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arXiv 2022年

作者： Passalis, Nikolaos Pedrazzi, S. Babuska, R. Burgard, W. Dias, D. Ferro, F. Gabbouj, M. Green, O. Iosifidis, A. Kayacan, E. Kober, J. Michel, O. Nikolaidis, N. Nousi, P. Pieters, R. Tzelepi, M. Valada, A. Tefas, A. Dept. of Informatics Aristotle University of Thessaloniki Greece Cyberbotics Switzerland Dept. of Cognitive Robotics Delft University of Technology Netherlands Dept. of Computer Science University of Freiburg Germany PAL Robotics Spain The units of Computing Sciences and Automation Technology and Mechanical Engineering Tampere University Finland Agrointelli Denmark The Department of Electrical and Computer Engineering Aarhus University Denmark

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles. Copyright © 2022, The Authors. All rights reserved.

关键词： Robotics

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rando Noor Akhmad Setiawan Adhistya Erna Permanasari Riries Rulaningtyas Andriyan Bayu Suksmono Imas Sukaesih Sitanggang Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. of Physics Universitas Airlangga Biomedical Engineering Program Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.

关键词： Satellites Extreme learning machines Medical services Feature extraction Generative adversarial networks Entropy Lesions

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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People movement linkage based on path revision across multiple cameras

People movement linkage based on path revision across multip...

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作者： Chang, I-Cheng Liu, Chieh-Yu Huang, Chung-Lin Kabi, Kunal Dept. of Computer Science and Information Engineering National Dong Hwa University Taiwan Dept. of Electrical Engineering National Tsing Hua University Taiwan Dept. of Applied Informatics and Multimedia Asia University Taiwan

This work presents an object-based people motion linkage system, which tracks and records the behavior of each person across multiple cameras. The proposed system includes two phases: path construction phase and path revision phase. The spatiotemporal relationships among cameras are trained by batch-learning procedures, and the appearance model is improved by color calibration for different cameras. When a person moves across cameras, the spatiotemporal relationship and appearance model are used to obtain the correspondence. However, object tracking may be lost due to some unexpected events. We revise the tracking paths by using the backward tracking technique to connect the missed links. Moreover, the trained Hidden Markov Models are further used to reconstruct the normal paths of these objects. In the experimental results, we demonstrate the efficiency of our approach by showing the reconnection of missing paths under different conditions. © 2020 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Dynamic programming

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Goal-Aware RSS for Complex Scenarios via Program Logic

Goal-Aware RSS for Complex Scenarios via Program Logic

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IEEE Symposium on Intelligent Vehicle

作者： Ichiro Hasuo Clovis Eberhart James Haydon Jérémy Dubut Rose Bohrer Tsutomu Kobayashi Sasinee Pruekprasert Xiao-Yi Zhang Erik André Pallas Akihisa Yamada Kohei Suenaga Fuyuki Ishikawa Kenji Kamijo Yoshiyuki Shinya Takamasa Suetomi National Institute of Informatics (NII) Tokyo Japan SOKENDAI (The Graduate University for Advanced Studies) Japan Japanese-French Laboratory for Informatics (IRL 3527) Tokyo Japan Dept. Computer Science Worcester Polytechnic Institute Worcester MA USA Inst. Software & Systems Engineering University of Augsburg Augsburg Germany Cyber Physical Security Research Center AIST Tokyo Japan Graduate School of Informatics Kyoto University Kyoto Japan Mazda Motor Corporation Fuchu Japan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

关键词： Intelligent vehicles computer architecture Software Cognition Safety Planning Complexity theory

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Designing an innovative educational toolbox to support the transition to new technologies

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SN Social Sciences 2021年第3期1卷 1-22页

作者： Leligou, Helen C. Ponci, Ferdinanda De Rosa, Rosanna Karkazis, Panagiotis A. Psomopoulos, Constantinos S. Department of Industrial Design and Production Engineering University of West Attica Athens Greece RWTH Aachen University Aachen Germany Dept. of Social Sciences University of Naples Federico II Naples Italy Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Electrical and Electronics Engineering University of West Attica Athens Greece

Our economies and societies are becoming more and more knowledge based which implies that increasing numbers of people need to be educated and trained on new subjects and processes. Thus, the reduction of the effort needed to design and prepare educational and training programmes that meet the needs of the society and the market is of paramount importance. To achieve this goal, first, we define a learning programme model so that programme designers can easily exchange and re-use programme structures and learning materials. The proposed model additionally enables easier creation of interdisciplinary programmes which is another need of today’s market. Second, we deploy a web-based tool that adopts this model towards facilitating the re-use of structures and materials. Third, to reduce the time required for the training actors to sense the market needs, we propose the establishment of an educational programme marketplace. All three endeavours have been validated in the energy transition sector and (positively) evaluated by experts during an international workshop. © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021.

关键词： Education resource re-use Educational programme modelling Effective programme design

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On the Way to Realize the 5th Industrial Revolution: Achievements, Challenges and Research Areas

On the Way to Realize the 5th Industrial Revolution: Achieve...

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IEEE Symposium on Network Operations and Management

作者： George Dimirakopoulos Thomas Gutt Hans Ehm Alfred Hoess Konstantina Karathanasopoulou Anna Lackner Germar Schneider dept. of Informatics and Telematics Harokopio Univesity of Athens Athens Greece Infineon Technologies A.G. Munich Germany dept. of Electrical Engineering Media and Computer Science University of Applied Sciences Amberg-Weiden Amberg Germany eesy-innovation GmbH Unterhaching Germany Infineon Technologies Dresden GmbH&Co.KG Dresden Germany

The industrial world is rapidly moving towards its $5^{\text {th}}$ industrial revolution (a.k.a. “Industry5.0”). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing $A I$-enabled hardware, as well as $A I$ tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty.

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SafeSCHEMA: Multi-domain Orchestration of Slices based on SafeRL for B5G Networks

SafeSCHEMA: Multi-domain Orchestration of Slices based on Sa...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Anestis Dalgkitsis Ashima Chawla Anne-Marie Bosneag Christos Verikoukis Iquadrat Informatica S.L. Barcelona Spain Network Management Research Lab LM Ericsson Athlone Ireland Computer Engineering & Informatics Dept. University of Patras Greece

ISBN: (纸本)9781665435413

The success of Software-Defined Networking and Network Function Virtualization enabled providers to partially automate the decision-making of various network operating workflows. Network slicing is an innate feature of the Fifth Generation (5G) and Beyond-5G (B5G) networks, bringing many advantages but also increasing the complexity of managing such networks. In this context, automation tools based on Artificial Intelligence (AI) have become a preeminent instrument of automation used by the industry, as it would be impossible to manually provision and manage such a vast and dynamic infrastructure. Safe interaction of the AI agents with the network is one of the predominant challenges, especially when Reinforcement Learning (RL) is used in critical environments. This is particularly important when the RL agent actions have a high or irreversible impact on the network or service. Slice management is one of the major features that operators want to automate, to offer future services at large scales with manageable complexity to the operator. However, during the exploration phase, RL agents can cause significant performance degradation during operation and possibly introduce irreversible damage to the service being offered. To address this major challenge, we propose a multi-agent, modular, SafeRL architecture for distributed slice orchestration. We study the problem of zero-touch slice management and orchestration, in the context of Ultra-Reliable Low Latency Communication services for B5G networks. Our results demonstrate improved performance over competing solutions, while ensuring the safety of the performed actions during real-time slice orchestration.

关键词： Automation Scalability Reinforcement learning Ultra reliable low latency communication Real-time systems Space exploration Safety

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The Role of Extraversion in Phishing Victimisation: A Systematic Literature Review

The Role of Extraversion in Phishing Victimisation: A System...

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eCrime Researchers Summit

作者： Pablo López-Aguilar Constantinos Patsakis Agusti Solanas Dept. of Research and Innovation Anti-Phishing Working Group - Europe Barcelona Catalonia Spain Dept. of Informatics University of Piraeus Piraeus Greece Athena Research Center Marousi Athens Greece Dept. of Computer Engineering and Mathematics Universitat Rovira i Virgili Tarragona Catalonia Spain

Over the last decade, phishing attacks have become preeminent and increasingly successful. Anti-phishing strategies focus on raising awareness and training users to identify risks. However, those strategies do not fully consider the psychological profile of each individual. We sustain that maximising potential victims’ resilience requires additional protection strategies to focus on individual personality *** this article, we concentrate on extraversion as a personality trait for which there is no consensus about its effect on susceptibility to phishing attacks. We implement a robust bibliographic analysis methodology and identify potentially relevant articles, which we screen and filter against inclusion and exclusion criteria. We report and analyse the findings of the 39 articles that fulfil all criteria and are deemed relevant to this research. Our analysis shows that, despite the positive correlation between extraversion and phishing susceptibility found in many studies, there is no consensus supported by a well-established psychological theory. Moreover, we identify a number of reasons justifying this lack of consensus, namely the use of non-representative samples, the non consideration of contextual factors, and the use of self-reported personality tests, which lead to limited reproducibility and data inconsistencies.

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