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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Ding, Yulian Guo, Yan Jiang, Limin Tang, Jijun Southern University of Science and Technology College of Engineering Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Miami Department of Public Health Sciences MiamiFL33136 United States

ISBN: (纸本)9798350386226

The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations. © 2024 IEEE.

关键词： Contrastive Learning

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TSC18 Convolutional Neural Network for Traffic Sign Classification

TSC18 Convolutional Neural Network for Traffic Sign Classifi...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Adil Hussain Kashif Naseer Qureshi Ayesha Aslam Tariq Tariq Muhammad Rabiu Abdullahi School of Electronics and Control Engineering Chang’an University Xi’An China Department of Electronic and Computer Engineering University of Limerick Limerick Ireland School of Information Engineering Chang’an University Xi’An China

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The classification of traffic signs holds significant importance in the realm of autonomous vehicles. The primary objective of our research is to effectively perform this classification to mitigate the occurrence of accidents and enhance the overall reliability of autonomous vehicles. This research proposed a TSC18 CNN model, using 18 layers only for the Traffic Sign Classification and performing the evaluation using the pre-trained Convolutional Neural Network (CNN) models. The TSC18 CNN model contains four convolutional layers, three max-pooling layers, one flattening layer, four fully connected layers, and batch normalization and dropout layers. The TSC18 CNN model is trained and validated using the Road Cracks dataset, which contains a traffic image collection of 43 classes. The training set includes 34,799 images that have been labeled, while the validation set contains 4,410 labeled images. The performance of the proposed CNN model is compared with the pre-trained CNN models, including EffecientNet, InceptionNet, and VGG-19, using the same dataset. The training and validation accuracy and losses are presented in graphical form for comparison. Also, the wellknown pre-trained CNN models, including EfficientNet, InceptionNet, and VGG- 19, were tested using the same dataset for the performance evaluation with the proposed model. The TSC18 model exhibited a test data accuracy of 99.21%. The performance of the proposed model is compared with the existing models using the GTSRB dataset. The proposed model performs well as compared to the pre-trained CNN models. The TSC18 CNN model can be used for road crack detection using road images.

关键词： Training Performance evaluation Accuracy Roads Data models Convolutional neural networks Reliability

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Design of a Mechatronic Endoskeleton Robot for Inspection Tasks: Model Reference Adaptive control (MRAC) for Serpentine Locomotion with Stability Analysis

Design of a Mechatronic Endoskeleton Robot for Inspection Ta...

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International Conference on Developments in eSystems engineering (DESE)

作者： Ammar K. Al Mhdawi Nick Wright Mohammed I. Younis Amjad J. Humaidi School of Engineering and Sustainable Development De Montfort University Leicester UK Department of Electrical and Electronic Engineering Newcastle University Newcastle UK Department of Computer Engineering University of Baghdad Iraq Department of Control and Systems Engineering University of Technology Baghdad Iraq

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

An advantage of bio-inspired robots is the versatility of their locomotion on a wide range of terrains that conventional robots are not able to traverse. The snake-like robot, which is a mechanism designed to move in the manner of a biological snake, is an example of a bio-inspired platform. This paper aims to implement and validate, using real and simulation tests on flat terrain, a snake-like endoskeleton robot that permits serpentine movement with adaptive control capabilities. The prototype mechatronic robot was comprised of eleven segments that were 3D printed sequentially. A simulation using Matlab’s Multibody Mechanics tool determines the necessary torques for each motor. In this simulation, a parameterized virtual model of the robot is created, where rectilinear gaits are programmed. In accordance with simulation and experimental results, the robot undertakes different times to reach the goal trajectory or end points depending on the frequency, angular position, and wave length (duty cycle). As a result, there is a high degree of similarity between the simulation tests and those conducted with the prototype endoskeleton. Furthermore, the robot is equipped with machine vision capabilities that allow it to detect faults within oil and gas pipes for the purpose of inspection and maintenance. Furthermore, a Model Reference Adaptive control (MRAC) method based on Lyapunov stability analysis and MIT rules is proposed. The theoretical analysis and numerical simulation show that the designed trajectory tracking control law can make the multi-joint snake-like robot track the trajectory of the front joint when the robot encounters disturbances and stabilize the angular position and velocity of the first two head joints when disturbances occur.

关键词： Adaptation models Analytical models Mechatronics Prototypes Inspection Mathematical models Trajectory Adaptive control MATLAB Robots

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Machine Learning Based Intrusion Detection System in Cloud Environment 8

Machine Learning Based Intrusion Detection System in Cloud E...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Saeed, Muhammad Salman Saurabh, Raman Bhasme, Sarang Balasaheb Nazarov, Alexey N. Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Attacks and intrusions on computer networks often have different characteristics and behaviors that require professional help. The number of attacks is growing in line with the development of computer networks. In fact, expert knowledge is declining over time and should be reviewed and made available in the system, making it necessary to hire an experienced person. Cloud computing is an advancement in IT technologies that provide users with the most up-To-date, highly sought-After virtual services with high flexibility, low infrastructure costs, and minimal maintenance. Protection against network intruders is one of the most important security challenges for cloud computing, as it affects the privacy, availability, and integrity of cloud services. Because cloud computing is a shared environment, it can be vulnerable to various threats. Because building strong access systems are essential to maintaining cloud security, but it remains an obscure goal and a major challenge due to the growing number of cyber-Attacks. In this study, a machine-learning based approach was used to propose an intrusion detection system (IDS) based on a network based cloud computing model. During the previous processing phase, we used a feature selection algorithm from the CICDDoS2019 database to select features. We have used the Naive Bayes Classifier, Decision Tree Classifier, Supporting Classifier, Logistic Regression, and Random Forest Classifier, all of which are well-known categories. Simulation results when compared to these five different classifiers, random forest algorithm show improvements in overall accuracy, precision, recall and F1 score. We explain how various characteristics of machine learning techniques can be used to build efficient IDS. The goal of this research is to develop a new method based on intrusion detection systems and their varied designs for improving intrusion detection accuracy in cloud computing. © 2022 IEEE.

关键词： Decision trees

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A Framework for Analyzing GPU-Executed Malware 23

A Framework for Analyzing GPU-Executed Malware

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23rd International Conference on control Systems and computer Science Technologies, CSCS 2021

作者： Apostol, Teodor-Petrut Velea, Radu Deaconescu, Razvan University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Romania

ISBN: (纸本)9781665439398

Graphics processing units (GPU) are an integral part of today's computing environment. The marketing emphasis on user experience pushes vendors to constantly look for better graphics hardware and newer drivers to integrate into their products. Most commodity GPUs function as accelerators for image and video processing tasks and are present (either as dedicated or integrated form factor) in everyday devices including desktops, laptops, smartphones or other portable gadgets. Recent trends have seen the GPU move to perform additional tasks such as offloading some of the computation from the CPU or solve highly parallel problems. As GPUs become more powerful and widespread they will naturally become a very attractive target for attackers. Cybercriminals will look for any vulnerability in the GPU hardware or software, and will try to use it as an attack vector to compromise the system. This paper presents some of the incidents that involve the use of GPUs to perform malicious actions and proposes a framework to allow analysts to inspect the code executed on the GPU and trigger security alerts. The proposed solution will look at the processes running inside an operating system, it will identify which of them require access to the GPU and will try to intercept and scan for any suspicious activities, similar to a conventional security solution. © 2021 IEEE.

关键词： Graphics processing unit

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Conductivity Profile Reconstruction of Stroke Head Models Based on Divide-and-Conquer Method and the Genetic Algorithm

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IEEE Transactions on Magnetics 2024年第3期60卷 1-4页

作者： Choi, Charles T. M. Yu, Chieh-Cheng Lai, Yan-Hung National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu30010 Taiwan Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

This article used finite element (FE) analysis to study and analyze the electrical conductivity profile of simulated stroke patients based on a 45 dB signal-to-noise ratio synthesized measurement. Clinical measurement data obtained from stroke patients with electrical impedance tomography (EIT) was reported to be approximately 45 dB SNR. Weighting factors in conjunction with the divide-and-conquer method and adaptive genetic algorithm (AGA) were used. The AGA was applied with the weighting factors to identify the conductivity distribution inside the brain by minimizing the difference between the simulation and synthesized measured voltages under stroke conditions. The result shows that it might be possible to reconstruct an approximate electrical conductivity distribution based on 45 dB SNR simulated measurement of stroke data. © 2023 IEEE.

关键词： Finite element method

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Visibility of Long-Fiber Sagnac Interferometers

Visibility of Long-Fiber Sagnac Interferometers

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Conference on Lasers and Electro-Optics (CLEO)

作者： Reem Mandil Li Qian Hoi-Kwong Lo Department of Physics Centre for Quantum Information and Quantum Control University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering Centre for Quantum Information and Quantum Control University of Toronto Toronto Ontario Canada

We measure visibility in fiber Sagnac interferometers up to 200 km using CW light. Results show visibility is backscattering-limited after 60 km. We obtain 90% visibility in 200 km fiber using a burst-patterning techn... 详细信息

ISBN: (纸本)9798350369311

关键词： Measurement by laser beam Sagnac interferometers Electro-optic effects Fiber lasers

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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Mediterranean Conference on control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

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Accelerating Distributed Nash Equilibrium Seeking

Accelerating Distributed Nash Equilibrium Seeking

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European control Conference (ECC)

作者： Tatiana Tatarenko Angelia Nedić Department of Control Theory and Intelligent Systems TU Darmstadt German School of Electrical Computer and Energy Engineering Arizona State University USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [4] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no access to the full information but are able to communicate with neighbors over some communication graph. The convergence rate is demonstrated to be geometric and improves the rates obtained by the previously presented procedures seeking Nash equilibria in the class of games under consideration.

关键词： Extrapolation Europe Games Nash equilibrium Topology Convergence

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Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

Benchmarking Semantic Communications for Image Transmission ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yanhu Wang Shuaishuai Guo Anming Dong Hui Zhao School of Control Science and Engineering Shandong University School of Computer Science and Technology Qilu University of Technology Communication Systems Department EURECOM

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes.

关键词： Wireless communication Training Transmitters Receivers Artificial neural networks Signal processing Semantic communication Transceivers Interference channels Signal to noise ratio

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