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arXiv 2024年

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Liu, Jun Gao, Jerry Department of Applied Data Science San José State University San José United States Dept. of ISE Virginia Tech Blacksburg United States Dept. of Computer Engineering San José State University San José United States

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024, CC BY.

关键词： Adversarial machine learning

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Ethereum Blockchain-Based Design and Implementation of Electronic Records for Home Care

Ethereum Blockchain-Based Design and Implementation of Elect...

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Networking and Communications (ICNWC), International Conference on

作者： M. Jahir Pasha K. K. Baseer N. Vasavi K. Sreenivasulu Syed Mohammed Nadeem A. Chandra Obula Reddy Dept. of CSE(DS) Rajeev Gandhi Memorial College of Engineering and Technology Nandyal Andhra Pradesh India Gitam School of Technology Gitam (Deemed to be University) Bengaluru Karnataka India Dept. of CSE Ashoka Womens Engineering College Kurnool India Dept. of CSE G. Pullaiah College of Engineering and Technology Kurnool India Dept. of CSE-Data Science G. Pulla Reddy Engineering College(Autonomous) Kurnool India Dept. of CSE (AI&ML) Sri Venkateswara College of Engineering Kadapa India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Blockchain's distributed and decentralized ledger, which conducts transactions over multiple computers and is digitally encrypted, will play a significant role in the future. It is meant to be transparent, impervious to tampering, and censorship-resistant. Building safe blocks joined by a chain containing transactions and a hash value is the fundamental idea underlying blockchain. Cryptographic hashes are used to join these blocks together to create an unchangeable, continuous chain. Many elements of the Ethereum blockchain can be useful when utilizing smart contracts. Thus, we make use of these smart contracts along with the many blockchain components that would be highly beneficial on the Ethereum network, and we thoroughly examine the transaction flow. Beyond only cryptocurrencies, Ethereum has been essential in the advancement of blockchain technology, opening the door for a wide range of decentralized projects and applications. In homecare, the low-secured records are utilized for family member and doctor consultations as well as the storage of diagnosis information. The majority of hospitals utilize email to share critical medical details due to its lower security and reliance on data. Most people are most familiar with blockchain technology as the foundation of cryptocurrencies like Bitcoin, where it serves as a safe and decentralized ledger for recording transactions. Its possibilities, however, go beyond cryptocurrency to a variety of other areas, such as banking and healthcare, where it might enable new kinds of decentralized apps and offer transparent and safe record-keeping. A blockchain system is an application code for a decentralized blockchain platform that runs and validates securely via a block-to-block network. Keeping a lot of data on the blockchain costs a lot of money, and exchanging health records is one issue that needs to be addressed. The recommended method for resolving this issue employs the Interplanetary File System (IPFS) to save costs

关键词： Computers Costs Codes Hospitals Smart contracts Decentralized applications Blockchains

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Zhang, Shuai Liu, Sijia Chen, Pin-Yu Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Dept. Data Science New Jersey Institute of Technology NewarkNJ United States Dept. Computer Science and Engineering Michigan State University East LansingMI United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which depends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Can We Estimate a Useful Range of Prices for Popular Models of Used Cars Using Past data?

Can We Estimate a Useful Range of Prices for Popular Models ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Md Shoaib Bhuiyan Dept. Health Data Science Faculty of Medical Engineering Suzuka University of Medical Science Suzuka Mie Japan

ISBN: (纸本)9781665492331

There are some models of cars that remain popular with the consumers, even if pre-used. However, they sell at a wide variety of prices. We built a dataset of past prices for two popular Toyota SUVs, Harrier and RAV4 using the final selling price obtained from a large Japanese auction house, USS. We then tried to predict a useful range of prices for each of these two models of pre-used Toyota SUVs, based on the parameters of that particular car like how many kilometers has it already travelled, whether the powertrain is hybrid or runs on gasoline only, which year was it originally built in, 2019 or in 2020 or later, etc. We also tried to find out whether options like having Sunroof/moonroof had any effect on the final bid of auction prices. Feature Selection and Feature Transformation were done using the Classification Learner App of the MATLAB Statistics and Machine Learning Toolbox version R2020b. Our analysis shows that we can establish a useful range of prices.

关键词： Machine learning Mechanical power transmission Predictive models Feature extraction data models Automobiles Petroleum

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Energy-Efficient-Enabled Edge-AI-IoT Integrated Traffic Incident Analysis and Avoidance of Secondary Incidents

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IEEE Internet of Things Journal 2025年

作者： Chavhan, Suresh Deepika, Illa Sai Gupta, Deepak Rodrigues, Joel J P C Indian Institute of Science Education and Research Thiruvananthapuram School of Data Sciences India Indian Institute of Management Mumbai India Maharaja Agrasen Institute of Technology Dept. of Computer Science and Engineering Delhi India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Amazonas State University AM Manaus Brazil

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment. © 2025 IEEE.

关键词： Traffic congestion

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Disentangling Voice and Content with Self-Supervision for Speaker Recognition

arXiv

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arXiv 2023年

作者： Liu, Tianchi Lee, Kong Aik Wang, Qiongqiong Li, Haizhou Singapore Dept. of Electrical and Computer Engineering National University of Singapore Singapore Dept. of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen China

For speaker recognition, it is difficult to extract an accurate speaker representation from speech because of its mixture of speaker traits and content. This paper proposes a disentanglement framework that simultaneously models speaker traits and content variability in speech. It is realized with the use of three Gaussian inference layers, each consisting of a learnable transition model that extracts distinct speech components. Notably, a strengthened transition model is specifically designed to model complex speech dynamics. We also propose a self-supervision method to dynamically disentangle content without the use of labels other than speaker identities. The efficacy of the proposed framework is validated via experiments conducted on the VoxCeleb and SITW datasets with 9.56% and 8.24% average reductions in EER and minDCF, respectively. Since neither additional model training nor data is specifically needed, it is easily applicable in practical use. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Introducing Grid WAR: Rethinking WAR for Starting Pitchers

arXiv

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arXiv 2022年

作者： Brill, Ryan S. Wyner, Abraham J. Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Dept. of Statistics and Data Science The Wharton School University of Pennsylvania United States

The baseball statistic "Wins Above Replacement" (WAR) has emerged as one of the most popular evaluation metrics. But it is not readily observed and tabulated;WAR is an estimate of a parameter in a vaguely defined model with all its attendant assumptions. Industry-standard models of WAR for starting pitchers from FanGraphs and Baseball Reference all assume that season-long averages are sufficient statistics for a pitcher’s performance. This provides an invalid mathematical foundation for many reasons, especially because WAR should not be linear with respect to any counting statistic. To repair this defect, as well as many others, we devise a new measure, Grid WAR, which accurately estimates a starting pitcher’s WAR on a per-game basis. The convexity of Grid WAR diminishes the impact of "blow-up" games and up-weights exceptional games, raising the valuation of pitchers like Sandy Koufax, Whitey Ford, and Catfish Hunter who exhibit fundamental game-by-game variance. Although Grid WAR is designed to accurately measure historical performance, it has predictive value insofar as a pitcher’s Grid WAR is better than Fangraphs’ FIP WAR at predicting future performance. Finally, at https://*** we host a Shiny app which displays the Grid WAR results of each MLB game since 1952, including career, season, and game level results, which updates automatically every morning. Copyright © 2022, The Authors. All rights reserved.

关键词： Baseball

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Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries

Machine Learning Challenges of E-government Models of Cloud ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： R Tamilarasi P N Santhosh Kumar G S Pradeep Ghantasala D Nageswara Rao Priyanka Bathla Gaurav Gupta Sonia Department of Computer Science and Engineering Alliance University Bengaluru India Dept. of CSE (Data Science) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering Alliance University Bangalore India Chandigarh University Punjab India Shoolini University Solan India Yogananda School of AI Computer and Data Sciences Shoolini University Solan India

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services.

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Generated Compressed Domain Images to the Rescue: Cross Distillation from Compressed Domain to Pixel Domain

Generated Compressed Domain Images to the Rescue: Cross Dist...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Reyhan Kevser Keser Muhammet Sebul Beratoğlu Behçet Uğur Töreyin Signal Processing for Computational Intelligence Research Group (SP4CING) Informatics Institute İstanbul Technical University İstanbul Türkiye Dept. of Artificial Intelligence and Data Engineering Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

data are the essential component in the pipeline of training a model that determines the performance of the model. However, there may not be enough data that meet the requirements of some tasks. In this paper, we introduce a knowledge distillation-based approach that mitigates the disadvantages of data scarcity. Specifically, we propose a method that boosts the pixel domain performance of a model, by utilizing compressed domain knowledge via cross distillation between these two modalities. To evaluate our approach, we conduct experiments on two computer vision tasks which are object detection and recognition. Results indicate that compressed domain features can be utilized for a task in the pixel domain via our approach, where data are scarce or not completely available due to privacy or copyright issues.

关键词： Training data privacy Computer vision Image coding computational modeling Pipelines Object detection

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