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SimBusters: Bridging Simulation Gaps in Intelligent Vehicles Perception

SimBusters: Bridging Simulation Gaps in Intelligent Vehicles...

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IEEE Symposium on Intelligent Vehicle

作者： Alberto Justo Javier Araluce Javier Romera Mario Rodriguez-Arozamena Leonardo González Sergio Díaz TECNALIA Basque Research and Technology Alliance (BRTA) Derio Spain Dept. of Computer Systems Engineering Mondragon University Bilbao Spain

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Recent advances in automated vehicle technology rely heavily on simulated environments for training and testing. However, a significant challenge lies in bridging the gap between simulated and real-world scenarios, as discrepancies between these environments can affect the performance and reliability after that transition, especially in perception. Particularly, LiDAR sensors are highly affected in this matter due to disparities in pointcloud distribution and intensity. Therefore, this paper presents an innovative approach to bridge the gap between simulation and reality. For it, we test and validate a realistic LiDAR library, PCSim, within the CARLA simulator, providing an enhanced simulation environment. Our method involves integrating perception models, pre-trained on real-world datasets, in this environment. Then, we develop a Real2Sim domain adaptation method to transfer these models into the library, leveraging their performance. Finally, we evaluate the 3D object detection models in PCSim LiDARs to prove our *** have assessed this proposal in PCSim, obtaining promising results in mitigating the simulation-reality gap. Our evaluations provide a guidance for future effective transition from virtual environments to real-world applications.

关键词： Training Adaptation models Solid modeling Laser radar Three-dimensional displays Intelligent vehicles Virtual environments

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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Task and Process Capturing Toolkit using GUI Automation 4

Task and Process Capturing Toolkit using GUI Automation

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Perera, Ravindu Lakshan Bellanthudawa, Hiruni Piyumika Hevavitharana, Nipun Dimantha Ariyasinghe, Kavindu Malshan Wickramarathne, Jagath Perera, Jeewaka Sri Lanka Institute of Information Technology Dept.Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665468350

Automation is one of the best ways to make easier everyone's day-to-day life. When considering the industry, if someone could be able to automate the mechanism of solving computer software issues, that will be helpful to every company. Because most companies are facing difficulties when it comes to giving IT support. Some companies are suffering from a lack of IT supporters because they have to solve the same problem on different computers at the same time. This most likely reduces the efficiency of the company and affects its performance as well. Without the proper knowledge of technology, employees tend to get a lot of technical issues while working with the technology. Here comes the opportunity to automate the IT-related problem-solving mechanism. That will save a lot of time and increase the company's efficiency as well. While carrying out the background research we thought, by automating the IT-related problem it will reduce the allocation of human power and that will directly affect company efficiency. The research team came up with the idea of creating a software product that can export an executable file by capturing selected tasks of the user's screen and its' relevant processes. Task and process capturing toolkit is a software product called the 'ClickMe toolkit'. It can capture relevant processes that are happening in any computer which runs on Windows 10 platform, then make a script called 'ClickMe script', and convert it into an executable file. It can be run on any Windows 10 Platform. © 2022 IEEE.

关键词： Graphical user interfaces

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Predictive Control and Production Process Forecasting Under Deterministic Chaos 23

Predictive Control and Production Process Forecasting Under ...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Manusov, Vadim Z. Orlov, Dmitry V. Karmanov, Vitaly S. Khusnutdinov, Alexander O. Kovalenko, Dmitry I. Ivanov, Gennady V. Novosibirsk State Technical University Dept. of Industrial Power Supply Systems Novosibirsk Russia Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia Surgut State University Dept. of Radio Electronics and Radio Engineering Surgut Russia

ISBN: (纸本)9781665498043

Electricity consumption forecasting is an integral part of the workflow in most industries. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The paper proposes two mathematical forecasting models, one of which is based on the recurrent method, and the second one is based on the vector method. At the same time, the structure of the models allows taking into account the individual features of the operating cycle of the production process, as well as identifying and smoothing the 'noise' components of the rank parametric surface of this production process. The Singular Spectrum Analysis method was chosen as the algorithm for preprocessing and smoothing time series of electricity consumption. The method was applied for the first time for a multidimensional array of electricity consumption data. As a result, rank parametric surfaces were plotted, one of which is the initial surface, and the second one is the final surface. © 2022 IEEE.

关键词： Energy conservation

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A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data 57

A Decision Making Model Where the Cell Exhibits Maximum Dete...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Emadi, Ali Lipniacki, Tomasz Levchenko, Andre Abdi, Ali New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland Yale University Yale Systems Biology Institute Dept. of Biomedical Eng. New HavenCT United States New Jersey Institute of Technology Dept. of Elect. Comput. Eng. Dept. of Biological Sciences NewarkNJ United States

ISBN: (纸本)9781665451819

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes. © 2023 IEEE.

关键词： Signal detection

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Analysis and Research on Students' Learning Behavior Based on Lag Sequence Analysis 4

Analysis and Research on Students' Learning Behavior Based o...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Han, Jiazheng Wei, Zhengang Ocean University of China Dept. of Computer Application Technology Qingdao China Ocean University of China Dept. of Software Engineering Qingdao China

ISBN: (纸本)9781665406925

with the effective practical application of data mining technology in other fields, many researchers have used data mining technology to analyze the learning process data in the field of education and teaching, and explore the hidden and potentially valuable information and knowledge. This paper mainly mines the association in students' learning behavior data through lag sequence analysis algorithm (LSA), and shows learners' learning trajectory, willingness and cognitive process in the learning process. Help to grasp the potential behavior mode of students' learning and effectively guide the development and implementation of follow-up teaching work. © 2021 IEEE.

关键词： Data mining

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Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Facial Shape-Based Eyeglass Recommendation Using Convolution...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Rakib Hossain Rifat Sunzida Siddique Laxmi Rani Das Mohd Ariful Haque Dept. of Computer Science and Engineering BRAC University Mohakhali Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Sonapur Bangladesh Dept. of Cyber-Physical Systems Clark Atlanta University Atlanta Georgia

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape; to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear.

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Transformer-Based Human Action Recognition with Dynamic Feature Selection

Transformer-Based Human Action Recognition with Dynamic Feat...

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Canadian Conference on computer and Robot Vision, CRV

作者： Soufiane Lamghari Guillaume-Alexandre Bilodeau Nicolas Saunier Dept. of Computer and Software Engineering LITIV Polytechnique Montreal Montreal Canada Dept. of Civil Geological and Mining Eng. Polytechnique Montreal Montreal Canada

Human action recognition in videos is an important task of computer vision that aims to automatically recognize and classify human actions in video sequences. However, accurately recognizing human actions can be challenging due to the complexity and variability of human motion and appearance. In this paper, we propose ActiViT, a novel approach for human action recognition in videos based on a Transformer architecture. Unlike existing methods that rely on convolutional or recurrent layers, our model is entirely based on the Transformer encoder, enabling us to leverage valuable information in action image patches features. We demonstrate that by dynamically selecting key patches guided by specific human poses, our model learns informative features useful for distinguishing between different actions. Our experimental results on real-world datasets convincingly demonstrate the effectiveness of our model and the importance of selecting discriminative key poses for action recognition.

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Proceedings of the 14th International Conference on Education Technology and computers

作者： Veronica Segarra-Faggioni Sylvie Ratté Frank De Jong Dept. Software and IT Engineering École de technologie supérieure Canada and Dept. Computer Science and Electronics Universidad Tecnica Particular de Loja Ecuador Dept. Software and IT Engineering École de technologie supérieure Canada Dept. Learning and Innovation Aeres Wageningen Applied University Netherlands

ISBN: (纸本)9781450397766

Knowledge Building as a pedagogy supports collaborative work between students to improve ideas. The result of knowledge-building discourse in student communities is the development of academic artifacts. Student reports are academic artifacts that can result from a knowledge-building process during their learning. However, the automatic analysis of student reports is quite challenging due to the documents’ length and written language. Given this context, our goal is to analyze the semantic similarity between student reports and curriculum literature using the information coverage measure based on the Skip-gram word2vec model. Word2vec is a well-known set of techniques that provides a way to create a representation of words to predict the nearby words between every word and its context to capture the internal semantic and syntactic information. In this work, through the experimental comparison between the vocabulary list, the best similarity value is 0.90 using word2vec. The results show that each student report was aligned with each document in the curriculum literature using the information coverage measure based on word2vec.

关键词： Machine learning natural language processing word2vec

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Cascaded Deep CNN Network for Breast Cancer Screening from Ultrasound Images Using Differential Evolution Algorithm

Cascaded Deep CNN Network for Breast Cancer Screening from U...

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Digital Society and Intelligent systems (DSInS), International Conference on

作者： Wahidur Rahman Md Shadman Sakib Md Solaiman Hosen Mir Mohammad Azad Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan Asadullah Shaikh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh Department of Computer Science Iowa State University USA Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science College of Computer Science and Information Systems Najran University Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Saudi Arabia

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images.

关键词： Limbs Ultrasonic imaging Machine learning algorithms Feature extraction Breast cancer Real-time systems Classification algorithms Convolutional neural networks Standards Optimization

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