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Disruptive Technologies (ICDT), International Conference on

作者： P Prabakaran R. Jayasudha Mukta Sandhu Sandeep Gupta Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Palwal Haryana India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun India

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches.

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Spectroscopy characterization of quantum modes in an on-chip squeezed microcomb

Spectroscopy characterization of quantum modes in an on-chip...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Jahanbozorgi, Mandana Yang, Zijiao Parnell, Emily A. Jeong, Dongin Sun, Shuman Pfister, Olivier Lee, Hansuek Yi, Xu University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States University of Virginia Department of Physics CharlottesvilleVA22904 United States Graduate School of Nanoscience and Technology Daejeon34141 Korea Republic of Korea Advanced Institute of Science and Technology Department of Physics Daejeon34141 Korea Republic of

We characterized the spectrum of 40 quantum modes in an on-chip squeezed microcomb. A theoretical model is developed to explain how cavity dispersion affects the squeezing and the frequency equidistance of these quant...

ISBN: (纸本)9781957171180

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Deep Generative Adversarial Network for Occlusion Removal from a Single Image

arXiv

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arXiv 2024年

作者： Jonna, Sanakaraganesh Medhi, Moushumi Sahay, Rajiv Ranjan The department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India The Advanced Technology Development Center Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering Indian Institute of Technology Kharagpur721302 India

Nowadays, the enhanced capabilities of in-expensive imaging devices have led to a tremendous increase in the acquisition and sharing of multimedia content over the Internet. Despite advances in imaging sensor technology, annoying conditions like occlusions hamper photography and may deteriorate the performance of applications such as surveillance, detection, and recognition. Occlusion segmentation is difficult because of scale variations, illumination changes, and so on. Similarly, recovering a scene from foreground occlusions also poses significant challenges due to the complexity of accurately estimating the occluded regions and maintaining coherence with the surrounding context. In particular, image de-fencing presents its own set of challenges because of the diverse variations in shape, texture, color, patterns, and the often cluttered environment. This study focuses on the automatic detection and removal of occlusions from a single image. We propose a fully automatic, two-stage convolutional neural network for fence segmentation and occlusion completion. We leverage generative adversarial networks (GANs) to synthesize realistic content, including both structure and texture, in a single shot for inpainting. To assess zero-shot generalization, we evaluated our trained occlusion detection model on our proposed fence-like occlusion segmentation dataset. The dataset can be found on GitHub. © 2024, CC BY.

关键词： Generative adversarial networks

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mingxian Li Hao Sun Yingtie Lei Xiaofeng Zhang Yihang Dong Yilin Zhou Zimeng Li Xuhang Chen School of Computer Science and Engineering Huizhou University Department of Computer Science and Engineering The Ohio State University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shenzhen Institute of Advanced Technology Chinese Academy of Sciences School of Electronic and Communication Engineering Shenzhen Polytechnic University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research.

关键词： Training Deep learning computer vision Text analysis Attention mechanisms Semantics Memory architecture Benchmark testing Transformers Image restoration

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Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

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Acta Pharmaceutica Sinica B 2024年第2期14卷 623-634页

作者： Jiacheng Xiong Rongrong Cui Zhaojun Li Wei Zhang Runze Zhang Zunyun Fu Xiaohong Liu Zhenghao Li Kaixian Chen Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215000China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China State Key Laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other ***-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic *** models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism *** this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated *** integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both *** significantly outperformed the benchmark methods in both cross-validation and external *** AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro ***,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://***.

关键词： Drugmetabolism Aldehyde oxidase Transferlearning Graph neural network Kinase inhibitor

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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

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Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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Preventing Order Inversion within Function-as-a-Service for Sensor Networks

Preventing Order Inversion within Function-as-a-Service for ...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Takashi Ikegami Ichiro Satoh Tatsuo Nakajima SOKENDAI / National Institute of Informatics The Graduate University for Advanced Studies Tokyo Japan National Institute of Informatics Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Server-less architectures, specifically Function-as-a-Service (FaaS), are sometimes used for data processing in sensor networks. However, due to fluctuations in the start and completion times of FaaS tasks, the order can get reversed even when tasks are required to execute in sequence. As long as the start and end times of FaaS tasks are treated as delay fluctuated, it is not feasible to execute data processing as FaaS tasks. Therefore, this paper aims to clarify and formalize the conditions under which the execution order of periodic tasks by FaaS is maintained, assuming that fluctuations in the start and completion times of FaaS tasks can be predicted within a certain range. Furthermore, it presents a method to minimize the impact on data analysis when the completion of FaaS tasks is delayed beyond that predicted range. It is the first challenge that can provide the direction for future research on addressing the fluctuations.

关键词： Fluctuations Data analysis computer architecture Dynamic scheduling Real-time systems Delays Resource management

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback 41

Position: Social Choice Should Guide AI Alignment in Dealing...

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41st International Conference on Machine Learning, ICML 2024

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI United States Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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