreenidhi institute of science and technology Yamnampet ghatkesar Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both ‘weights’ while ‘bias’ factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application.

关键词： Accuracy Smart healthcare Merging Medical services Data collection Data models Diabetes

来源：评论

学校读者我要写书评

暂无评论

Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

引用

International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network system for Next Era Internet of Things

引用

Journal of Healthcare engineering 2023年第1期2023卷

作者： Venkatesh, J. Shukla, Prashant Kumar Ahanger, Tariq Ahamed Maheshwari, Manish Pant, Bhasker Hemamalini, R. Rani Halifa, Awal Center for System Design Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522502 India College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science & Applications Makhanlal Chaturvedi National University of Journalism and Communication Madhya Pradesh Bhopal India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of Electrical and Electronics Engineering St. Peter's Institute of Higher Education and Research Tamil Nadu Chennai India Kwame Nkrumah University of Science and Technology Kumasi Ghana

The Internet of Things (IoT) is now growing dramatically on various levels and helps to digitize various vital industries quickly. The most difficult obstacle for BCIs to overcome is the fact that not everyone has the same brain. Every new session requires the BCI to learn from the user's brain, which is accomplished via the use of machine learning. However, this learning process is time-consuming. Calibration time refers to the amount of time it takes for the BCI to adapt to the user's brain in order to properly categorize their thoughts and determine their meaning. The patient has had to wait an arduous and tiresome length of time for the system to be completely functioning up until now because of this calibration, which may take up to 20-30 minutes. The aim of this paper was to find a way to decrease the amount of time required for calibration to the smallest amount feasible. In the first section of this paper, a first effort is made to determine the optimum number of features required for the BCI to operate reasonably, taking into consideration all of the calibration data provided. When the results were averaged across five participants, the percentage of properly identified thoughts was just 67.15 percent. Transfer learning was used in order to improve the performance of the BCI while simultaneously decreasing the calibration time. It is feasible to decrease the amount of calibration required for the categorization of thoughts coming from a new target subject by using knowledge collected from previously recorded subjects to the greatest extent possible in transfer learning. It was determined that existing methods were superior, and a new methodology was created that required just 24 seconds of calibration data while accurately identifying 86.8% of the thoughts. In order to alleviate mental stress and anger, the system suggested fits effectively with a deep learning network. This paper proposes a brain learning framework that uses a neural network model that is

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Visible Light Positioning Based on a Single Luminaire: A Novel Visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

引用

IEEE International Conference on communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RELD: Regularization by Latent Diffusion Models for Image Restoration

arXiv

引用

arXiv 2025年

作者： Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo Department of the Arts University of Bologna Bologna40123 Italy Department of Political Sciences Communication and International Relations University of Macerata Macerata62100 Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Modena41125 Italy Department of Environmental and Science Policy University of Milan Milan20133 Italy Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO63130 United States

In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Performance Evaluation of Topcon, Hit, and Perc Tandem Solar Cells: Analysing Performance and Stability

SSRN

引用

SSRN 2024年

作者： Yi, Junsin Rahman, Rafi Ur Khokhar, Muhammad Quddamah Madara, Polgampola Chamani Aida, Maha Nur Jony, Jaljalalul Abedin Alamgeer Yousuf, Hasnain Chu, Mengmeng Park, Sangheon Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of

The evolution of photovoltaic technologies has led to significant advancements in tandem solar cells, notably TOPCon, HIT, and PERC cells. Historically, these technologies have demonstrated substantial improvements in efficiency and stability. This study conducts a comprehensive performance evaluation of TOPCon, HIT, and PERC tandem solar cells by measuring light current-voltage (LIV) characteristics in both series and parallel connections under varying sunlight intensities (0 to 0.5 suns). The maximum efficiency recorded for the TOPCon/TOPCon cells in series connection was 6.63%, while in parallel, it was 25.78%. For HIT/HIT cells, series and parallel connections yielded maximum efficiencies of 27.00% and 6.58%, respectively. PERC/PERC cells achieved efficiencies of 17.81% in series and 21.38% in parallel. These results were obtained by systematically varying the sunlight intensity from 0 to 0.5 suns, revealing that TOPCon technology excels under low-light conditions. Future work will optimize material properties and interconnection methods to enhance performance and commercial viability. © 2024, The Authors. All rights reserved.

关键词： Current voltage characteristics

来源：评论

学校读者我要写书评

暂无评论

Multi-secret Sharing Scheme for Zero Trust Environment

Multi-secret Sharing Scheme for Zero Trust Environment

引用

IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Qin Zhou Rumiao Yang Hao Zhou Lei Cai Yongyou Network Technology Co. Ltd Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China The 4th System Department North China Institute of Computing Technology Beijing China

The zero-trust concept of “never trust, always verify” injected new vitality into the development of network security. However, the related research on zero trust is still preliminary, and the research on secure data transmission is relatively less. How to achieve secure data transmission in zero trust environment is a problem to be solved. Aiming the data transmission scenario of private data secret sharing among agents, this paper proposed a secure data transmission scheme based on multi-secret sharing to solve the problems of identity comparison confirmation of participants and adjust the access structure according to the security value of trust evaluation in existing secret sharing schemes. The proposed scheme adopts the secret sharing design without the trusted third party, which avoids the security risk of trusted third party disclosure in the traditional secret sharing scheme. Secondly, in the system parameter setting phase, the identity of participants and zero-trust identity database are compared to combine the characteristics of zero-trust “identity authentication”, to improve the security of secret sharing. Finally, during secret distribution, zero-trust trust evaluation security values are used to adjust the access structure based on zero-trust ‘trust evaluation’ to ensure secure data transmission.

关键词：

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：