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2021 International Conference on computer engineering and Innovative Application of VR, ICCEIA VR 2021

作者： Gao, Hui Wang, Wenhao Yang, Chengjin Jiao, Weile Chen, Ziwei Zhang, Tong Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

The detection and recognition of traffic signal image is an important content in intelligent transportation system. It can be applied to driver assistance system to effectively recognize traffic signal signs on the road, so as to reduce the occurrence of traffic accidents. At the same time, it also provides strong technical support for the future unmanned driving system. The main content of this paper is based on the deep learning method, using YOLOv3 and YOLOv4 algorithm to detect and recognize the traffic signal image on the road. The experimental results show that the recognition result of YOLOv4 network is better than that of YOLOv3 network. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signals

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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computer Systems Science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Traffic sign detection based on Haar and adaBoost classifier 4

Traffic sign detection based on Haar and adaBoost classifier

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2021 4th International Conference on Advanced Algorithms and Control engineering, ICAACE 2021

作者： Wang, Wenhao Gao, Hui Chen, Xiaobing Yu, Zhenyang Jiang, Mingxing Faculty of Computer and Software Engineering HuaiYin Institute of Technology Huai'an Jiangsu223003 China

Intelligent transportation system is a hot issue in the field of computer vision. Traffic sign detection is an important part of intelligent transportation system. A traffic sign detection algorithm based on Haar feature and AdaBoost algorithm is proposed in this paper. Firstly, Haar features are extracted from the positive and negative samples of the training set, and then these features are used to train the AdaBoost cascade classifier. Finally, the Haar features are extracted from the test image, and the possible traffic signs in the image are detected, the experimental results show that the method can achieve good detection effect, and provide a new idea for the detection of traffic signs. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signs

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Emotion recognition research of eye-movement feature extraction classification network in online video learning environment 3

Emotion recognition research of eye-movement feature extract...

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3rd International Seminar on Artificial Intelligence, Networking, and Information technology, AINIT 2022

作者： Liu, Shengxi Li, Ze Tao, Xiaomei College of Computer Engineering Henan Institute of Economy and Trade Henan Zhengzhou China School of information science and engineering Guilin University of Technology Guangxi Guilin China School of Computer Science and Engineering School of Software Guangxi Normal University Guangxi Guilin China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

With the rapid development of artificial intelligence technology, emotion recognition has been applied in all aspects of life, using eye movement tracking technology for emotion recognition has become an important branch of emotion computing. In order to explore the relationship between eye movement signals and learners' emotional states in the online video learning environment, we used machine learning and convolutional neural network methods to recognize eye movement signals, and classify learners' emotional states into two categories, positive and negative. The study of eye movement data under different time windows mainly includes four stages: data collection, data preprocessing, classifier modeling, training and testing. In this paper, a Eye-movement Feature Extraction Classification Network(EFECN) based on convolutional neural network is proposed for small sample data and the classification of emotion state based on eye movement. The eye movement data were transformed into images through cross-modal conversion as input of multiple different deep convolutional neural networks, and the emotional states were classified in positive and negative directions. The accuracy was used as the evaluation index to evaluate and compare the different models. The accuracy of the eye movement emotion recognition model reached 72% in the SVM model and 91.62% in the EFECN model. Experimental results show that the convolutional neural network based on deep learning has a significant improvement in recognition accuracy compared with traditional machine learning methods. © 2023 SPIE.

关键词： Emotion Recognition

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Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

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computer Modeling in engineering & Sciences 2022年第4期131卷 23-26页

作者： Qi Liu Xiaodong Liu Radu Grosu Ching-Nung Yang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburgh018373UK Institute of Computer Engineering Vienna University of TechnologyWien1040Austria Department of Computer Science and Information Engineering National Dong Hwa UniversityHualien97401Taiwan

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving ***-to-end procedures and new types of user-machine interaction are made possible via the *** the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

关键词： CPS hardware services

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Unmanned aerial vehicles advances in object detection and communication security review

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Cognitive Robotics 2024年第1期4卷 128-141页

作者： Laghari, Asif Ali Jumani, Awais Khan Laghari, Rashid Ali Li, Hang Karim, Shahid Khan, Abudllah Ayub Software College Shenyang Normal University Shenyang China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Department of Computer Science ILMA University Karachi Sindh Pakistan Department of Mechanical Engineering Technology Sindh Institute of Management and Technology Sindh Karachi Pakistan Research and development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Benazir Bhutto Shaheed University Liyari Pakistan

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research. © 2024

关键词： Object detection

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DAGC: Data-Aware Adaptive Gradient Compression 42

DAGC: Data-Aware Adaptive Gradient Compression

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lu, Rongwei Song, Jiajun Chen, Bin Cui, Laizhong Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua-Berkeley Shenzhen Institute China Dalian University of Technology School of Software Technology China Harbin Institute of Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering China China Peng Cheng Laboratory China

ISBN: (纸本)9798350334142

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers;This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication. © 2023 IEEE.

关键词： Nonlinear programming

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Diverse Hazy Image Synthesis via Coloring Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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Sketching Very Large-scale Dynamic Attributed Networks More Practically 25

Sketching Very Large-scale Dynamic Attributed Networks More ...

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34th ACM Web Conference, WWW 2025

作者： Wu, Wei Li, Shiqi Chen, Ling Li, Fangfang Luo, Chuan School of Computer Science and Engineering Central South University Changsha China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Beihang University Beijing China

ISBN: (纸本)9798400712746

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. © 2025 Copyright held by the owner/author(s).

关键词： network embedding

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