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arXiv 2024年

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Sakai Osaka599-8531 Japan

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Identification of driving factors of algal growth in the South-to-North Water Diversion Project by Transformer-based deep learning

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Water Biology and Security 2023年第3期2卷 47-56页

作者： Jing Qian Nan Pu Li Qian Xiaobai Xue Yonghong Bi Stefan Norra Institute of Applied Geosciences Karlsruhe Institute of TechnologyKarlsruhe76131Germany Institute of Advanced Computer Science Leiden UniversityLeiden2333CANetherlands Institute of Informatics Ludwig Maximilian University of MunichMunich80538Germany MioTech Research Yingtou Information Technology(Shanghai)LimitedShanghai200120China State Key Laboratory of Freshwater Ecology and Biotechnology Institute of HydrobiologyChinese Academy of SciencesWuhan430072China Institute of Environmental Sciences and Geography Soil Sciences and GeoecologyPotsdam University Potsdam-Golm14476Germany

Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of *** this study,we developed a deep learning-based Transformer model,named Bloomformer-1,for end-to-end identification of the drivers of algal growth without the needing extensive a priori knowledge or prior *** Middle Route of the South-to-North Water Diversion Project(MRP)was used as the study site to demonstrate that Bloomformer-1 exhibited more robust performance(with the highest R^(2),0.80 to 0.94,and the lowest RMSE,0.22–0.43μg/L)compared to four widely used traditional machine learning models,namely extra trees regression(ETR),gradient boosting regression tree(GBRT),support vector regression(SVR),and multiple linear regression(MLR).In addition,Bloomformer-1 had higher interpretability(including higher transferability and understandability)than the four traditional machine learning models,which meant that it was trustworthy and the results could be directly applied to real ***,it was determined that total phosphorus(TP)was the most important driver for the MRP,especially in Henan section of the canal,although total nitrogen(TN)had the highest effect on algal growth in the Hebei *** on these results,phosphorus loading controlling in the whole MRP was proposed as an algal control strategy.

关键词： Algal growth Deep learning Driving factor determination Model interpretability Transformer

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Probing Large Language Models from A Human Behavioral Perspective

arXiv

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arXiv 2023年

作者： Wang, Xintong Li, Xiaoyu Li, Xingshan Biemann, Chris Department of Informatics Universität Hamburg Germany School of Computer Science and Technology Beijing Institute of Technology China Institute of Psychology Chinese Academy of Sciences China

Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, the understanding of their prediction processes and internal mechanisms, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), remains largely unexplored. In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely recognized as meaningful indicators of human reading patterns. Our findings reveal that LLMs exhibit a similar prediction pattern with humans but distinct from that of Shallow Language Models (SLMs). Moreover, with the escalation of LLM layers from the middle layers, the correlation coefficients also increase in FFN and MHSA, indicating that the logits within FFN increasingly encapsulate word semantics suitable for predicting tokens from the vocabulary. © 2023, CC BY.

关键词： Eye tracking

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Smart Road Damage Survey System Using Machine Vision and IoT technology

Smart Road Damage Survey System Using Machine Vision and IoT...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Kusprasapta Mutijarsa Figo Agil Alunjati Fadhil Hidayat Ranna Kurnia Zulkifli Arsyad School of Electrical Engineering and Informatics Bandung Institute of Technology (ITB) Bandung Indonesia Department of Civil Engineering National Institute of Technology Bandung Indonesia Department of Computer and Informatics Engineering Bandung State Polytechnic Bandung Indonesia

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung institute of technology and the Department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively.

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A Hybrid Model of Query Expansion using Word2Vec

A Hybrid Model of Query Expansion using Word2Vec

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2021 IEEE International Conference on technology, Research, and Innovation for Betterment of Society, TRIBES 2021

作者： Shukla, Abhishek Kumar Das, Sujoy Maulana Azad National Institute of Technology Department of Mathematics Bio-informatics and Computer Applications Bhopal India

ISBN: (纸本)9781665433426

Query expansion method is one of the most popular methods to reduce the vocabulary mismatch in Information retrieval tasks. Traditional methods of query expansion that use Pseudo relevance feedback are not much efficient for document retrieval from a large collection of documents. In the proposed method we will try to minimize the vocabulary mismatch using a hybrid method of query expansion using the word embedding technique. The proposed method uses both Word2Vec and a local method to predict the expansion terms. The mean average precision of the proposed method is 0.2992. The proposed model also compares with the original query and BM25 model. © 2021 IEEE.

关键词： Information retrieval

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I See Syscalls by the Seashore: An Anomaly-based IDS for Containers Leveraging Sysdig Data

I See Syscalls by the Seashore: An Anomaly-based IDS for Con...

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IEEE Symposium on computers and Communications (ISCC)

作者： Anderson Frasão Tiago Heinrich Vinicius Fulber-Garcia Newton C. Will Rafael R. Obelheiro Carlos A. Maziero Informatics Department Federal University of Paraná Paraná Brazil Max Planck Institute for Informatics Saarbrücken Germany Computer Science Department Federal University of Technology Paraná Brazil Computer Science Department State University of Santa Catarina Joinville Brazil

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Intrusion detection in virtualized environments is vital due to the widespread adoption of virtualization technology. A common strategy for achieving this task involves collecting data from the virtual environment and providing it to intrusion detection solutions. However, these solutions can be affected by other elements present in the virtual environment. An approach that has gained prominence is applying machine learning (ML) models to perform anomaly-based intrusion detection based on system call traces. In Linux-based environments, many tools can be used for collecting the system calls issued by processes and containers; two of the most popular are strace and sysdig. This paper introduces a dataset of system call traces collected with sysdig with a focus on anomaly-based intrusion detection for containerized applications and uses this dataset to compare the effectiveness of strace and sysdig data and evaluate the performance of five different ML models for anomaly detection. The results reveal that sysdig is an attractive option, enabling the collection of system call traces with lower overhead than strace while achieving good detection performance with several ML models.

关键词： computers Intrusion detection Machine learning Containers Data models Risk management Proposals Virtualization Monitoring Anomaly detection

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Padma Nyoman Crisnapati I Made Darma Susila I Komang Agus Ady Aryanto Jaturapith Krohkaew Ricky Aurelius Nurtanto Diaz I Made Suandana Astika Pande Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective.

关键词： Fuzzy logic PI control Microcontrollers 2-DOF Manuals Manipulators Three-dimensional printing PD control Root mean square Tuning

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning 3

Predictive Design for Quality Assessment Employing Cloud Com...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jindal, Gaurav Tiwari, Vidhika Mahomad, Riyaz Gehlot, Anita Jindal, Mimoh Bordoloi, Dibyhash G L Bajaj College of Technology and Management Department of Master of Computer Applications Gr. Noida India DPGITM Mechanical Engineering Department Gurugram India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India National Informatics Centre Delhi India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure. © 2023 IEEE.

关键词： Cloud computing

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Benchmarking changepoint detection algorithms on cardiac time series

arXiv

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arXiv 2024年

作者： Cakmak, Ayse Selen Reinertsen, Erik Nemati, Shamim Clifford, Gari D. Department of Biomedical Informatics Emory University United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics University of California at San Diego United States

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detec- tion algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance, noise, and abnormal conduction (ectopy) on realistic artificial cardiovascular time series data. All algorithms were applied to real data (cardiac time series of 22 patients with REM-behavior disorder (RBD) and 15 healthy controls) using the parameters selected on artificial data. Finally, features were derived from the de- tected changepoints to classify RBD patients from healthy controls using a K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint Detection al- gorithm provided superior positive predictive value for state change identification while Recursive Mean Difference Maximization (RMDM) achieved the highest true positive rate. For the classification task, features derived from the RMDM algorithm provided the highest leave one out cross validated accuracy of 0.89 and true positive rate of 0.87. Automatically detected changepoints provide useful information about subject's phys- iological state which cannot be directly observed. However, the choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task. This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application *** Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10 © 2024, CC BY-NC-SA.

关键词： Time series

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Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach

arXiv

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arXiv 2024年

作者： Siddhad, Gourav Iwamura, Masakazu Roy, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Sakai Osaka599-8531 Japan

Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-computer Interface (BCI) domain presents significant challenges, particularly the need for extensive datasets. This study aims to generate synthetic EEG samples that are similar to real samples but are distinct by augmenting noise to a conditional denoising diffusion probabilistic model, thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the DEAP dataset, showcasing upto 4.21% improvement in classification performance when using synthetic data. This is higher compared to the traditional GAN-based and DDPM-based approaches. The proposed diffusion-based approach for EEG data generation appears promising in refining the accuracy of emotion recognition systems and marks a notable contribution to EEG-based emotion recognition. Our research further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels. © 2024, CC BY.

关键词： Emotion Recognition

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