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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation intelligent sensors

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End-to-End Multi-Modal Tiny-CNN for Cardiovascular Monitoring on Sensor Patches

End-to-End Multi-Modal Tiny-CNN for Cardiovascular Monitorin...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Mustafa Fuad Rifet Ibrahim Tunc Alkanat Maurice Meijer Alexander Schlaefer Peer Stelldinger NXP Semiconductors Eindhoven The Netherlands Institute of Medical Technology and Intelligent Systems Hamburg University of Technology Hamburg Germany Department of Computer Science Hamburg University of Applied Sciences Hamburg Germany

The vast majority of cardiovascular diseases are avoidable or treatable by preventive measures and early de-tection. To efficiently detect early signs and risk factors, car-diovascular parameters can be monitored continuously with small sensor patches, which improve the comfort of patients. However, processing the sensor data is a challenging task with the demanding needs of robustness, reliability, performance and efficiency. The field of deep learning has tremendous potential to provide a way to analyze cardiovascular sensor data to detect anomalies which alleviates the workload of doctors for more effective data interpretation. In this work, we show the feasibility of applying deep learning for the classification of synchronized electrocardiogram and phonocardiogram recordings under very tight resource constraints. Our model employs an early fusion of data and uses convolutional layers to solve the problem of binary classification of anomalies. Our experiments show that our model matches the accuracy of the current state-of-the-art model on the “training-a” dataset of the Physionet Challenge 2016 database while being more than two orders of magnitude more efficient in memory footprint and compute cost. Further, we demonstrate the applicability of our model on edge devices, such as sensor patches, by estimating processor performance, power consumption, and silicon area.

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SPL: A Socratic Playground for Learning Powered by Large Language Model

SPL: A Socratic Playground for Learning Powered by Large Lan...

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2024 Joint of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops, HEXED-L3MNGET 2024

作者： Zhang, Liang Lin, Jionghao Kuang, Ziyi Xu, Sheng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States School of Psychology Shaanxi Normal University Xi’an710062 China School of Psychology Central China Normal University Wuhan430079 China Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

Dialogue-based intelligent Tutoring systems (ITSs) have significantly advanced adaptive and personalized learning by automating sophisticated human tutoring strategies within interactive dialogues. However, replicating the nuanced patterns of expert human communication remains a challenge in Natural Language Processing (NLP). Recent advancements in NLP, particularly Large Language Models (LLMs) such as OpenAI’s GPT-4, offer promising solutions by providing human-like and context-aware responses based on extensive pre-trained knowledge. Motivated by the effectiveness of LLMs in various educational tasks (e.g., content creation and summarization, problem-solving, and automated feedback provision), our study introduces the Socratic Playground for Learning (SPL), a dialogue-based ITS powered by the GPT-4 model, which employs the Socratic teaching method to foster critical thinking among learners. Through extensive prompt engineering, SPL can generate specific learning scenarios and facilitates efficient multi-turn tutoring dialogues. The SPL system aims to enhance personalized and adaptive learning experiences tailored to individual needs, specifically focusing on improving critical thinking skills. Our pilot experimental results from essay writing tasks demonstrate SPL has the potential to improve tutoring interactions and further enhance dialogue-based ITS functionalities. Our study, exemplified by SPL, demonstrates how LLMs enhance dialogue-based ITSs and expand the accessibility and efficacy of educational technologies. © 2024 Copyright for this paper by its authors.

关键词： Problem solving

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Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

Augmented Reality Display of Robot with Graphs of Property R...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Kazuki Tsukamoto Masanobu Koga Department of Creative Informatics Kyushu Institute of Technology Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.

关键词： Solid modeling Automation Shape Robot kinematics Robot vision systems Education Control systems

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Fixed-Point RNNs: From Diagonal to Dense in a Few Iterations

arXiv

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arXiv 2025年

作者： Movahedi, Sajad Sarnthein, Felix Cirone, Nicola Muça Orvieto, Antonio ELLIS Institute Tübingen Tübingen AI Center Germany Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Department of Mathematics Imperial College London United Kingdom

Linear recurrent neural networks (RNNs) and state-space models (SSMs) such as Mamba have become promising alternatives to softmax-attention as sequence mixing layers in Transformer architectures. Current models, however, do not exhibit the full state-tracking expressivity of RNNs because they rely on channel-wise (i.e. diagonal) sequence mixing. In this paper, we propose to compute a dense linear RNN as the fixed-point of a parallelizable diagonal linear RNN in a single layer. We explore mechanisms to improve its memory and state-tracking abilities in practice, and achieve state-of-the-art results on the commonly used toy tasks A5, S5, copying, and modular arithmetics. We hope our results will open new avenues to more expressive and efficient sequence mixers. © 2025, CC BY.

关键词： Recurrent neural networks

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Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs

arXiv

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arXiv 2024年

作者： Guo, Siyuan Didolkar, Aniket Ke, Nan Rosemary Goyal, Anirudh Huszár, Ferenc Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science University of Cambridge United Kingdom Mila University of Montreal Canada Google DeepMind United Kingdom

We are beginning to see progress in language model assisted scientific discovery. Motivated by the use of LLMs as a general scientific assistant, this paper assesses the domain knowledge of LLMs through its understanding of different mathematical skills required to solve problems. In particular, we look at not just what the pre-trained model already knows, but how it learned to learn from information during in-context learning or instruction-tuning through exploiting the complex knowledge structure within mathematics. Motivated by the Neural Tangent Kernel (NTK), we propose NTKEval to assess changes in LLM’s probability distribution via training on different kinds of math data. Our systematic analysis finds evidence of domain understanding during in-context learning. By contrast, certain instruction-tuning leads to similar performance changes irrespective of training on different data, suggesting a lack of domain understanding across different skills. © 2024, CC BY.

关键词： Pattern matching

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Decision-Making Framework For AMR Fleet Size In Manufacturing Environments 25

Decision-Making Framework For AMR Fleet Size In Manufacturin...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Rema, Catarina Santos, Romao Piqueiro, Henrique Matos, Diogo Miguel Oliveirat, Paulo Moura Costa, Pedro Silva, Manuel F. University of Trás-os-Montes and Alto Douro (UTAD) Department of Engineering Vila Real Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre for Robotics in Industry and Intelligent Systems Porto 4200-465 Portugal University of Porto (FEUP) Faculty of Engineering Porto 4200-465 Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto 4249-015 Portugal JPM Industry Vale de Cambra 3731-901 Portugal

ISBN: (纸本)9798331538606

Industry 4.0 is transforming manufacturing environments, with robotics being a key technology that enhances various capabilities. The flexibility of Autonomous Mobile Robots has led to the rise of multi-robot systems in industrial settings. Considering the high cost of these robots, it is essential to determine the best fit of number and type before making any major investments. Simulation and modeling are valuable decision-support tools, allowing the simulation of different setups to address robot fleet sizing issues. This paper introduces a decision-support framework that combines a fleet manager software stack with the FlexSim simulator, helping decision-makers determine the most suitable mobile robots fleet size tailored to their needs. Unlike previous approaches, the developed solution integrates the same real robot coordination software in both simulation and actual deployment, ensuring that tested scenarios accurately reflect real-world conditions. A case study was conducted to evaluate the framework, involving multiple tasks of loading and unloading materials within a warehouse. Five different scenarios with varying fleet sizes were simulated, and their performances assessed. The analysis concluded that, for the case study under consideration, a fleet of three robots was the most suitable, considering relevant key performance indicators. The results confirmed that the developed solution is an effective alternative for addressing the problem and represents a novel technology with no prior state-of-the-art equivalents. © 2025 IEEE.

关键词： AMR Fleet Manager Fleet Size Simulation and Decision Support Framework

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The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example

The Methodological Pitfall of Dataset-Driven Research on Dee...

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MILCOM, Military Communications Conference

作者： Tianshi Wang Denizhan Kara Jinyang Li Shengzhong Liu Tarek Abdelzaher Brian Jalaian Department of Computer Science University of Illinois at Urbana Champaign Urbana IL Department of Intelligent Systems and Robotics University of West Florida Pensacola FL 32514

In this paper, we highlight a dangerous pitfall in the state-of-the-art evaluation methodology of deep learning algorithms. It results in deceptively good evaluation outcomes on test datasets, whereas the underlying algorithms remain prone to catastrophic failure in practice. We illustrate the pitfall in the context of an Internet-of-Things (IoT) application example and show that it occurs despite the use of cross-validation that breaks down the data into separate training, validation, and testing sets. The pitfall is illustrated by designing two target detection and classification algorithms. One is based on a recently proposed neural network architecture for embedded AI, and the other is based on a traditional machine learning approach with domain-inspired feature engineering. The neural network approach outperforms the traditional one on test data. Yet, it fails in deployment. The mechanics behind the failure are explained and linked to the way the algorithms are trained. Suggestions are presented to avoid the pitfall. The paper is a “call to arms” to improve the evaluation methodology of machine learning algorithms for mission-critical systems.

关键词： Deep learning Training Military communication Feedback loop Machine learning algorithms Neural networks Mission critical systems

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Velocity Potential Field Modulation for Dense Coordination of Polytopic Swarms and Its Application to Assistive Robotic Furniture

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IEEE robotics and Automation Letters 2025年第7期10卷 7452-7459页

作者： Tang, Lixuan Ruegg, David Zhang, Runze Bolotnikova, Anastasia Rabaey, Jan Ijspeert, Auke Swiss Federal Institute of Technology in Lausanne - EPFL Biorobotics Laboratory Switzerland Tongji University Department of Computer Science and Technology Shanghai China Laboratory for Analysis and Architecture of Systems (LAAS-CNRS) Robotics and InteractionS Team France IMEC System-Technology Co-Optimization (STCO) Belgium

We explore the use of a mobile furniture swarm that are intended to assist users with limited mobility in their daily indoor activities. We focus on the multi-robot coordination problem when a dense target pose configuration is required, such as in an apartment setting. In those cases, the convergence of one robot to the target can be significantly affected by neighboring robots with specific shapes. In this letter, we propose a solution, named Velocity Potential Field Modulation (VPFM), to deal with the dense coordination problem of a polytopic swarm in a decentralized manner. We adapt our method to assistive applications, such as room reconfigurations and facilitating indoor movement of wheelchair users. We evaluate the performance of our method in simulations and on real-world mobile furniture hardware, demonstrating its effectiveness and real-time performance. © 2016 IEEE.

关键词： Collision Avoidance Path Planning for Multiple Mobile Robots or Agents Swarm robotics

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