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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Hudnurkar, Manoj Singh, Kartik Ambekar, Suhas Sahu, Geeta Yecho, Harshil Pune India Department of Information Technology And Data Science Mumbai India

ISBN: (纸本)9798350365092

The spread of the Metaverse has created new moral and legal challenges, especially when it comes to protecting against fraud. This study explores the legal complexities surrounding Ethereum-specific metaverse transactions and provides guidance on how to identify and address fraudulent activity. The focus of this investigation is the field of fraud detection in the Ethereum environment. Current methods of detecting fraud, such as Blockchain analysis and machine learning-based algorithms, are carefully examined. Next, a new approach to fraud detection is put out, which is predicated on a collection of seven machine learning algorithms: K-Nearest Neighbours (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, KNN, XGBoost (XGB), and Artificial Neural Network (ANN). The results of this study are carefully outlined, demonstrating the accuracy, recall, F1 score, and precision that each of the previously listed algorithms demonstrated. Moreover, this article lays out possible directions for further research, including the incorporation of group approaches and the investigation of creating characteristics to strengthen fraud detection abilities. This academic paper provides a significant and novel insight into the identification of fraudulent activity in Ethereum transactions, highlighting the potential benefits of using machine learning algorithms for this kind of discernment. © 2024 IEEE.

关键词： Blockchain

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Research on Road Damage Detection Based on an Improved YOLOv8 Model 4

Research on Road Damage Detection Based on an Improved YOLOv...

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4th International Conference on Computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2024

作者： Xiao, Yuhan Wan, Chufeng Lin, Junqing Dong, Shu Gao, Weishun Mei, Liangcai Beijing Institute of Technology Zhuhai Department of Data Science Zhuhai China

ISBN: (纸本)9798350378542

This paper presents the YOLOv8 model for road damage detection, highlighting performance enhancements. YOLOv8 features a novel backbone network, an anchor-free detection head, and new loss functions, ensuring efficient operation across various hardware platforms. A self-attention mechanism is integrated to enhance classification accuracy and robustness. Experimental comparisons indicate that while the attention-augmented YOLOv8 improves precision and recall, the modified version achieves superior overall performance. Analyses using confusion matrices and precision-recall curves demonstrate the enhanced YOLOv8's greater accuracy and stability with complex backgrounds and diverse damage types. The results confirm its superiority in practical applications, showcasing strong performance in road damage detection. © 2024 IEEE.

关键词： Object detection

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Smart Toll System with Theft Detection Using Tesseract 1

Smart Toll System with Theft Detection Using Tesseract

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1st International Conference on Pioneering Developments in Computer science and Digital Technologies, IC2SDT 2024

作者： Devesh Stuti, Nagar ABES Institute of Technology CSE-Data Science Department Ghaziabad India

ISBN: (纸本)9798350365016

All toll booths on highways are manually operated, with an attendant taking the driver's payment. We frequently experience gridlock at the toll booths on congested highways because this procedure can be slow. Time, effort, and human resources will be saved by automating the toll collection process. In this work, we propose a low-cost and effective method for collecting tolls called Smart Toll Collection using Automatic Number Plate Recognition (ANPR) that automatically recognises the vehicle's number plate after applying various pre-processing techniques to the input image of the car. The number plate could then be masked and segmented out either by detecting contours or by using the CNN, after which it could be recognised using Tesseract (OCR). When they pass through the toll plaza, the toll will be deducted from the account of the owner. Additionally, the owner will be informed that the vehicle entered the toll plaza at the specified time. We also assume that the owner keeps an electronic wallet, so that tolls are automatically deducted from the account at the toll booth upon identification of that specific licence plate. The shortcomings of the current manual and semi-automatic RFID system can be resolved by using this method of toll collection. This toll collection procedure will save time, effort, manpower and enhanced security features. © 2024 IEEE.

关键词： Toll collection

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of Computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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Practical operation and theoretical basis of difference-in-difference regression in science of science:The comparative trial on the scientific performance of Nobel laureates versus their coauthors

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Journal of data and Information science 2023年第1期8卷 29-46页

作者： Yurui Huang Chaolin Tian Yifang Ma Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055GuangdongChina

Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics *** method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend *** this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID ***/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID ***,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the ***,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work ***:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the *** a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning *** limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical *** implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics ***/value:This study gains insights into the usage of econometric tools in science of science.

关键词： science of science Bibliometrics Difference-in-Difference CEM PSM Nobel Prize

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The Application of Self-Attention Mechanism in Real-Time Micro-Expression Detection: A Case Study of rt-MESNet 2

The Application of Self-Attention Mechanism in Real-Time Mic...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Xu, Chuhuan Ningbo University of Technology Department of Statistics and Data Science Ningbo China

ISBN: (纸本)9798350360240

Micro-expressions, fleeting and subtle facial expressions, possess significant application potential. However, their brief duration, low intensity, and localized motion pose challenges for traditional detection methods. Recent advancements in deep learning have shown promise in micro-expression detection, yet effectively capturing the spatiotemporal features remains a hurdle. The self-attention mechanism, adept at learning feature dependencies, has demonstrated effectiveness in various visual tasks. This paper provides a scholarly review of applying the self-attention mechanism in real-time micro-expression detection, illustrated by the enhanced model, rt-MESNet, and discusses prospective avenues for future research. © 2024 IEEE.

关键词： Deep learning

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Binding Text, Images, Graphs, and Audio for Music Representation Learning

Binding Text, Images, Graphs, and Audio for Music Representa...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Tabaza, Abdulrahman Quishawi, Omar Yaghi, Abdelrahman Qawasmeh, Omar Data Science Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798400716928

In the field of Information Retrieval and Natural Language Processing, text embeddings play a significant role in tasks such as classification, clustering, and topic modeling. However, extending these embeddings to abstract concepts such as music, which involves multiple modalities, presents a unique challenge. Our work addresses this challenge by integrating rich multi-modal data into a unified joint embedding space. This space includes: (1) textual, (2) visual, (3) acoustic, and (4) graph-based modality features. By doing so, we mirror cognitive processes associated with music interaction and overcome the disjoint nature of individual modalities. The resulting joint low-dimensional vector space facilitates retrieval, clustering, embedding space arithmetic, and cross-modal retrieval tasks. Importantly, our approach carries implications for music information retrieval and recommendation systems. Furthermore, we propose a novel multi-modal model that integrates various data types - text, images, graphs, and audio - for music representation learning. Our model aims to capture the complex relationships between different modalities, enhancing the overall understanding of music. By combining textual descriptions, visual imagery, graph-based structures, and audio signals, we create a comprehensive representation that can be leveraged for a wide range of music-related tasks. Notably, our model demonstrates promising results in music classification, and recommendation systems. Code Availability: The source code for the multi-modal music representation model described in this paper is available on GitHub. © 2024 Owner/Author.

关键词： Music

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Predicting Delays for Truck Delivery Logistics: An Application of AI and ML 9

Predicting Delays for Truck Delivery Logistics: An Applicati...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Hudnurkar, Manoj Renji, Kuriakose M. Ambekar, Suhas Sahu, Geeta Joseph, Kavitha Mary Pune India Department of Information Technology And Data Science Mumbai India

ISBN: (纸本)9798350365092

Supply chain management has a lot of logistical challenges, which can significantly affect the efficacy and efficiency of logistics operations as a whole. Machine learning techniques are increasingly popular because they can evaluate vast data volumes and produce precise predictions. Logistics management must include the forecast of delays in the supply chain. It entails anticipating delays in logistics operations, which can assist logistics managers in effectively planning and allocating resources. This paper summarizes the logistics delay prediction in truck delivery systems and identifies some opportunities and problems. The article also suggests a framework for predicting logistics delays based on machine learning methods and assesses the framework's effectiveness using actual data. Thus, the research uses machine learning and AI approaches to anticipate logistics delays. The study gathered information on previous logistical operations and determined the major causes of delays. Then, models for predicting logistical delays were used using machine learning techniques, such as Artificial Neural Networks (ANN), decision trees, random forests, and support vector machines. Our findings demonstrate that these methods have an accuracy rate of up to 97.6% in predicting logistics delays. Also, we discovered that the Adaboost classifier model performed better than the other models in accuracy and precision. Our study offers insightful information on the application of machine learning methods for anticipating logistics delays, which can assist supply chain managers and logistics firms in better organizing and manage their operations to cut down on delays and increase overall effectiveness. © 2024 IEEE.

关键词： Trucks

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B-Statistical Uniform Integrability and (L)p Convergence

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数学研究 2024年第4期57卷 446-459页

作者： Mengru Chen Weicai Peng Zhongzhi Wang School of General Education Wanjiang University of TechnologyMa'anshan 243000China School of Mathematics & Big Data Chaohu UniversityHefei 238000China School of Microelectronics & Data Science AnHui University of TechnologyMa'anshan 243000China

In this paper,we first give the concept of B-statistical uniform integrability with respect to Cesàro matrix(B-CUI),which is weaker than Cesàro uniform *** we establish some B-statistical convergence theorems for random sequence under the condition of B-CUI,which generalizes the outcomes of some known ***,for some special kinds of triangular array or pairwise independent sequences of random variables,similar results are also derived without conditions of B-CUI.

关键词： B-statistical uniform integral Cesàro matrix martingale difference(L)p convergence

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EventSum: A Large-Scale Event-Centric Summarization dataset for Chinese Multi-News Documents 39

EventSum: A Large-Scale Event-Centric Summarization Dataset ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Mengna Zeng, Kaisheng Wang, Mao Xiao, Kaiming Hou, Lei Huang, Hongbin Li, Juanzi Laboratory for Big Data and Decision National University of Defense Technology China Department of Computer Science and Technology Tsinghua University China College of Information and Communication National University of Defense Technology China

ISBN: (纸本)157735897X

In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event is often scattered across multiple documents, involving complex event knowledge understanding and reasoning, which is under-explored in previous work. Therefore, we proposed the Event-Centric Multi-Document Summarization (ECS) task, which aims to generate concise and comprehensive summaries of a given event based on multiple related news documents. Based on this, we constructed the EventSum dataset, which was constructed using Baidu Baike entries and underwent extensive human annotation, to facilitate relevant research. It is the first large-scale Chinese multi-document summarization dataset, containing 5,100 events and a total of 57,984 news documents, with an average of 11.4 input news documents and 13,471 characters per event. To ensure data quality and mitigate potential data leakage, we adopted a multi-stage annotation approach for manually labeling the test set. Given the complexity of event-related information, existing metrics struggle to comprehensively assess the quality of generated summaries. We designed specific metrics including Event Recall, Argument Recall, Causal Recall, and Temporal Recall along with corresponding calculation methods for evaluation. We conducted comprehensive experiments on EventSum to evaluate the performance of advanced long-context Large Language Models (LLMs) on this task. Our experimental results indicate that: 1) The event-centric multi-document summarization task remains challenging for existing long-context LLMs;2) The recall metrics we designed are crucial for evaluating the comprehensiveness of the summary information. © 2025, Association for the Advancement of Artificial Intelligence.

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