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Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors 1

15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Mueang Phayao Thailand Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Human motion detection based on smartphone sensors has gained popularity for identifying everyday activities and enhancing situational awareness in pervasive and ubiquitous computing research. Modern machine learning and deep learning classifiers have been demonstrated on benchmark datasets to interpret people’s behaviors, including driving activities. While driving, driver behavior recognition may assist in activating accident detection. In this paper, we investigate driving behavior detection using deep learning techniques and smartphone sensors. We proposed the DriveNeXt classifier, which employs convolutional layers to extract spatial information and multi-branch aggregation transformation. This research evaluated the proposed model using a publicly available benchmark dataset that captures four activities: a driver entering/exiting and sitting/standing out of a vehicle. Classifier performance was evaluated using two common HAR indicators (accuracy and F1-score). The recommended DriveNeXt outperforms previous baseline deep learning models with the most fantastic accuracy of 96.95% and the highest F1-score of 96.82%, as shown by many investigations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smartphones

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Object Identification and Localization of Visual Explanation for Weapon Detection

Object Identification and Localization of Visual Explanation...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Yuenyong, Sumeth Wongpatikaseree, Konlakorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation. © 2022 IEEE.

关键词： Convolutional neural networks

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Lung Segmentation Using ResUnet++ Powered by Variational Auto Encoder-Based Enhancement in Chest X-ray images 26th

Lung Segmentation Using ResUnet++ Powered by Variational Aut...

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26th Annual Conference on Medical image Understanding and Analysis, MIUA 2022

作者： Ibrahim, Samar Elgohary, Kareem Higazy, Mahmoud Mohannad, Thanaa Selim, Sahar Elattar, Mustafa Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt Research and Development Division Intixel Co. S.A.E. Cairo Egypt

ISBN: (纸本)9783031120527

X-ray has a huge popularity around the world. This is due to its low cost and easy to access. Most of lung diseases are diagnosed using Chest X-ray (CXR). So, developing computer aided detection (CAD) provided with automatic lung segmentation can improve the efficiency of the detection and support the physicians to make a reliable decision at early stages. But when the input image has image artifacts, then any lung segmentation model will introduce suboptimal lung segmentation results. In this paper, a new approach is proposed to make the lung segmentation model robust and boost the basic models’ performance. This is done through two stages: image enhancement and lung segmentation. The first stage aims to enhance the image quality by using the combination of Variational Autoencoder (VAE) and U-Net. The features of VAE and U-Net are concatenated to decode the enhanced output CXR image. The second stage is segmenting the lung using ResUNet++, which can perform well with a few number of images. Moreover, it combines the advantages of residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP), and attention blocks. The proposed method is trained on JSRT dataset. The proposed approach achieved Dice score of 0.9848, 0.99 and the Jaccard score of 0.9783, 0.984 on test data of NLM-MC and JSRT datasets, respectively, which outperforms results in other state of the art models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide image Classification

arXiv

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arXiv 2024年

作者： Shu, Tong Shi, Jun Sun, Dongdong Jiang, Zhiguo Zheng, Yushan School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Software Hefei University of Technology Hefei230601 China Image Processing Center School of Astronautics Beihang University Beijing102206 China School of Engineering Medicine Beijing Advanced Innovation Center on Biomedical Engineering Beihang University Beijing100191 China

Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution of cancers is a continuous process, the correlations and differences across various stages, anatomical locations and patients should be taken into account. However, recent research mainly focuses on the inner-contextual information in a single WSI, ignoring the correlations between slides. To verify whether introducing the slide inter-correlations can bring improvements to WSI representation learning, we propose a generic WSI analysis pipeline SlideGCD that considers the existing multi-instance learning (MIL) methods as the backbone and forge the WSI classification task as a node classification problem. More specifically, SlideGCD declares a node buffer that stores previous slide embeddings for subsequent extensive slide-based graph construction and conducts graph learning to explore the inter-correlations implied in the slide-based graph. Moreover, we frame the MIL classifier and graph learning into two parallel work-flows and deploy the knowledge distillation to transfer the differentiable information to the graph neural network. The consistent performance boosting, brought by SlideGCD, of four previous state-of-the-art MIL methods is observed on two TCGA benchmark datasets. The code is available at https://***/HFUT-miaLab/SlideGCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots

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ASME Letters in Dynamic Systems and Control 2022年第1期2卷 011009页

作者： Vallachira, Sathish Norrlof, Mikael Orkisz, Michal Butail, Sachit Robotics Service Intelligence Unit ABB Ability Innovation Center Karnataka Bangalore560048 India Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi New Delhi110020 India ABB Robotics Division Vasteras721 68 Sweden ABB Corporate Technology Center Krakow31-038 Poland Department of Mechanical Engineering Northern Illinois University DeKalbIL60115 United States

In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the related efficacy of the information-theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear-induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy-based approach is able to detect the axis of failure more than 80% of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time series analysis used to identify directional dependence among processes. Copyright © 2021 by ASME.

关键词： Industrial robots

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Decentralized Optimal Merging Control for Mixed Traffic with Vehicle Inference

Decentralized Optimal Merging Control for Mixed Traffic with...

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American Control Conference (ACC)

作者： Wei Xiao Christos G. Cassandras Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA Division of Systems Engineering and Center for Information and Systems Engineering Boston University Brookline MA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper addresses the optimal control of vehicles arriving from two curved roads at a merging point where the objective is to jointly minimize the travel time, energy consumption, and passenger discomfort. Unlike prior work where traffic consists entirely of Connected and Automated Vehicles (CAVs), we consider optimal controls for CAVs in mixed traffic including Human-Driven Vehicles (HDVs) behaving according to some car-following model that includes random actions. The control applied to CAVs is based on partial information about the presence and states of HDVs which is inferred from local observations available to the CAVs. The passing order of HDVs at the merging point is determined and assisted by CAVs using a proposed Minimum-Effort Merging Contract (MEMC) that uses Control Barrier Functions (CBFs) to guarantee safety. A coordinator is used to manage both the CAV information and inferred HDV information such that the problem can still be solved in a decentralized way. Our approach first determines an analytically tractable unconstrained optimal solution. We then use the joint Optimal Control and Barrier Function (OCBF) method to obtain a controller which optimally tracks such a solution while also guaranteeing all safety and control constraints, including a safe merging contract between CAVs and HDVs. Simulation examples are included to compare the performance under different CAV penetration rates.

关键词： Energy consumption Rollover Roads Merging Optimal control Safety Contracts

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A Method with Universal Transformer for Multimodal Sentiment Analysis 22

A Method with Universal Transformer for Multimodal Sentiment...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Hao Ai Ying Liu Jie Fang Sheikh Faisal Rashid Center for Image and Information Processing Xi'an University of Posts and Telecommunications China Al-Khwarizmi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Pakistan

ISBN: (纸本)9781450396899

Multimodal sentiment analysis refers to the use of computers to analyze and identify the emotions that people want to express through the extracted multimodal sentiment features, and it plays a significant role in human-computer interaction and financial market prediction. Most existing approaches to multimodal sentiment analysis use contextual information for modeling, and while this modeling approach can effectively capture the contextual connections within modalities, the correlations between modalities are often overlooked, and the correlations between modalities are also critical to the final recognition results in multimodal sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis approach based on the universal transformer, a framework that uses the universal transformer to model the connections between multiple modalities while employing effective feature extraction methods to capture the contextual connections of individual modalities. We evaluated our proposed method on two benchmark datasets for multimodal sentiment analysis, CMU-MOSI and CMU-MOSEI, and the results outperformed other methods of the same type.

关键词： Emotional Features Feature Extraction Multimodal sentiment analysis Universal transformer

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Efficient Distributed Sequence Parallelism for Transformer-based image Segmentation

Efficient Distributed Sequence Parallelism for Transformer-b...

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IS and T International Symposium on Electronic Imaging 2024: High Performance Computing for Imaging 2024

作者： Lyngaas, Isaac Meena, Murali Gopalakrishnan Calabrese, Evan Wahib, Mohamed Chen, Peng Igarashi, Jun Huo, Yuankai Wang, Xiao National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Radiology Duke University DurhamNC United States Riken Center for Computational Science Tokyo Japan National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Computer Science Vanderbilt University NashvilleTN United States

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a Vision Transformer encoder with a convolutional decoder to provide image segmentation mappings. The utility of the distributed sequence parallel approach is especially useful in cases where the tokenized embedding representation of image data are too large to fit into standard computing hardware memory. To demonstrate the performance and characteristics of our models trained in sequence parallel fashion compared to standard models, we evaluate our approach using a 3D MRI brain tumor segmentation dataset. We show that training with a sequence parallel approach can match standard sequential model training in terms of convergence. Furthermore, we show that our sequence parallel approach has the capability to support training of models that would not be possible on standard computing resources. © 2024, Society for Imaging science and technology.

关键词： image segmentation

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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