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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Moezzi, Reza Saw, Adrian Bischoff, Stefan Cyrus, Jindrich Hlava, Jaroslav Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Faculty of Electrical Engineering and Computer Science Görlitz University of Applied Sciences Zittau Germany

ISBN: (纸本)9789819923212

By the unceasing efforts of many scientists, machine learning algorithms are increasing quickly with an enhanced object detection performance for the explicit applications on specific devices. This paper investigates the operation of TurtleBot3 as an educational wheeled autonomous robot which Tiny-YOLO algorithm has been applied as a novel application on it. At first, the required knowledge about the kinematics and dynamics of a differential drive robot with constraints is studied, and a graph where the robot is working under some specific conditions is plotted. The robot operating system (ROS) and simultaneous localization and mapping (SLAM) of the TurtleBot3 have been explained. The robot is equipped with a LiDAR sensor and a camera embedded onto it as a sensor fusion SLAM for a more precise type of mapping. Finally, Tiny-YOLO as a machine learning algorithm for object detection is implemented based on the fact that Raspberry Pi could not handle the full YOLO due to its memory capacity limitation, and the results are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robot Operating System

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scDrug: From single-cell RNA-seq to drug response prediction

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Computational and Structural Biotechnology Journal 2023年 21卷 150-157页

作者： Hsieh, Chiao-Yu Wen, Jian-Hung Lin, Shih-Ming Tseng, Tzu-Yang Huang, Jia-Hsin Huang, Hsuan-Cheng Juan, Hsueh-Fen Taiwan AI Labs Taipei10351 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan Department of Life Science National Taiwan University Taipei10617 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei10617 Taiwan

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://***/ailabstw/scDrug. © 2022 The Authors

关键词： RNA

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview 16

Automated Recognition of Structures in Scanning Electron Mic...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Marascu, Valentina Mihailescu, Marius Iulian Nita, Stefania Loredana Rogobete, Marius 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania 'Ferdinand i' Military Technical Academy Department of Computers and Cybersecurity Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified. © 2024 IEEE.

关键词： Python

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Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

Building Safer Autonomous Agents by Leveraging Risky Driving...

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2021 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2021

作者： Rana, Ashish Malhi, Avleen Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom

ISBN: (纸本)9781665432085

The highway-env reinforcement learning tasks provides a good abstract testbed for designing driving agents for specific driving scenarios like lane changing, parking or intersections etc. But, generally these driving simulation environments often restrict themselves to safer and precise trajectories. However, we clearly know that real driving tasks often involve very high risk collision prone unexpected situations. Hence, the autonomous model-free driving agents prepared in these environments are blind to certain low probability traffic collision corner cases. In our study we systematically focus on generating adversarial driving collision prone scenarios with dangerous driving behavior and heavy traffic in order to create robust autonomous agents. In our experimentation we train model free learning agents with additional collision prone scenario simulations and compare their efficacy with regular simulation based agents. Ultimately, we create a causal experimentation setup which successfully accounts for the performance improvements across different driving scenarios by utilizing learning from risky driving situations. © 2021 IEEE.

关键词： Autonomous agents

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Large sequence models for sequential decision-making:a survey

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Frontiers of computer Science 2023年第6期17卷 25-42页

作者： Muning WEN Runji LIN Hanjing WANG Yaodong YANG Ying WEN Luo MAI Jun WANG Haifeng ZHANG Weinan ZHANG School of Electronics Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200241China Digital Brain Lab Shanghai 201306China Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute for Artificial Intelligence Peking UniversityBeijing 100091China School of Informatics The University of EdinburghEdinburgh EH89JUUK Department of Computer Science University College LondonLondon WC1E 6BTUK

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision,e.g.,GPT-3 and Swin *** originally designed for prediction problems,it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems,which are typically beset by long-standing issues involving sample efficiency,credit assignment,and partial *** recent years,sequence models,especially the Transformer,have attracted increasing interest in the RL communities,spawning numerous approaches with notable effectiveness and *** survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer,by discussing the connection between sequential decision-making and sequence modeling,and categorizing them based on the way they utilize the ***,this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making,encompassing theoretical foundations,network architectures,algorithms,and efficient training systems.

关键词： sequential decision-making sequence modeling the Transformer training system

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Improving Land Cover Segmentation Using Multispectral Dataset 8

Improving Land Cover Segmentation Using Multispectral Datase...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

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Approximating Edit Distance in the Fully Dynamic Model

Approximating Edit Distance in the Fully Dynamic Model

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Annual IEEE Symposium on Foundations of computer Science

作者： Tomasz Kociumaka Anish Mukherjee Barna Saha Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science University of Warwick Coventry UK Dept. of Computer Science & Engineering University of California San Diego US

The edit distance is a fundamental measure of sequence similarity, defined as the minimum number of character insertions, deletions, and substitutions needed to transform one string into the other. Given two strings of length at most n, a simple dynamic programming computes their edit distance exactly in $\mathcal{O}\left(n^{2}\right)$ time, which is also the best possible (up to subpolynomial factors) assuming the Strong Exponential Time Hypothesis (SETH). The last few decades have seen tremendous progress in edit distance approximation, where the runtime has been brought down to subquadratic, to near-linear, and even to sublinear at the cost of approximation. In this paper, we study the dynamic edit distance problem where the strings change dynamically as the characters are substituted, inserted, or deleted over time. Each change may happen at any location of either of the two strings. The goal is to maintain the (exact or approximate) edit distance of such dynamic strings while minimizing the update time. The exact edit distance can be maintained in $\mathcal{O}\left(n \log ^{2} n\right)$ time per update (Charalampopoulos, Kociumaka, Mozes; 2020), which is again tight assuming SETH. Unfortunately, even with the unprecedented progress in edit distance approximation in the static setting, strikingly little is known regarding dynamic edit distance approximation. Utilizing the best near-linear-time (Andoni, Nosatzki; 2020) and sublinear-time (Goldenberg, Kociumaka, Krauthgamer, Saha; 2022) approximation algorithm, an old exact algorithm (Landau and Vishkin; 1988), and a generic dynamic strings implementation (Mehlhorn, Sundar, Uhrig; 1996), it is possible to achieve an $\mathcal{O}\left(n^{c}\right)$-approximation in $n^{0.5-c+o(1)}$ update time for any constant $c \in\left[0, \frac{1}{6}\right]$. Improving upon this trade-off, characterized by the approximation-ratio and update-time product $n^{0.5+o(1)}$, remains wide open. The contribution of this work is a dynami

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Forwarding Strategy Performance using Interest Variation Scheme in Named Data Networking 8

Forwarding Strategy Performance using Interest Variation Sch...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Ahdan, Syaiful Nurkahfi, Galih Nugraha Nurhayati, Ade Mayasari, Ratna Negara, Ridha Muldina Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung35132 Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta11710 Indonesia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Named Data Networking (NDN) is a new paradigm for the future internet architecture where the NDN communication process is based on the name of the content, two types of packets in NDN namely Interest and data controlled by the recipient. Forwarding strategy is responsible for determining where the interest will be forwarded, because each different application will require different forwarding behavior, this study aims to determine the performance of the forwarding strategy in accordance with the required application, This study uses a broadcast forwarding and bestroute strategy approach with the study parameters of the cumulative delay distribution in the observation time interval based on the number of variations of interest, whether the difference between the forwarding strategy and the amount of interest can affect the delay, this study uses the NDNSim simulator to test and make several scenarios with changes in interest, the results obtained after analyzing the two strategies are that the broadcast strategy has a higher delay than the bestroute strategy, to find out the results in detail, this study also displays the output in graph. © 2022 IEEE.

关键词： Wireless communication Process control Telematics Delays Behavioral sciences

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Multi-modal cooperative awareness of connected and automated vehicles in smart cities 5

Multi-modal cooperative awareness of connected and automated...

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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece Industrial Systems Institute Athena Research Center Patras Greece

ISBN: (纸本)9781665445115

Cooperative autonomous driving in 5G and smart cities environment is expected to further improve safety, security and efficiency of transportation systems. To this end, involved vehicles is imperative to have accurate knowledge of both their own and neighboring vehicles’ location, a task known as cooperative awareness. In this paper, we have formulated two novel distributed localization and tracking schemes, based on Gradient Descent and Extended Kalman Filter algorithms, to cope with erroneous GPS location. Sensor-rich vehicles exploit Vehicle-to-Vehicle communications and a multitude of integrated sensors, like LIDAR and Cameras, to generate and fuse heterogeneous data. Each vehicle interacts only with its own connected neighboring vehicles, formulating individual star topologies. Extensive simulation studies using CARLA autonomous driving simulator, verify the significant reduction of GPS error achieved by the two methods in various experimental conditions. Distributed tracking proves to be much superior than Gradient descent algorithm, both in the case of self (58% reduction of GPS error) and neighboring vehicles location estimation (38% reduction of average GPS error). © 2021 IEEE.

关键词： 5G mobile communication systems

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A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

arXiv

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arXiv 2024年

作者： Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030-790 Portugal Virginia Commonwealth University RichmondVA23284 United States

Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated *** Codes 68T01 © 2024, CC BY.

关键词： Federated learning

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