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Sustainable Expert systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants Machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

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IFAC-PapersOnLine 2022年第35期55卷 37-42页

作者： Matthis H. de Lange Chris Verhoek Valentin Preda Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600 MB The Netherlands ESTEC European Space Agency Noordwijk 2200 AG The Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest 1111 Hungary

Obtaining models that can be used for flight control is of outmost importance to ensure reliable guidance and navigation of spacecrafts, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert an existing, high-fidelity nonlinear model of the atmospheric flight dynamics of a GPRV to a Linear Parameter-Varying (LPV) form that enables high-performance navigation control design. Application of existing systematic conversion methods for such complicated nonlinear models often result in complex LPV representations, which are not suitable for controller synthesis. We apply and compare state-of-the-art conversion techniques on the GPRV model, including learning based approaches, to optimize the complexity and conservatism of the resulting LPV embedding. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is efficiently chosen.

关键词： linear parameter-varying systems scheduling reduction spacecraft modeling aerodynamics principle component analysis deep neural networks

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University digital transformation plan through the implementation of Digital Resources: The case of the Technological University of Panama 11

University digital transformation plan through the implement...

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11th International Conference on Virtual Campus, JICV 2021

作者： Villarreal, Vladimir Mora, Dafni Merchan, Fernando Castillo, Aris Alaín, Lineth Chavarría, Miguel Technological University of Panama Research Group in Emerging Computational Technologies David Chiriquí Panama Technological University of Panama Research Group in Energy and Comfort Bioclimatic Buildings Panamá Panama Technological University of Panama Research Group in Advanced Digital Communications Systems Panamá Panama Technological University of Panama Faculty of Computer Systems Engineering Panamá Panama Technological University of Panama Research Group in the Field of Education and Disability La Chorrera Panama

ISBN: (纸本)9781665437035

With the globalization and proliferation of COVID-19, new challenges have emerged in higher education regarding the use and integration of new technologies. In this context, the university is called upon to organize, distribute, and design the best strategies to overcome the challenge of distance education in times of pandemic. This document proposes elements that support the implementation of a Digital Transformation Plan of the Technological University of Panama. The digital resource platform developed for professors and researchers to share with their students, digital resources such as: manuals, tools, videos, webinars, podcasts, and various experiences that contribute to the support of the teaching-learning process is presented. The structure of the platform is presented, as well as an evaluation by registered users. © 2021 IEEE.

关键词： COVID-19

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A Generic Obfuscation Framework for Preventing ML-Attacks on Strong-PUFs through Exploitation of DRAM-PUFs

A Generic Obfuscation Framework for Preventing ML-Attacks on...

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IEEE European Symposium on Security and Privacy (EuroS&P)

作者： Owen Millwood Meltem Kurt Pehlivanoğlu Aryan Mohammadi Pasikhani Jack Miskelly Prosanta Gope Elif Bilge Kavun Department of Computer Science The University of Sheffield Sheffield UK Department of Computer Engineering Kocaeli University Kocaeli Turkey Centre for Secure Information Technologies Queen’s University Belfast Belfast United Kingdom Secure Intelligent Systems Research Group University of Passau Passau Germany

Considering the limited power and computational resources available, designing sufficiently secure systems for low-power devices is a difficult problem to tackle. With the ubiquitous adoption of the Internet of Things (IoT) not appearing to be slowing any time soon, resource-constrained security is more important than ever. Physical Unclonable Functions (PUFs) have gained momentum in recent years for their potential to enable strong security through the generation of unique identifiers based on entropy derived from unique manufacturing variations. Strong-PUFs, which are desirable for authentication protocols, have often been shown to be insecure to Machine Learning Modelling Attacks (ML-MA). Recently, some schemes have been proposed to enhance security against ML-MA through post-processing of the PUF; however, often, security is not sufficiently upheld, the scheme requires too large an additional overhead or key data must be insecurely stored in Non-Volatile Memory. In this work, we propose a generic framework for securing Strong-PUFs against ML-MA through obfuscation of challenge and response data by exploiting a DRAM-PUF to supplement a One-Way Function (OWF) which can be implemented using the available resources on an FPGA platform. Our proposed scheme enables reconfigurability, strong security and one-wayness. We conduct ML-MA using various classifiers to thoroughly evaluate the performance of our scheme across multiple 16-bit and 32-bit Arbiter-PUF (APUF) variants, showing our scheme reduces model accuracy to around 50% for each PUF (random guessing) and evaluate the properties of the final responses, demonstrating that ideal uniformity and uniqueness are maintained. Even though we demonstrate our proposal through a DRAM-PUF, our scheme can be extended to work with memory-based PUFs in general.

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Benchmarks for End-to-End Microservices Testing

Benchmarks for End-to-End Microservices Testing

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2023 IEEE International Conference on Service-Oriented System engineering (SOSE)

作者： Sheldon Smith Ethan Robinson Timmy Frederiksen Trae Stevens Tomas Cerny Miroslav Bures Davide Taibi Computer Science Baylor University Waco Texas USA Systems and Industrial Engineering University of Arizona Tucson Arizona USA Computer Science Czech Technical University FEE Prague Czech Republic M3S Cloud Group University of Oulu Oulu Finland

Testing microservice systems involves a large amount of planning and problem-solving. The difficulty of testing microservice systems increases as the size and structure of such systems become more complex. To help the microservice community and simplify experiments with testing and traffic simulation, we created a test benchmark containing full functional testing coverage for two well-established open-source microservice systems. Through our benchmark design, we aimed to demonstrate ways to overcome certain challenges and find effective strategies when testing microservices. In addition, to demonstrate our benchmark use, we conducted a case study to identify the best approaches to take to validate a full coverage of tests using service-dependency graph discovery and business process discovery using tracing.

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Ontology-Driven Analytics for Indoor Point Clouds 25

Ontology-Driven Analytics for Indoor Point Clouds

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25th International Conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2020

作者： Stojanovic, Vladeta Hagedorn, Benjamin Trapp, Matthias Döllner, Jürgen Faculty of Digital Engineering Hasso Plattner Institute University of Potsdam Computer Graphics Systems Group Germany

ISBN: (纸本)9789887891741

Automated processing, semantic enrichment and visual analytics methods for point clouds are often use-case specific for a given domain (e.g, for Facility Management (FM) applications). Currently, this means that applicable processing techniques, semantics and visual analytics methods need to be selected, generated or implemented by human domain experts, which is an error-prone, subjective and non-interoperable process. An ontology-driven analytics approach can be used to solve this problem by creating and maintaining a Knowledge Base, and utilizing an ontology for automatically suggesting optimal selection of processing and analytics techniques for point clouds. We present an approach of an ontology-driven analytics concept and system design, which supports smart representation, exploration, and processing of indoor point clouds. We present and provide an overview of high-level concept and architecture for such a system, along with related key technologies and approaches based on previously published case studies. We also describe key requirements for system components, and discuss the feasibility of their implementation within a Service-Oriented Architecture (SOA). © 2020 and published by the Association for computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Ontology

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Computational and Experimental Design of Fast and Versatile Magnetic Soft Robotic Low Re Swimmers

SSRN

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SSRN 2024年

作者： Pramanik, Ratnadeep Park, Minjo Ren, Ziyu Sitti, Metin Verstappen, Roel W.C.P. Onck, Patrick R. Computational & Numerical Mathematics Group Bernoulli Institute for Mathematics Computer Science & Artificial Intelligence University of Groningen Netherlands Micromechanics Group Zernike Institute for Advanced Materials University of Groningen Netherlands Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany School of Mechanical Engineering and Automation Beihang University Beijing China Institute for Biomedical Engineering ETH Zurich Switzerland School of Medicine College of Engineering Koc University Istanbul Turkey

Miniaturized magnetic soft robots have shown extraordinary capabilities of contactless manipulation, complex path maneuvering, precise localization, and rapid actuation, enabling them to cater to challenging biomedical applications such as targeted drug delivery, internal wound healing, and laparoscopic surgery. However, despite their successful fabrication by several different research groups, a thorough design strategy encompassing the optimized kinematic performance of the three fundamental biomimetic swimming modes at miniaturized length scales has not been reported until now. Here, we resolve this by designing magnetic soft robotic swimmers (MSRSs) from the class of helical and undulatory low Reynolds number (Re) swimmers using a fully coupled, experimentally calibrated computational fluid dynamics model. We study (and compare) their swimming performance, and report their steady-state swimming speed for different non-dimensional numbers that capture the competition by magnetic loading, nonlinear elastic deformation, and viscous solid-fluid coupling. We investigated their stability for different initial spatial orientations to ensure robustness during real-life applications. Our results show that the helical 'finger-shaped' swimmer is by far the fastest low Re swimmer in terms of body lengths per cycle, but that the undulatory 'carangiform-like' swimmer proved to be the most versatile, bidirectional swimmer with maximum stability. © 2024, The Authors. All rights reserved.

关键词： Reynolds number

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A Hybrid Approach Using Survey and Ism Analysis for Security Coding Risks and Best Practices for Gsd Enterprises

SSRN

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SSRN 2023年

作者： Khan, Rafiq Ahmad Akbar, Muhammad Azeem Alzahrani, Abdulrahman Habibullah, Safa Alwageed, Hathal Salamah Alzahrani, Musaad School of Software Northwestern Polytechnical University Shaanxi Xian710129 China Software Engineering Research Group Department of Computer Science & IT University of Malakand Pakistan Software engineering LUT University Finland Information Systems and Technology Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Department of Computer Science Al-Baha University Al-Baha65799 Saudi Arabia

Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM).Methods: Initially, we conduct a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ***: Initially, we identified 13 major security-coding risks and 82 practices to mitigate these risks. The ISM analysis revealed that out of the identified security-coding practices, "never submit security measures to illegitimate authority", "avoiding buffer overflow and format string vulnerabilities", "control the brute force attack", and "identify a middleman attack" were considered fully dependent. While "avoid revealing information to achieve a secure design" is entirely independent within the GSD security coding ***: The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes. © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Safe Barrier-Constrained Control of Uncertain systems via Event-triggered Learning

arXiv

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arXiv 2024年

作者： Lederer, Armin Begzadić, Azra Hirche, Sandra Cortés, Jorge Herbert, Sylvia The Learning and Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland The Department of Mechanical and Aerospace Engineering University of California San Diego United States The Chair of Information-oriented Control TUM School of Computation Information and Technology Technical University of Munich Germany

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge 1

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14th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2021

作者： Shannon, Peter David Nehaniv, Chrystopher L. Phon-Amnuaisuk, Somnuk Adaptive Systems Research Group Centre for Computer Science & Informatics Research University of Hertfordshire Hatfield United Kingdom Laksmana College of Business Bandar Seri Begawan Brunei Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Electrical & Computer Engineering University of Waterloo WaterlooON Canada School of Computing and Informatics Universiti of Teknologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9783030802530

ISBN: (纸本)9783030802523

We present a study looking at the effect of a priori domain knowledge on an EA fitness function. Our experiment has two aims: (1) applying an existing NSGA-II framework for GP with PDL to the cartpole problem—applying GP & PDL to cartpole and a purely behavioral problem for the first time—and (2) contrasting two multi-objective fitness functions: one with high and the other with low a priori domain knowledge. In our experiment we created two populations with an EA, varying in the number of objectives use for the fitness function, 2 objective criteria to represent low a priori knowledge and 3 to represent high. With fitness functions tailored to find specifically prescribed solutions we expect greater discriminating power and more feedback to an evolutionary process. This comes at the cost of excluding some unexpected solutions from the evolutionary process and placing a greater burden on the designer. We address the question: how large is the disadvantage for the low a priori fitness function in a worst-case scenario, where innovative solutions will not enhance performance. This question is interesting because we would prefer to guide EA with simple, easy to create and understand, objective criteria rather than complex and highly specific criteria. Understanding any associated penalty for using simple, easy to create fitness functions, is crucial in assessing how much effort and should be put into designing objective criteria. © 2021, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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