检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Lin, Hui Ma, Zhiheng Hong, Xiaopeng Wang, Yaowei Su, Zhou School of Cyber Science and Engineering Xi’an Jiaotong University China Shenzhen Institute of Advanced Technology Chinese Academy of Science China School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China Xi’an Jiaotong University China

In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes (agents) and push away background ones. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further. Finally, an efficient noise depression loss is provided to minimize the negative influence of annotation noises. Extensive experiments on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised counting methods by a large margin. Code is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

An Intelligent IoT enabled Health Care Surveillance using Machine Learning

An Intelligent IoT enabled Health Care Surveillance using Ma...

引用

2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Ahamed, Saahirabanu Bhatt, Pankaj Sultanuddin, S.J. Walia, Ranjan Haque, M. Akiful Inayathahamed, S.B. Jazan University Department of Computer Science Saudi Arabia KIET Group of Institutions KIET School of Pharmacy Uttar Pradesh India Measi Institute of Information Technology Department of MCA Chennai India Model Institute of Engineering and Technology Department of Electrical Engineering Jammu India Anurag University Dept. of Pharmacy Medchal India Kalasalingam Business School Kalasalingam Academy of Research and Education Deemed to Be University Tamil Nadu 626126 India

ISBN: (纸本)9781665495295

Over the last decade, health care enabled services and accompanying advanced technology have been the subject of much research. Medical equipment, sensors, and healthcare professionals may all be linked through the Internet of Things (IoT) to deliver high-quality medical services everywhere. Patient safety has improved as a consequence, health care costs have decreased, access to health care has grown, and the sector as a whole has become more efficient and productive. According to recent study, Internet of Things (IoT)-based technologies offer enormous promise for enhancing health care. The most effective data analysis methods now use machine learning-Assisted techniques. Due to the conductivity of the top three layers, intelligent applications and services such as sickness detection, behavioural recognition, and intelligent support may be supplied. This research examined medical IoT improvements in terms of enabling technology, health care services, and implementations to address a variety of health care challenges. Additionally, potential issues and concerns about the Health Care IoT system are explored. As a consequence, future researchers interested in investigating and discovering new applications for health care IoT will have a credible resource for learning about the technology's many applications. © 2022 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation

arXiv

引用

arXiv 2023年

作者： Yuan, Meng Zhuang, Fuzhen Zhang, Zhao Wang, Deqing Dong, Jin Institute of Artificial Intelligence Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing Beijing China Zhongguancun Laboratory Beijing China

Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been an influx of KG-enhanced recommendation methods. Most of existing methods are entirely designed based on euclidean space without considering curvature. However, recent studies have revealed that a tremendous graph-structured data exhibits highly non-euclidean properties. Motivated by these observations, in this work, we propose a knowledge-based multiple adaptive spaces fusion method for recommendation, namely MCKG. Unlike existing methods that solely adopt a specific manifold, we introduce the unified space that is compatible with hyperbolic, euclidean and spherical spaces. Furthermore, we fuse the multiple unified spaces in an attention manner to obtain the high-quality embeddings for better knowledge propagation. In addition, we propose a geometry-aware optimization strategy which enables the pull and push processes benefited from both hyperbolic and spherical spaces. Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones. At the same time, we set larger margins in the area far from the origin to ensure the model has sufficient error tolerance. The similar manner also applies to spherical spaces. Extensive experiments on three real-world datasets demonstrate that the MCKG has a significant improvement over state-of-the-art recommendation methods. Further ablation experiments verify the importance of multi-space fusion and geometry-aware optimization strategy, justifying the rationality and effectiveness of MCKG. © 2023, CC BY.

关键词： Spheres

来源：评论

学校读者我要写书评

暂无评论

EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

EventTrojan: Manipulating Non-Intrusive Speech Quality Asses...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Implementation of the Optical Lock through Visible Light Communication using AES

Implementation of the Optical Lock through Visible Light Com...

引用

Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： K.P. Swain Sumant Kumar Mohapatra Puspanjali Mallik R. K. Dash Rakesh Kumar Lenka Department of ETC Trident Academy of Technology Bhubaneswar India Dept. of CSE Shailabala Women’s College Cuttack India Dept. of Electrical Electronics and Communication Engineering Galgotia University Dept. of Computer Science Central University of Odisha Sunabeda Koraput

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study presents the development and implementation of a novel optical lock using visible light communication (VLC), leveraging a custom-built mobile application and flash LED for secure information transmission. The system utilizes an Android application with a user-friendly interface to input and encrypt messages using the AES-128 bit algorithm and Base64 encoding. The encrypted message is transmitted via a smartphone’s flash LED as an OOK signal, received by a specially designed PCB composed of a photodiode and a servo motor. At the heart of the receiving mechanism is a Raspberry Pi, which is responsible for decrypting the message and executing a comparison check with a pre-stored message to determine system unlocking. This innovative approach combines the convenience of mobile technology with advanced encryption, offering a robust solution in optical locking systems.

关键词： Heart Signal processing algorithms Information processing Light emitting diodes Mobile applications Encryption Photodiodes Optical signal processing Servomotors Visible light communication

来源：评论

学校读者我要写书评

暂无评论

Pre-trained Language Model based Medical Named Entity Recognition

Pre-trained Language Model based Medical Named Entity Recogn...

引用

2021 China Automation Congress, CAC 2021

作者： Cheng, Zheng Wu, Jiaju Ji, Bin Liu, Huijun School of Automation University of Electronic Science and Technology of China Mianyang China Institute of Computer Application China Academy of Engineering Physics Mianyang China College of Civil Aviation Nanjing University of Aeronautics and Astronautic Nanjing China College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781665426473

Medical named entity recognition is an important part of structuring Chinese electronic medical records and construction of medical knowledge graph. The CCKS2019 conference organized a medical named entity recognition evaluation task to extract six types of medical entities from unstructured Chinese electronic medical records. Based on the data set of this evaluation task, pre-trained language model based entity recognition approaches are studied First, select the BiLSTM-CRF model based on random initialized word embedding as the baseline system;secondly, apply wordlvec to the baseline system;thirdly, apply ELMo to the baseline system. Experimental results show that the pre-trained language model is comparable to the best approach of this evaluation task, and the context-related pre-trained language model performs better. © 2021 IEEE

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Research Progress of Parallel Control and Management

引用

IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 355-367页

作者： Gang Xiong Xisong Dong Hao Lu Dayong Shen State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex *** paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application *** future research,application directions,and suggestions are also discussed.

关键词： ACP methodology artificial systems computational experiments parallel control parallel management parallel systems

来源：评论

学校读者我要写书评

暂无评论

Lessons Learned from Optimizing the Sunway Storage System for Higher Application I/O Performance

引用

Journal of computer science & technology 2020年第1期35卷 47-60页

作者： Qi Chen Kang Chen Zuo-Ning Chen Fellow CCF Wei Xue Xu Ji Bin Yang Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China Chinese Academy of Engineering Beijing 100088China National Supercomputing Center in Wuxi Wuxi 214100China School of Software Shandong UniversityJinan 250101China

It is hard for applications to make full utilization of the peak bandwidth of the storage system in highperformance computers because of I/O interferences,storage resource misallocations and complex long I/O *** performed several studies to bridge this gap in the Sunway storage system,which serves the supercomputer Sunway *** locate these issues and connections between them,an end-to-end performance monitoring and diagnosis tool was developed to understand I/O behaviors of applications and the *** the help of the tool,we were about to find out the root causes of such performance barriers at the I/O forwarding layer and the parallel file system *** application-aware I/O forwarding allocation framework was used to address the I/O interferences and resource misallocations at the I/O forwarding layer.A performance-aware data placement mechanism was proposed to mitigate the impact of I/O interferences and performance variations of storage devices in the ***,applications obtained much better I/O *** the process,we also proposed a lightweight storage stack to shorten the I/O path of applications with N-N I/O *** paper summarizes these studies and presents the lessons learned from the process.

关键词： high performance computing I/O interference parallel file system performance optimization resource misallocation

来源：评论

学校读者我要写书评

暂无评论

UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models

arXiv

引用

arXiv 2024年

作者： Guo, Jiayi Chen, Zan Ji, Yingrui Zhang, Liyun Luo, Daqin Li, Zhigang Shen, Yiqin Glasgow College University of Electronic Science and Technology of China Chengdu China School of Engineering University of Glasgow Glasgow United Kingdom Toursun Synbio Shanghai China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Research Institute Ubtech Robotics Corp Shenzhen China Qiyuan Lab. Beijing China Department of Computer Science Johns Hopkins University MD United States

Automated Machine Learning (AutoML) can streamline the complexities of machine learning pipelines. However, existing AutoML frameworks focus on discriminative tasks leaving generative tasks unexplored. Moreover, existing AutoML frameworks often lack interpretability and user engagement during the model development and training process, due to the absence of human-centered designs. To address these challenges, we propose UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify the automation of both discriminative (e.g., Transformers and CNNs for classification or regression) and generative tasks (e.g., fine-tuning diffusion models or LLMs). UniAutoML enables natural language interactions between users and the framework to provide real-time feedback and progress updates, thus enhancing the interpretability, transparency, and user control, where users can seamlessly modify or refine the model training in response to evolving needs. To ensure the safety and reliability of LLM-generated content, UniAutoML incorporates a safety guard-line that filter inputs and censor outputs. We evaluated UniAutoML’s performance and usability through experiments conducted on eight diverse datasets and user studies involving 25 participants. Our results demonstrate that UniAutoML not only achieves higher performance but also improves user control and trust in the AutoML process, which makes ML more accessible and intuitive for a broader audience. The code for UniAutoML is available at: https://***/richarf22/UniAutoML. © 2024, CC BY.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Management of a Microgrid using Deep Learning Techniques 5

Management of a Microgrid using Deep Learning Techniques

引用

5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Panduro-Ramirez, Jeidy Joshi, Kapil Chakravarthi, M. Kalyan Singh, Prabhdeep Bind, Naveen Kumar Banik, Ayan Universidad Tecnológica Del Perú Peru Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India VIT - AP University School of Electronics Engineering Andhra Pradesh Amaravati India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida India West Bengal Kolkata India

ISBN: (纸本)9798350398267

Our power generation grid is experiencing a dramatic modernization as a result of recent developments in smart-grid advancements. By offering a flexible method of sort of technology solar and wind power (RES) into the electricity system, microgrid (MG) plays a significant role in the modernity. Spread RES, like solar and wind, can, nevertheless, be very essentially random and occasional. It is challenging to effectively execute an MG because of these erratic resources, state of charge, and the like but on the sides of the market. This paper addressed this issue and presented a novel strategy for the real-time timetabling of an MG taking into account the adequacy of renewables, energy cost, and utility grid confusion. The suggested solution is studying and therefore does not involve an accurate model of the confusion, in contrast to traditional model-based methods that call for an indicator to estimate the confusion. The goal of the MG power process is to reduce the daily operational expenses, and it is specifically modelled as a Markov Decision Process (MDP). To resolve the MDP, a deep encouragement learning (DRL) strategy has been developed. The deep Q-network (DQN) algorithm is used to find the human brain in the DRL framework, which uses a deep fnn network to measure the ideal response role. The suggested method directly generates real-time creation schedule using the MG's current state as inputs. Furthermore, case experiments are performed to show the usefulness of the suggested strategy using total power data from the Cal Organization For Standardization (CAISO). © 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：