检索结果-内蒙古大学图书馆

Enhancing Offensive Language Detection with data Augmentation and Knowledge Distillation

Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

来源：评论

学校读者我要写书评

暂无评论

Training Samples Selection Method Based on Model-data-Driven for Maritime Radar Target Detection

引用

IEEE Sensors Journal 2025年第11期25卷 19896-19908页

作者： Chen, Wenhao Li, Zhengzhou Ding, Zhiquan Qin, Tianqi Azeem, Abdullah Gan, Haolin Li, Aoran Chongqing University School of Microelectronics and Communication Engineering Chongqing40044 China Sichuan Institute of Aerospace Electronic Equipment Chengdu610000 China Chongqing University School of Computer Science Chongqing40044 China

Multidimensional feature extraction and fusion algorithms are widely utilized to improve the performance of radar maritime target detection. However, these methods often suffer from underfitting and overfitting issues due to the uneven distribution of training samples. To enhance the performance of maritime target detection while mitigating the issues of underfitting and overfitting, an effective approach is to improve the comprehensiveness of training samples. This study proposes a novel training sample selection method based on model-data-driven maritime radar target detection. A surrogate equation is constructed in this study using existing empirical models and measured data. Subsequently, it dynamically selects training samples exhibiting strong coverage and compactness, guided by the global and local variation characteristics of this surrogate equation. Experimental results show that, when using the same feature and classifier conditions, the feature classifiers formed by the proposed method outperform those built using traditional sample selection approaches (such as random assignment, expert knowledge, and statistical methods). This superiority is evident across various target types, including daily vessels, small fishing boats, and marine buoys, as well as under diverse sea conditions and observation parameters like incidence angles. This approach not only enhances the performance of maritime target detection, but also ensures the generalization ability of the method. © 2025 IEEE.

关键词： Fishing vessels

来源：评论

学校读者我要写书评

暂无评论

Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

引用

Internet of Things and Cyber-Physical Systems 2023年第1期3卷 309-322页

作者： Kumar, Mohit Raj, Hritu Chaurasia, Nisha Gill, Sukhpal Singh Department of Information Technology National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Punjab Jalandhar India School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission. © 2023 The Authors

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Trajectory-Aware Task Coalition Assignment in Spatial Crowdsourcing (Extended Abstract) 40

Trajectory-Aware Task Coalition Assignment in Spatial Crowds...

引用

40th IEEE International Conference on data engineering, ICDE 2024

作者： Xie, Yuan Wu, Fan Zhou, Xu Luo, Wensheng Yin, Yifang Zimmermann, Roger Li, Keqin Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Singapore State University of New York Department of Computer Science New Paltz United States School of Data Science Chinese University of Hong Kong Hong Kong A STAR Singapore

ISBN: (纸本)9798350317152

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms. © 2024 IEEE.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Real-Time Atmospheric Duct Height Prediction Framework Based on Spatio-Temporal to Ensure Maritime Communication Security 18th

Real-Time Atmospheric Duct Height Prediction Framework Based...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Ben Yan, Ke Hui, Bei Huang, Qian Gao, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China ChengDu China KASH Institute of Electronics and Information Industry Kashi China

ISBN: (纸本)9783031714665

Atmospheric duct (AD) is a natural phenomenon affected by humidity or air pressure diversities, which impacts the propagation of electromagnetic signals. Recent studies have been focused on ADH prediction based on meteorological data which makes the task of real-time ADH prediction challenging and impacts the global representation of ADs leading to maritime communications security issues. We address these issues by discovering a novel way to abstract the spatial and temporal features from the historical data. Thus, we proposed a framework based on DNN models and attention mechanisms to predict the ADH in a real-time form from the abstracted features. Our experimental results demonstrate that our technique improves the performance of real-time prediction and reduces the acquisition cost, professional devices can receive it in real-time through IoT to avoid dangerous areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ducts

来源：评论

学校读者我要写书评

暂无评论

Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

引用

8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Air quality and dust level monitoring systems in hospitals using IoT

引用

Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Murali, N. David, D. Beulah Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350334357

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

GuardGrid: A Queriable and Privacy-Preserving Aggregation Scheme for Smart Grid via Function Encryption

引用

IEEE Internet of Things Journal 2025年第11期12卷 17622-17633页

作者： Yu, Pengfei Huang, Weicong Zhang, Ran Qian, Xinyuan Li, Hongwei Chen, Hanxiao China Electric Power Research Institute State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China

Smart grids have revolutionized electricity management by leveraging real-time consumption data, enabling more efficient power control through advanced algorithms. However, this transformation raises significant privacy and security concerns due to the extensive collection of user data. Current solutions face challenges, such as aggregator gateway misbehavior, lack of support for function queries, and the need to balance privacy with efficiency. In this article, we propose FEHH, a novel scheme that ensures both privacy preservation and verifiable aggregation. It allows multiple aggregators to perform inner-product computations on encrypted data while safeguarding the aggregated results from the aggregator. Additionally, it supports verification of aggregated data’s correctness using Linear Homomorphic Hash. Building on FEHH, we introduce GuardGrid, a privacy-preserving aggregation scheme for smart grids that inherits FEHH’s core features and adds support for essential arithmetic operations necessary for function queries. This allows cloud servers to respond to queries from either the control center or users without compromising data confidentiality. Experimental results show that the encryption overhead of GuardGrid is only 7% of that of the PPDA scheme, and its communication overhead is 123x less. These results demonstrate that GuardGrid significantly reduces computation and communication costs, providing a more sustainable and cost-effective smart grid solution. © 2014 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A IoT Edge Advanced VANET Technique for Vehicle Communication and Improve Safety in Hill Station Critical Scenario 8

A IoT Edge Advanced VANET Technique for Vehicle Communicatio...

引用

8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Institute of Information Technology Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The safety of the vehicles is given great importance by advanced intelligent transportation systems. We are here to make sure that swift transit vehicles and infrastructure communicate with one other quickly. The majority of the vehicles need regular monitoring for appropriate warnings and road maps to achieve their ultimate objective in a timely manner in the hill station bad environment, and travel in hairpin bends and risky turning in particular is a duty that is vital for ITS. Here, my study will provide a strategy for preventing accidents by applying a sophisticated early warning system before notifying the car in a certain circumstance. In order to prevent accidents, machine learning is a technique where the system automatically learns and enhances the rapid object detection stage in bobby pin road turning scenarios. Here, my study offers effective unsupervised learning of these characteristics based on the acquired data, which in turn serves as the foundation for the clustering I performed, which successfully builds a mobile ad hoc network. In this work, we suggested an innovative automated discovering vehicle prier that uses the UNetXST technique in real-time to notify warring for vehicle in three-way shining LED light with blinking mode, slowing warring message in display unit, and beep alarm sound. By combining V2I and V2V technologies and sending messages to the central traffic light management system, we highlighted safety and communication. In worst-case scenarios, we employ a v2v technology sensor to detect the vehicle that will cross the junction road, and we investigate two-way merging technology's potential to implement and resolve these problems in transparent object tracking in the turning system. We looked at future direction and hurdles at this time in addition to a case study illustrating a VANET-based scenario of a critical urban road turning, receiving the earliest notice to flee from a traffic collision, and experiencing the perfect EV momen

关键词： Vehicles

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：