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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jeong, Hongbi Son, Junggab Kim, Hyunbum Kang, Kyungtae Hanyang University Dept. Applied Artificial Intelligence Major in Bio Artificial Intelligence Ansan Korea Republic of University of Nevada Las Vegas Dept. Computer Science Las VegasNV89154 United States Incheon National University Dept. Embedded Systems Engineering Incheon Korea Republic of Hanyang University Dept. Applied Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9781665468190

Electrocardiogram (ECG) based user identification has received considerable attention with the advent of wearable devices. It provides emerging applications including personal healthcare a convenient way to authenticate users as the process can be performed at the moment the user makes contact with the device. However, a recent study discovered that injecting noise into the signal transmitted from the user to an application can effectively hinder the classification process. Many efforts have been made to deal with this noise injection attack, but most approaches have focused on noise removal. In contrast, this paper proposes Defensive Adversarial Training (DAT), which involves training a model with various noisy data to enhance the robustness of deep learning-based identification algorithms. We used two types of noise, Gaussian and Laplacian, to create noisy data. In addition, a sliding-window technique was used to effectively extract useful features and to achieve better accuracy. Our simulation results demonstrate that the proposed approach is highly robust to noise injection attacks and even against random noise. A comparative analysis with noise removal schemes also shows that the proposed DAT significantly enhances the robustness of ECG-based user identification. © 2022 IEEE.

关键词： Electrocardiograms

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Current-induced second harmonic generation of Dirac or Weyl semimetals in a strong magnetic field

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Physical Review B 2021年第4期103卷 L041301-L041301页

作者： Yang Gao Furu Zhang College of Computer Science and Engineering Anhui University of Science and Technology Huainan 232001 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

We theoretically investigate the second harmonic generation (SHG) of Dirac or Weyl semimetals under parallel DC electric and strong magnetic fields using the Boltzmann equation approach. The DC current-induced SHG process originates from the optical intraband transitions of the Landau subbands. It is a remarkable fingerprint of three-dimensional Dirac or Weyl dispersions and absent in other materials. An analytical formula for the SHG tensor is derived, and it shows chirality independence. The second-order nonlinear optical susceptibility is proportional to the DC field, giving strong optical nonlinearity up to 105pm/V with THz light, which is several orders larger than that of the usual materials. More interesting, the second harmonic conductivity is found to exhibit periodicity in magnetic field 1/B oscillations, which are similar to the Shubnikov–de Haas oscillations. Thus our work proposes another approach for SHG and potential applications of Dirac or Weyl semimetals in nonlinear optics.

关键词： Landau levels Nonlinear optical susceptibility Photoconductivity Semimetals Boltzmann theory Optical second-harmonic generation

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Overall performance indicators for sustainability assessment and management in mining industry 7

Overall performance indicators for sustainability assessment...

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7th International Conference on Optimization and Applications, ICOA 2021

作者： Lamjahdi, Aicha Bouloiz, Hafida Gallab, Maryam Ibn Zohr University Laboratory of Systems Engineering and Decision-making Support National School of Applied Sciences Agadir Morocco ENSMR MIS - LISTD Laboratory Department of Computer Science Rabat Morocco

ISBN: (纸本)9781665441032

The mining industry is the source of the production of many consumer goods and equipment. Therefore, the companies that control this activity play a significant role in the global economy. However, it is an important source of pollution of water, air, soil and ecosystems by metals. It exploits fossil and non-renewable resources, requiring large amounts of energy and sometimes water. Thus, implementing a sustainable approach through an overall performance integration in manufacturing practices, is become a necessity in this sector. Hence, this research presents a collection of key performance indicators for overall performance evaluation and management in mining industry. The proposed indicators were established from literature on the basis of the sustainability triple bottom line of economic, social, and environmental factors. A survey is conducted then, in a Moroccan mining industry to validate the adaptability of the chosen indicators with the industry activities and practices. Future work will further develop an evaluation tool that will help managers to assess the overall performance in mining industry and to take the appropriate actions to increase its competitiveness. © 2021 IEEE.

关键词： Decision making

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A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classifications

A Parameterized Generative Adversarial Network Using Cyclic ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiangyu Xiong Yue Sun Xiaohong Liu Chan-Tong Lam Tong Tong Hao Chen Qinquan Gao Wei Ke Tao Tan Faculty of Applied Sciences Macao Polytechnic University John Hopcroft Center (JHC) for Computer Science Shanghai Jiao Tong University College of Physics and Information Engineering Fuzhou University Department of Mathware Jiangsu JITRI Sioux Technologies Co. Ltd

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

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A Vision Transformer Model for Violence Detection from Real-Time Videos 21

A Vision Transformer Model for Violence Detection from Real-...

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Proceedings of the 5th International Conference on Future Networks and Distributed Systems

作者： Arfin Shagufta Mohammad Tarique Hesham Sarfaraz Masood Ahmed Abd El-latif Department of Applied Sc. and Humanities Jamia Millia Islamia India Department of Computer Engineering Jamia Millia Islamia India Department of Mathematics and Computer Science Menoufia University Egypt

ISBN: (纸本)9781450387347

Based on the rising incidences of crime and violence, it has become a matter of general importance that technology may be developed to automatically detect the presence of violence in the surveillance footage. For law enforcement, the detection of violent incidents can play an important role in urban safety. The efficiency of event detectors is usually measured in terms of detection speed, precision, and generality over many types of video inputs in a distinct format. However, various recent studies in this area have either focused on the correctness of the model, its response speed, or both, but have missed on considering the multiple data sources for effective model analysis. The main objective of the work is to propose a real-time violence event identifier based on state-of-the-art deep learning methods. Primarily the proposed model is based on the ViT (Vision Transformer) architecture, while other models like ConvLSTM and VGG16 with LSTM were also explored in this work. Two benchmark datasets viz. the RLVS (Real Life Violence Situations) and the Hockey fight datasets were used in this work for robust training and test analysis of the proposed model. For evaluating the model’s performance various metrics including accuracy, f1-score, precision, and recall were evaluated. The results show that the Vision Transformer-based model outperformed all the explored models with an overall accuracy of 98% and 97% on the RLVS dataset and the Hockey datasets respectively, which are also significantly higher than the recent solutions proposed on these datasets.

关键词： Hockey Fight Dataset

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Multimodal Stress and Boredom Detection via Smartwatch Sensors and Hybrid Deep Residual Network with CBAM

Multimodal Stress and Boredom Detection via Smartwatch Senso...

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IEEE SENSORS

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The prevalence of stress and boredom in contempo-rary culture substantially influences mental health and overall well-being. Identifying activities associated with these emotional states is essential to implement effective treatments and support systems. This research presents a new method for identifying stress and boredom activities using wristwatch sensor data and a hybrid deep learning model called CNN-ResBiGRU-CBAM. This model utilizes convolutional neural networks (CNN s), residual bidirectional gated recurrent units (GRUs), and a convolutional block attention module (CBAM) to acquire knowledge about spatial and temporal characteristics and comprehend intricate patterns in sensor input. The experimental results underscore the practical application of the proposed model in the field of affective computing and mental health monitoring. This model not only outperforms both traditional and state-of-the-art deep learning models in accurately identifying stress and boredom behaviors, but also demonstrates its adaptability in leave-one-subject-out validation. With an impressive accuracy of 99.47 % and an F1-score of 99.44% in 5-fold cross-validation, this model shows promise in acquiring features that are relevant to different individuals, thereby enhancing the field of affective computing and mental health monitoring through the use of wearable devices.

关键词： Deep learning Adaptation models Affective computing Wearable Health Monitoring Systems Computational modeling Mental health Logic gates Convolutional neural networks Monitoring Residual neural networks

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Time and frequency domain low order, low frequency approximation of mechanical systems

arXiv

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arXiv 2024年

作者： Zwart, Hans Veldman, Daniël W.M. Sharifi, Sahar F. the Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede7500 AE Netherlands The Netherlands the Department of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands the Department of Data Science Friedrich-Alexander Universität Erlangen-Nürnberg Bavaria Erlangen91052 Germany the Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede7500 AE Netherlands The Netherlands. She is now with TEMPRESS Radeweg 31 MD Vaassen8171 Netherlands

Control design for linear, time-invariant mechanical systems typically requires an accurate low-order approximation in the low frequency range. For example a series expansion of the transfer function around zero consisting of a mass, velocity, and compliance term. Because computing such a series expansion of the transfer function can be cumbersome, a new method to compute low-order approximations of mechanical systems is developed in this paper. The method does not require an explicit expression for the transfer function, which is not always available for infinite-dimensional systems. The advantages of the proposed method is demonstrated in three *** Codes 93B11, 93C20, 93C05, 93C80, 93C95 © 2024, CC BY.

关键词： Transfer functions

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Challenges of measuring spin Seebeck noise

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Physical Review B 2024年第10期109卷 104429-104429页

作者： Renjie Luo Xuanhan Zhao Tanner J. Legvold Liyang Chen Changjiang Liu Deshun Hong Anand Bhattacharya Douglas Natelson Department of Physics and Astronomy Rice University Houston Texas 77005 USA Applied Physics Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Science Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA

Just as electronic shot noise in driven conductors results from the granularity of charge and the statistical variation in the arrival times of charge carriers, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. Using spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet, we demonstrate the technical challenges inherent in such noise measurements. While there is a small increase in voltage noise in the inverse spin Hall detector at low temperatures associated with adding a magnetic field, the dependence on field orientation implies that this is not due to magnon shot noise. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Further, we show that magnon shot noise detection through the standard inverse spin Hall approach is likely impossible due to geometric factors. Implications for future attempts to measure magnon shot noise are discussed.

关键词： Spin Seebeck effect Spin current Magnetic insulators Noise measurements

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Retrosynthesis Prediction via Search in (Hyper) Graph

arXiv

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arXiv 2024年

作者： Lan, Zixun Hong, Binjie Zhu, Jiajun Zeng, Zuo Liu, Zhenfu Yu, Limin Ma, Fei Department of Applied Mathematics Xi’an Jiaotong-Liverpool University China Department of Computer Science The University of Manchester United Kingdom Electrical and Electronic Engineering Xi’an Jiaotong-Liverpool University China Hours Technology Co. Ltd. Suzhou China

Predicting reactants from a specified core product stands as a fundamental challenge within organic synthesis, termed retrosynthesis prediction. Recently, semi-template-based methods and graph-edits-based methods have achieved good performance in terms of both interpretability and accuracy. However, due to their mechanisms these methods cannot predict complex reactions, e.g., reactions with multiple reaction center or attaching the same leaving group to more than one atom. In this study we propose a semi-template-based method, the Retrosynthesis via Search in (Hyper) Graph (RetroSiG) framework to alleviate these limitations. In the proposed method, we turn the reaction center identification and the leaving group completion tasks as tasks of searching in the product molecular graph and leaving group hypergraph respectively. As a semi-template-based method RetroSiG has several advantages. First, RetroSiG is able to handle the complex reactions mentioned above by its novel search mechanism. Second, RetroSiG naturally exploits the hypergraph to model the implicit dependencies between leaving groups. Third, RetroSiG makes full use of the prior, i.e., one-hop constraint. It reduces the search space and enhances overall performance. Comprehensive experiments demonstrated that RetroSiG achieved competitive results. Furthermore, we conducted experiments to show the capability of RetroSiG in predicting complex reactions. Ablation experiments verified the efficacy of specific elements, such as the one-hop constraint and the leaving group hypergraph. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Semantic Data Representation for Explainable Windows Malware Detection Models

arXiv

引用

arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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