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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Raza, Abir Badidi, Elarbi Badidi, Basma Al Zahmi, Sarah Department of Computer Science and Software Engineering College of Information Technology UAE University P.O. Box 15551 Al-Ain United Arab Emirates

ISBN: (纸本)9783031423161

Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection

arXiv

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arXiv 2024年

作者： Yao, Liang Liu, Fan Zhang, Chuanyi Ou, Zhiquan Wu, Ting The College of Computer Science and Software Engineering Hohai University Nanjing210098 China

Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, UAV-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in UAV images make it challenging for the student model to efficiently learn the object features. In this paper, we propose a novel knowledge distillation framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then a new feature alignment method is provided to extract object-related features for enhancing student model’s knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art (SoTA) performance in two UAV-OD datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Advancements in Cybersecurity Using Deep Learning Techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Hosanna Princye, P. Ahila, A. Lakshmi, A. Anna Ragavendran, N. Purushothaman, K.E. Uma Maheswari, G. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India R.M.K. Engineering College Department of Information Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Software Analyst Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study. © 2024 IEEE.

关键词： Cyber attacks

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Time Series Fusion Model for Predicting Electric power tools and equipment 6

Time Series Fusion Model for Predicting Electric power tools...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Xue, Liang Ling, Xinghong Yang, Lei Yang, Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (纸本)9798331541699

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. The experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run. © 2024 IEEE.

关键词： Prediction models

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AmpNorm: An Effective Style Normalization Method for Single Domain Generalization 7

AmpNorm: An Effective Style Normalization Method for Single ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Hu, Jingyu Zhong, Tao He, Xilin Xie, Weicheng Song, Siyang Shen, Linlin College of Computer Science and Software Engineering Shenzhen University Shenzhen China University of Leicester LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9781839539848

Deep neural networks have demonstrated remarkable efficacy in numerous computer vision tasks. However, due to the training and testing sets of data coming from different domains, the domain gap limits the performances of deep neural networks. To enhance the generalization performance of deep neural networks, we provide a unique amplitude normalization (AmpNorm) approach to decrease the domain gap from the frequency domain perspective. Specifically, ours AmpNorm collects the amplitude spectrum from the source domain during training and then converts the images from the unseen target domain into those with a style similar to the source domain during testing. Our AmpNorm is simple yet effective, as well as plug-and-play, which is readily implemented into the majority of single-domain generalization (SDG) methods. Extensive results on three public benchmarks demonstrate that our AmpNorm can greatly improve these models’ performance on the invisible target domain. © The Institution of engineering & Technology 2023.

关键词： Deep neural networks

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Neighbor Discovery in a Multi-Transceiver Free-Space-Optical Ad Hoc Network 19

Neighbor Discovery in a Multi-Transceiver Free-Space-Optical...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Vazquez-Estrada, Jessica Bhunia, Suman Khan, Mahmudur Qian, Yicheng Huu, Nero Tran Miami University Department of Computer Science and Software Engineering OxfordOH United States York College of Pennsylvania Electrical and Computer Engineering and Computer Science Department YorkPA United States

ISBN: (纸本)9781665431620

In this paper, we present a novel neighbor discovery method for a wireless ad hoc network where each node is equipped with a Free-Space-Optical (FSO) transceivers capable of electronic beam switching. Directional neighbor discovery can be very challenging due to the requirement of strict line-of-sight (LOS) alignment and can result in long and impractical discovery time if the nodes in the network do not have prior information about each other's location. Our method guarantees that a node can discover all of its neighbors within one 360 FSO beam sweep. We present a preliminary prototype of a FSO transceiver module capable of electronic beam steering. Through experiments using the developed prototype, we demonstrate that the proposed method helps in discovering neighbor nodes with minimal delay. © 2022 IEEE.

关键词： Optical communication

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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CHAR: Composite Head-body Activities Recognition with A Single Earable Device

CHAR: Composite Head-body Activities Recognition with A Sing...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Peizhao Zhu Yongpan Zou Wenyuan Li Kaishun Wu College of Computer Science and Software engineering Shenzhen University

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 97.0% and 89.7% in user- dependent and independent cases, respectively.

关键词： Pervasive computing Performance evaluation Legged locomotion Headphones Prototypes Activity recognition Multitasking

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WAN-INT: Cost-Effective In-Band Network Telemetry in WAN With A Performance-aware Path Planner 29

WAN-INT: Cost-Effective In-Band Network Telemetry in WAN Wit...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Chen, Simian He, Dongbiao Ma, Xiaopeng Ming, Zhongxing Cui, Laizhong Sangfor Technologies Inc China China National Tobacco Corporation Anhui Province China Shenzhen University College of Computer Science and Software Engineering China China

ISBN: (纸本)9798350330717

With the development of cross-datacenter services, accurate and low-cost network performance measurement enables better traffic scheduling. However, the existing network measurement suffers from a lack of telemetry granularities and excessive costs. Besides, the implementation of INT in the WAN remains undefined. In this work, We propose WAN-INT, a two-phase path orchestration algorithm designed to address the challenges posed by limited communication resources and varying link states in WAN scenarios. By generating a telemetry policy based on this algorithm, it can effectively adapt to the requirements of various applications and network states, achieving a balance between telemetry quality and cost limitations. We conduct experiments in a commercial WAN environment. Results show that WAN-INT outperforms existing schemes and reaches a good compromise between the telemetry quality and cost constraints. Compared with the state-of-the-art telemetry system, WAN-INT effectively reduces by at least 43% of telemetry cost while ensuring telemetry quality. © 2023 IEEE.

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