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6th International Conference on Machine Learning and Intelligent Systems, MLIS 2024

作者： Ye, Jiahong Ye, Feiyue School of Data Science The Chinese University of HongKong Shenzhen China School of Computer Engineering Jiangsu University of technology China

ISBN: (纸本)9781643685533

Addressing the anti-counterfeiting and verification issues of handwritten signatures, a traceable offline handwritten signature anti-counterfeiting and verification system is proposed. The system consists of four parts: a front-end mobile terminal application system, a database storage system, a backend management system, and a portable dynamic encoding stamp device. The system can achieve traceability of signatures, thereby more effectively preventing counterfeit signatures. The front-end mobile terminal application system can generate a unique two-dimensional code based on the relevant information of the signature file and send it to the database system for storage, while also sending it to the portable dynamic encoding stamp device. The portable dynamic encoding stamp device can receive the dynamic two-dimensional code from the front-end mobile terminal application system and produce a two-dimensional code imprint. The database system can store information such as signature file details, corresponding QR codes, and signature photos, which can be used for traceability queries and verification. The proposed system utilizes the SHA algorithm to generate unique codes and can generate corresponding QR code prints on a portable dynamic encoding device. When signing, a QR code seal is affixed, and during signature verification, the QR code on the signature file can be compared with the information in the database to verify the authenticity of the signature. When needed, signature photos stored in the database can also be used for further verification through machine learning. Compared with traditional signature verification methods, this method has better effectiveness. © 2024 The Authors.

关键词： database systems

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Multi-Modal Self-Perception Enhanced Large Language Model for 3D Region-of-Interest Captioning With Limited data

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IEEE Transactions on Multimedia 2025年 27卷 2935-2948页

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation School of Computer Science and Technology Visual Intellgence + X International Cooperation Joint Laboratory of MOE Beijing100044 China Central South University School of Computer Science and Engineering Changsha410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer Science and Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

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Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks 7

Enhancing Road Safety: An Intelligent Drowsiness Detection S...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nalavade, Jagannath E. Sachdeo, Rajneeshkaur Kale, Dattatray R. Buchade, Amar Subhedar, Mansi Shinde, Swapnil K. Mit Art Design and Technology University Computer Science and Engineering Pune India BRACT's Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Pillai Hoc College of Engineering and Technology Electronics and Computer Science Rasayani India

ISBN: (纸本)9798331527822

The aim of this research is to create an intelligent vehicle system that can effectively mitigate the risks associated with drowsy driving, thereby preventing road accidents. Various factors like driver exhaustion, challenging road conditions, and adverse weather contribute to motor vehicle crashes, leading to over 1.35 million deaths annually, as estimated by entities such as the World Health Organization (WHO). To achieve this, the development of a robust warning system is crucial. In this study, we proposed an alert system to detect drowsy drivers. It utilizes a video stream process (VSP) to analyze the gaze's eye-aspect ratio (EAR) and geometric distance through an innate reflex model. Additionally, a facial landmark-based algorithm is employed for accurate eye recognition. When driver drowsiness is detected, the system issues a warning message, along with crash impact and location information, and activates the voice surveillance system. The suggested technique establishes a non-invasive approach for evaluating driver weariness in connection with accident severity. The camera, the fundamental element of this system, continuously acquires facial landmarks recognized by designated locations and analyzes them. Drivers who are distracted and have poor lighting may show signs of sleepiness. © 2024 IEEE.

关键词： Deep neural networks

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Multi-objective evolutionary optimization for hardware-aware neural network pruning

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Fundamental Research 2024年第4期4卷 941-950页

作者： Wenjing Hong Guiying Li Shengcai Liu Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-Inspired Inteligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.

关键词： Multi-objective optimization Evolutionary algorithm Neural network pruning Hardware-awaremachine learning Hardware efficiency

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SafeRoute: Pioneering Online Ride Booking with Pega & ML-Powered Safety Measures 8

SafeRoute: Pioneering Online Ride Booking with Pega & ML-Pow...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Rajalakshmi, D. Immaculate, P.S. Pandi, S. Senthil Pavitra, A. Rehash Rushmi Vijayalakshmi, V. Sharanya, S. Srm Institute of Science and Technology Data Science and Business Systems Chennai India Sri Sai Ram Institute of Technology Chennai Master of Business Administration India Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The advent of online ride booking services has revolutionized the transportation industry, providing users with convenient and efficient means of commuting. However, ensuring the safety of passengers and drivers remains a paramount concern. This project introduces an innovative solution that leverages Pega, a leading business process management platform, coupled with machine learning algorithms to enhance the safety measures within an online ride booking service. The proposed system integrates seamlessly into the existing online ride booking infrastructure and introduces advanced safety mechanisms based on realtime data analysis. Machine learning models are employed to predict and mitigate potential risks, ensuring a secure and reliable experience for both passengers and drivers. © 2024 IEEE.

关键词： Reservation systems

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Neural Machine Translation Models with Attention-Based Dropout Layer

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computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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Pairwise Alignment Improves Graph Domain Adaptation 41

Pairwise Alignment Improves Graph Domain Adaptation

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Shikun Zou, Deyu Zhao, Han Li, Pan Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States School of Data Science University of Science and Technology of China Hefei China Department of Computer Science University of Illinois Urbana-Champaign Champaign United States

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets. Copyright 2024 by the author(s)

关键词： Graph theory

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HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES 12

HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Zihan Lee, Jason D. Chen, Yuxin Du, Simon S. Department of Electrical and Computer Engineering Princeton University United States Department of Statistics and Data Science University of Pennsylvania United States Paul G. Allen School of Computer Science and Engineering University of Washington United States

A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a.k.a. the horizon-free bounds. However, these regret bounds only apply to settings where a polynomial dependency on the size of transition model is allowed, such as tabular Markov Decision Process (MDP) and linear mixture MDP. We give the first horizon-free bound for the popular linear MDP setting where the size of the transition model can be exponentially large or even uncountable. In contrast to prior works which explicitly estimate the transition model and compute the inhomogeneous value functions at different time steps, we directly estimate the value functions and confidence sets. We obtain the horizon-free bound by: (1) maintaining multiple weighted least square estimators for the value functions;and (2) a structural lemma which shows the maximal total variation of the inhomogeneous value functions is bounded by a polynomial factor of the feature dimension. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

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Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

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computers, Materials & Continua 2023年第12期77卷 3077-3094页

作者： Manas Ranjan Prusty Rishi Dinesh Hariket Sukesh Kumar Sheth Alapati Lakshmi Viswanath Sandeep Kumar Satapathy Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Advanced Data Science Vellore Institute of TechnologyChennai600127India

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping *** this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation ***,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain *** proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ *** findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

关键词： Nuclei segmentation image segmentation ensemble U-Net deep learning histopathology image convolutional neural networks

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 IEEE.

关键词： Image denoising

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