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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

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A Comparative Analysis of Query Generation Methods for IoT Middleware Evaluation 17

A Comparative Analysis of Query Generation Methods for IoT M...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng W. Jayaraman, Prem Prakash Deakin University School of Information Technology Melbourne Australia Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia

ISBN: (纸本)9798350351637

The rapid expansion of Internet of Things (IoT) applications necessitates the demand for efficient IoT middleware platforms, especially Context Management Platforms (CMPs) for accessing real-time contextual information. Current IoT middleware evaluation methods lack accessibility, posing a significant challenge for standardized performance analysis of CMPs. This paper introduces a novel scenario-based context query generation approach for assessing the data retrieval performance of a CMP. The proposed approach enables scene creation using either IoT data or real-world images, employing both data-driven and image-driven scene generation methods. This paper presents the challenges, limitations, and applicability of each approach in scene generation through a qualitative evaluation. The scenes are transformed into scene graphs, enabling inference to identify situations. Both approaches generate queries reflecting dynamic real-world situations, allowing for random query loads to evaluate CMPs fairly. This paper validates the proposed approach using real-world IoT datasets and camera images with a focus on bicycle riding safety use cases. The evaluation provides insights into the contextual completeness of scene graph representations crucial for accurately capturing real-world situations. The proposed approach generates queries of varying complexity, with the only difference being the underlying mechanism of scenario generation, providing a streamlined and user-friendly method for evaluating the data retrieval performance of CMPs. © 2024 IEEE.

关键词： Middleware

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew 8

From Linguistics to Practice: a Case Study of Offensive Lang...

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8th Workshop on Online Abuse and Harms, WOAH 2024 - co-located with NAACL 2024

作者： Liebeskind, Chaya Vanetik, Natalia Litvak, Marina Department of Computer Science Jerusalem College of Technology Jerusalem Israel Department of Software Engineering Shamoon College of Engineering Beer-Sheva Israel

ISBN: (纸本)9798891761056

The perception of offensive language varies based on cultural, social, and individual perspectives. With the spread of social media, there has been an increase in offensive content online, necessitating advanced solutions for its identification and moderation. This paper addresses the practical application of an offensive language taxonomy, specifically targeting Hebrew social media texts. By introducing a newly annotated dataset, modeled after the taxonomy of explicit offensive language of (Lewandowska-Tomaszczyk et al., 2023), we provide a comprehensive examination of various degrees and aspects of offensive language. Our findings indicate the complexities involved in the classification of such content. We also outline the implications of relying on fixed taxonomies for Hebrew. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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A deep neural network based feature fusion panoramic segmentation method

A deep neural network based feature fusion panoramic segment...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Fei, Hongyan Mokhtarzadeh, Amir Ali Zhou, Jiayu Geng, Junzuo Huaiyin Institute of Technology School of Computer and Software Engineering 223003 China Jiangsu Foreign Expert WorhShop Jiangsu Huaian223003 China Nanjing Institute of Technology School of Computer and Software Engineering 211167 China

Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Multi-Person Estimation Method Combining Interactive and Contextual Information 5

Multi-Person Estimation Method Combining Interactive and Con...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Chen, Zhuxiang Li, Shujie Jia, Wei School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China

ISBN: (纸本)9798350385557

Multi-person pose estimation based on monocular cameras is one of the hot research topics in computer vision. Current monocular multi-person 3D pose estimation methods often treat individuals as independent entities for estimation and are based solely on single-frame assessments. This methodology has notable limitations, particularly its insufficient utilization of the rich interactive information interlinking individuals, as well as the contextual continuity inherent across sequential frames. To address these challenges, our study introduces a novel method for monocular multi-person 3D pose estimation. Firstly, it utilizes the attention mechanism of Transformer to learn the contextual and interactive information of both local and global coordinates. Subsequently, the methd employs low-order convolution operators of a root trajectory network to further assimilate global coordinate information. Finally, an optimization algorithm is used to adaptively filter frames with significant occlusions. Our method effectively integrates the interactive information among multiple individuals and the contextual information across multiple frames. Experimental results on the public dataset MuPoTS-3D demonstrate that compared to the state-of-the-art methods, our approach shows a 4.8% improvement in average joint accuracy and a 19. 4% reduction in average root joint error. © 2024 IEEE.

关键词： computer vision

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