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AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Technique and Disinformation Detection using Pre-Trained Language Models On Imbalanced Datasets 1

AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Techni...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： El-Sayed, Ahmed Nasr, Omar El-Madany, Nour El-Din Department of Computer Engineering Arab Academy for Science and Technology Egypt Department of Electronics And Communication Arab Academy for Science and Technology Egypt

ISBN: (纸本)9781959429272

This paper presents the pipeline developed by the AAST-NLP team to address both the persuasion technique detection and disinformation detection shared tasks. The proposed system for all the tasks’ sub-tasks consisted of preprocessing the data and finetuning AraBERT on the given datasets, in addition to several procedures performed for each subtask to adapt to the problems faced in it. The previously described system was used in addition to Dice loss as the loss function for sub-task 1A, which consisted of a binary classification problem. In that sub-task, the system came in eleventh place. We trained AraBERT for task 1B, which was a multilabel problem with 24 distinct labels, using binary cross-entropy to train a classifier for each label. On that sub-task, the system came in third place. We utilised AraBERT with Dice loss on both subtasks 2A and 2B, ranking second and third among the proposed models for the respective subtasks. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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Performance Analysis of Machine Learning Algorithms for Password Strength Check 1

Performance Analysis of Machine Learning Algorithms for Pass...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Divya, R. Gaganashree Devamane, Shridhar B Dharshini, V. Deepika, S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350339727

Passwords are a vital component of system security, providing a simple, direct means of protecting a system and representing the identity of an individual for a system. However, the same patterns that people use to create passwords also render them vulnerable to attack. Since the bulk of records has weak passwords or well-known password patterns, password cracking algorithms have been developed to anticipate the password both offline and online. In order To prevent these vulnerabilities from being exploited, it is critical for companies to realize the risks that passwords provide and to create robust policies governing the selection and use of passwords. The proposed model employs multiple Machine Learning (ML) methods, including Decision Tree (DT), Logistic Regression (LR), Random Forest Classifier (RF), and K Nearest Neighbor, pushing users to select a strong password as a protection against these online and offline attacks. The Random Forest Classifier had the best results during the testing of the models over the test set, with an accuracy of 98%, and Logistic Regression achieved the lowest accuracy of 82%. © 2023 IEEE.

关键词： Decision trees

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Extending generalized unsupervised manifold alignment

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science China(Information sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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OpenAnnotate2: Multi-Modal Auto-Annotating for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhou, Yijie Cai, Likun Cheng, Xianhui Zhang, Qiming Xue, Xiangyang Ding, Wenchao Pu, Jian School of Computer Science Fudan University Shanghai China Independent Researcher Shanghai China SenseAuto Inc Shanghai China Computer Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for BrainInspired Intelligence Fudan University Shanghai China

The demand for high-quality annotated data has surged in recent years for applications driven by real-world artificial intelligence, such as autonomous driving and embodied intelligence. Consequently, the development of a tool that can assist humans in the highly automated and high-quality annotation of large-scale, multi-modal data is of significant importance and urgency for both academic research and practical applications. Most existing multi-modal data annotation tools require frame-by-frame, object-by-object annotation with keyboard and mouse, making it challenging to provide high-quality and real-time annotations for 2D images and 3D point clouds in highly open scenarios like autonomous driving. To address these challenges, we propose OpenAnnotate2, which understands human intentions based on natural language prompt, and formulates plans to decompose and execute complex multi-modal data annotation tasks. Additionally, the tool can continually enhance its cognitive and annotation capabilities with minimal human-computer interaction, through an ever-updating external knowledge base. This significantly simplifies the annotation workflow, paving the way for the creation of massive datasets suitable for large-scale visual models. The source code will be released at https://***/Fudan-ProjectTitan/OpenAnnotate. IEEE

关键词： Three dimensional displays

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Clinical Decision Support System for Anemia Identification and Severity Stratification 1

Clinical Decision Support System for Anemia Identification a...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Vidhya Shree, H. Yukthi, H.S. Vandana, W.M. Chirchi, Khushi E. Saha, Jayita Dayananda Sagar University Computer Science and Engineering Bengaluru India Manipal Institute of Technology Bengaluru Computer Science and Engineering Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350371802

The research addresses the pervasive challenges faced by anemic individuals globally. This innovative solution goes beyond traditional approaches by incorporating real-time data collection, including critical parameters such as Complete Blood Count (CBC), age, and gender providing a dynamic and holistic view of an individual's health status. The integration of CBC data, coupled with demographic information, enhances the precision of the monitoring process, allowing for a more accurate assessment of anemia risk and severity. The collected real-time data is seamlessly transmitted to a centralized cloud platform, where advanced machine learning models, specifically recurrent neural networks (RNNs), come into play. Trained on a diverse dataset that includes CBC counts, age, and gender, these models excel in capturing temporal patterns and subtle variations associated with anemia. The result is a robust classification system that achieves a remarkable 97% accuracy, facilitating early detection and tailored interventions. By combining real-time data collection with sophisticated machine learning and deep learning models, the proposed system represents a significant advancement in addressing the challenges posed by anemia. This holistic and personalized approach not only ensures timely intervention but also contributes to improved healthcare outcomes for individuals affected by this widespread condition. © 2024 IEEE.

关键词： Recurrent neural networks

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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Smart Contract Based Carpooling Application for Secure and Efficient Ride Sharing 3

Smart Contract Based Carpooling Application for Secure and E...

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3rd IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2023

作者： Ramani Bai, V. Sudhir, Pranav Joshi, Rashmi Nair, Vighnesh Anilkumar, Vishnu Vidya Academy of Science and Technology Department of Computer Science Engineering Kerala Thrissur India

ISBN: (纸本)9798350341676

Carpooling offers an effective solution to mitigate pollutant emissions and alleviate traffic congestion. This paper presents a novel system designed to address the shortcomings of centralized ride-sharing services by leveraging smart contracts. By ensuring data security, confidentiality, and privacy, the proposed system initiates the decentralization process for carpooling systems. Users can assume the role of either a driver or a passenger, enabling them to publish rides or search for and book desired rides. Smart contracts facilitate seamless execution of user requests, triggered by events such as ride creation or booking. The fare calculation is based on the passenger's route distance, current fuel price, and the driver's car mileage, resulting in significantly reduced costs. Upon booking a ride, the fare amount is debited from the passenger and credited to the driver only upon the passenger reaching their destination. The system's overarching objective is to simplify people's lives by minimizing travel expenses and traffic congestion, all while upholding user security. © 2023 IEEE.

关键词： Smart contract

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Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

Unveiling Themes in Judicial Proceedings: A Cross-Country St...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Didwania, Krish Toshniwal, Durga Agarwal, Amit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Indian Institute of Technology Roorkee India

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today’s world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and BerTopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years. © 2024 Copyright for this paper by its authors.

关键词： Non-negative matrix factorization

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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