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Behavior Trees and State Machines in Robotics Applications

IEEE Transactions on Software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Exploration of the Role of Cryptoprocessors in Advancing IoT Security

Exploration of the Role of Cryptoprocessors in Advancing IoT...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Elena Kakoulli Eleftherios Zacharioudakis Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Igor Sikorsky Kyiv Polytechnic Institute National Technical University of Ukraine Kyiv Ukraine

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the security of IoT ecosystems. Leveraging insights from recent research papers, the survey examines various aspects of Cryptoprocessor implementations, ranging from lightweight countermeasures to advanced lattice-based post-quantum cryptography. The proposed paper amalgamates findings from studies on hardware architectures, energy-efficient designs, and innovative cryptographic algorithms to provide a holistic overview of state-of-the-art advancements in IoT security. The synthesis of this survey’s exploration aims to contribute to the ongoing discourse on securing IoT devices through robust hardware-backed cryptographic solutions.

关键词： Surveys Industries Technological innovation Program processors Ecosystems Hardware Energy efficiency

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

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Leveraging Outage probability in system limited by BX fading and Co-channel interference for ChatGPT-enabled QoS adaptation 31

Leveraging Outage probability in system limited by BX fading...

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31st Telecommunications Forum, TELFOR 2023

作者： Vujović, Vuk Milić, Dejan Petrović, Nenad Marković, Filip Suljović, Suad Stefanović, Radiša MB University Belgrade Computer Science and Informatics Information Technology Belgrade Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Computer Science and Informatics Faculty of Electronic Engineering Niš Serbia University of Priština Computer Science and Informatics Faculty of Technical Sciences Kosovska Mitrovica Serbia Computer Science and Informatics the Academy of Applied Technical Studies Belgrade Belgrade Serbia

ISBN: (纸本)9798350303131

In this paper, we performed an analysis of the outage probability (Pout) at the output of the receiver with selection combination (SC) with L branches of the wireless communication system in the presence of Beaulieu-Xie (BX) fading and BX co-channel interference (CCI). The mathematical form of the function for Pout as a normalized signal-to-interference ratio (SIR) was derived, based on which a graph was drawn for different values of fading and CCI parameters. The second part of the paper considers applying machine learning approach relying on ChatGPT with aim to enable fading type recognition based on Pout measurements, which can be further used in order to perform Quality of Service (QoS) adaptation by tuning receiver's parameters. © 2023 IEEE.

关键词： Quality of service

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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Energy and cost benefits of advanced lighting technology applications in traffic light systems

Energy and cost benefits of advanced lighting technology app...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Alexis Polycarpou Marios S. Georgiou Department of Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus Electromechanical Section Nicosia Municipality Nicosia Cyprus

ISBN: (数字)9781837242689

Following a pilot project, which concluded in 2016, the replacement of old energy consuming Traffic Light Systems (TLS) of Nicosia Municipality were the subject of an investigation with regards to the energy saving potential and most importantly the payback period of the investment, through presumptions. This paper presents the results obtained of the new efficient technology implementation, with regards to the energy saving that materialised in the Nicosia Municipality. Investigation is presented in an attempt to determine whether the proposed solution and calculated capital employed had the expected energy usage reduction, return on investment, and payback period. The estimated cost benefit of the installed systems compared to the actual benefits varies significantly, after factors such as maintenance and electricity costs are considered.

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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