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20th international conference on Generative Intelligence and intelligent Tutoring Systems, ITS 2024

20th International Conference on Generative Intelligence and...

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20th international conference on Generative Intelligence and intelligent Tutoring Systems, ITS 2024

ISBN: (纸本)9783031630279

the proceedings contain 62 papers. the special focus in this conference is on Generative Intelligence and intelligent Tutoring Systems. the topics include: Combined Maps as a Tool of Concentration and Visualization of Knowledge in the Logic of Operation of the intelligent Tutoring Systems;fast Weakness Identification for Adaptive Feedback;quizMaster: An Adaptive Formative Assessment System;preliminary Systematic Review of Open-Source Large Language Models in Education;Jill Watson: Scaling and Deploying an AI Conversational Agent in Online Classrooms;Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts;enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational data;individualised Mathematical Task Recommendations through Intended learning Outcomes and Reinforcement learning;developing Conversational intelligent Tutoring for Speaking Skills in Second Language learning;SAMI: An AI Actor for Fostering Social Interactions in Online Classrooms;exploring the Methodological Contexts and Constraints of Research in Artificial Intelligence in Education;a Constructivist Framing of Wheel Spinning: Identifying Unproductive Behaviors with Sequence Analysis;evaluating the Ability of Large Language Models to Generate Motivational Feedback;Towards Cognitive Coaching in Aircraft Piloting Tasks: Building an ACT-R Synthetic Pilot Integrating an Ontological Reference Model to Assist the Pilot and Manage Deviations;impact of Conversational Agent Language and Text Structure on Student Language;Analyzing the Role of Generative AI in Fostering Self-directed learning through Structured Prompt engineering;detecting Function Inputs and Outputs for learning-Problem Generation in intelligent Tutoring Systems;automated Analysis of Algorithm Descriptions Quality, through Large Language Models;An AI-Learner Shared Control Model Design for Adaptive Practicing;early Math Skill as a Predictor for Foundational Literacy;explaining Problem Rec

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th international conference on learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. this paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. the proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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An OGC TrainingDML-AI approach for making EO training datasets ready in deep learning frameworks 11

An OGC TrainingDML-AI approach for making EO training datase...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Liu, Shuaiqi Yue, Peng Xu, Hanwen Jiang, Liangcun Liu, Ruixiang Wuhan University School of Remote Sensing and Information Engineering Wuhan China Wuhan University of Technology School of Resources and Environmental Engineering Wuhan China

ISBN: (纸本)9798350303513

Training data (TD) are vital for artificial intelligence deep learning or machine learning (AI DL/ML) in remote sensing image interpretation. With the proliferation of a large number of Earth Observation (EO) datasets, the availability of a wide range of datasets has introduced challenges in ensuring the FAIR (Findable, Accessible, Interoperable, Reusable) use of training datasets. this paper proposes an approach that leverages the OGC Training data Markup Language for AI (TrainingDML-AI) standard to make training data ready to be consumable by existing DL frameworks. It presents a training data pipeline approach to integrate TD in DL. the approach enables the retrieval and transformation of training data for compatibility with existing deep learning frameworks. © 2023 IEEE.

关键词： Pipelines

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Investigating Domain Bias in NILM 24

Investigating Domain Bias in NILM

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11th ACM international conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2024

作者： Breyer, Justus Jauhari, Sparsh Glebke, René Alizai, Muhammad Hamad Stroot, Markus Wehrle, Klaus Communication and Distributed Systems RWTH Aachen University Aachen Germany SBA School of Science and Engineering LUMS Lahore Pakistan IAEW RWTH Aachen University Aachen Germany

ISBN: (纸本)9798400707063

Enhancing household energy efficiency is crucial, and Non-intrusive Load Monitoring (NILM) offers a valuable solution by giving consumers insights into their energy use without individual device monitoring. However, the deployment of NILM models in new settings is challenging due to their training on domain-specific data. To effectively use public data for training NILM models for identifying individual appliances, understanding the challenges of model transfer is crucial. this study explores several factors that could hinder successful model transfer and highlights the challenges in broader NILM system deployment. We developed and tested various NILM models, both event-based and eventless, across multiple household domains and found that domain bias, e.g., noise and line frequency, does not significantly impact model performance. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Koopman-Based data-Driven Techniques for Adaptive Cruise Control System Identification 27

Koopman-Based Data-Driven Techniques for Adaptive Cruise Con...

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27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Meng, Yiming Li, Hangyu Ornik, Melkior Li, Xiaopeng University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States University of Illinois Urbana-Champaign Coordinated Science Laboratory UrbanaIL61801 United States University of Illinois Urbana-Champaign Department of Aerospace Engineering UrbanaIL61801 United States

ISBN: (纸本)9798331505929

Accurately identifying the intrinsic model of Adaptive Cruise Control has the potential to enhance the prediction of automated car-following behavior, helping vehicles' decision-making and contributing to safer and more efficient traffic flows. Moreover, white box models offer an analytical base for evaluating the impact of automated driving functions on macroscopic traffic dynamics, consequently aiding the management of the whole intelligent transportation system. Many existing system identification techniques have been applied to automated vehicles. However, most of these studies focus on identifying parameters for models of a fixed prototype. their reliance on accurate estimation of state time derivatives prevents their real applications, challenged by low sampling rates, noisy measurements, and limited observation periods. In contrast, the Koopman operator learning framework presents a promising improvement that can identify the nonlinear evolutionary properties of continuous-time systems. In this study, we apply Koopman-based methods to data driven Adaptive Cruise Control model identification. Additionally, as the challenge remains in establishing a practical relationship between identification accuracy and sampling rate, we numerically compared the performance of three Koopman-based learning frameworks, finite-difference, Koopman-logarithm, and a newly devised resolvent-type method, with that of a commonly used offline simulation-based batch optimization approach. We introduce a novel modification to the resolvent-type method, and the experimental results demonstrate its state of the art performance, particularly in identifying the potential existence of parametric noise at lower sampling rates. © 2024 IEEE.

关键词： Continuous time systems

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

11th International Conference on Signal and Information Proc...

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

ISBN: (纸本)9789819721191

the proceedings contain 126 papers. the special focus in this conference is on Signal and Information Processing, Network and Computers. the topics include: A Hybrid Strategy Whale Optimization Algorithm for Edge Computing Task Scheduling;screening of Syndrome Elements and Construction of Diagnostic Model of Hyperactivity of Liver-Yang Syndrome Based on Machine learning;TDOA-Based Autonomous Positioning Method for Smart IoT Services;study on the Coupling of Higher Education and Artificial Intelligence from the Perspective of Organizational Ecology;research on the Cultivating Critical thinking through Analysis of Process Errors;MEC data Offloading Strategy for UPF Sinking in 5G Core Network;an Optical Detection Method Based on Adaptive Filtering for Noise Suppression;design and Application of Big data Analysis Management Platform of Coalbed Gas;Statistical Analysis of IPTV Start-Up in China;MPS System Verification Based on Digital Twins intelligent Technology;map Generation Scheme Based on Nodes Communication Delay;a Review of Research on Big data Technology;FPGA Implementation of Pulse Compression in Time Domain;A CMOS Multisegmented Bias Circuit for Temperature Variation Compensation of RF Amplifier;Situation Analysis and Countermeasures of Rural Revitalization Promoted by Artificial Intelligence Based on SWOT Model;Research on Wastewater Quality Prediction Method Based on DBSCAN Algorithm and Conv1D-LSTM Model;specialty Construction Under the Guidance of Curriculum Ideology and Politics;a Multi-constraint Attitude Control Scheme for Micro-nano Satellite During On-Orbit Standby;multi-level Information Flow Design of Fire Detection System;An Improved PSO Algorithm for Hypersonic Vehicle Cruise Trajectory Planning;technical Implementation of X-ray Pulsar Navigation Observation and Project Management Experience Exploration.

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A Compliance-Enhancing Approach to Separated Continuous Auditing of intelligent Endpoints Security in War Potential Network Based on Location-Sensitive Hashing 1

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13th international conference on Computer engineering and Networks (CENet)

作者： Zhang, Hanrui Huang, Chenrong Lyu, Andrew Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210000 Peoples R China Nanjing Inst Technol Sch Informat & Commun Engn Nanjing 210000 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 210000 Peoples R China Univ York Heslington England

ISBN: (数字)9789819992478

ISBN: (纸本)9789819992492;9789819992478;9789819992461

the War Potential Network (WPN) is critical infrastructure determining national security. With the recent trend of increasingly tense international situation, frequent occurrences of cyber-attacks, and the proliferation of new intelligent endpoint devices in WPN, the importance of Continuous Auditing (CA) for intelligent endpoints in WPN has become increasingly significant. Several researches have focused on the accuracy of CA. However, the information in WPN intelligent endpoint devices might have sensitive information. Some laws require computer systems to not disclose data containing national secrets, while certain legal regulations demand the protection of personal privacy. In order to meet compliance requirements, specific technologies have to be implemented in CA, while there are existing research gaps in this field. To fill the gap, this research proposed a compliance-enhancing approach based on Locality-Sensitive Hashing (LSH) and clustering method to enhance compliance in CA. In this approach, auditing nodes gathers encoded data which cannot be read by human, while can be analyzed by algorithms to conduct CA. To quantitatively evaluate this approach, this research also introduced an inference attacking method in WPN scenario as threat model. the research also evaluated the influence of the capability of the auditing object and the correctness of the auditing result, to prove our compliance-enhancing approach can achieve relatively good performance in different evaluation dimensions.

关键词： War potential network LSH Machine learning Cybersecurity Separated continuous auditing

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Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems 4

Cognitive Twins for Predictive Maintenance and Security in I...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Swamy, B. Venkata Barmola, Pareshwar Prasad thangavel, Senthil Kaliappan, S. Patel, Hardik Abhyankar, Girish B V Raju Institute of Technology Department of Chemistry Medak India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Paypal Inc CA United States Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

the increasing complexity and interconnectedness of Internet of things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. this paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. the framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. the framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%. © 2024 IEEE.

关键词： Reinforcement learning

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Detecting Abnormal Record in Automotive Maintenance data Based on Deep Sparse Auto-Encoder 11

Detecting Abnormal Record in Automotive Maintenance Data Bas...

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11th international conference on Electrical and Electronics engineering, ICEEE 2024

作者： Chen, Fanghua Zhou, Gang Geng, Xin Zhang, Jinrui Beijing University of Technology Faculty of Materials and Manufacturing Beijing China Research Institute of Highway Ministry of Transport Beijing China Henan Transportation Development Center Henan Zhengzhou China

ISBN: (纸本)9798350362541

With the rapid development of automotive usage and maintenance services, the expansion of the automotive maintenance data scale inevitably brings about abnormal records,these records raise serious problems for the automotive maintenance industries. this paper proposes a new approach based on the deep sparse auto-encoder to deal with huge amounts of automotive maintenance data in reality. the failure description, maintenance project, and maintenance parts in the data are firstly vectorized using advanced text representation algorithms. Besides the measurement information in the original data, the obtained dataset has more actual semantic information to enhance the data correlation. the dataset is then pre-processed with normalization to improve the performance of the learning model.A developed deep sparse auto-encoder (DSAE) detection method is finally arranged for abnormal automotive maintenance records. the first layer of the deep sparse auto-encoder is adapted with a sparse suppression to match the dataset features. the results show that the developed deep sparse auto-encoder model can correctly identify the abnormal records, which achieves accuracy, precision, F1-Score, recall, and ROC-AUC as 99.2%, 99.9%, 97.4%, 99.6%, and 97.6%, respectively. the proposed model is further compared with other benchmark methods, the developed deep sparse auto-encoder model performs better in terms of handling the abnormal automotive maintenance data and accurate detection. © 2024 IEEE.

关键词： Condition based maintenance

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Hybrid Deep learning Network for Attack Detection in Cloud Environment Using Bot-IoT dataset 4

Hybrid Deep Learning Network for Attack Detection in Cloud E...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Arunprakash, T. Shree Venkateshwara Hi - Tech Engineering College Faculty of Computer Science and Engineering Tamilnadu Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

the rapid development of new emerging computing technologies has prompted many enterprises to outsource their data and computational needs. Such services must always adhere to safety standards that include privacy, accessibility, and integrity;therefore, an extremely secure infrastructure is among the most critical features of cloud computing environments. By comprehending malware activity across its entire behavioral spectrum, conventional security measures are significantly surpassed. A hybrid Deep learning (DL) model, Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), is proposed for detecting attacks in cloud data. the BoT-IoT dataset is used to validate the solution by characterizing various types of malicious behaviors. the proposed model is compared with traditional Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models in terms of performance reliability and execution time. Achieving a maximum accuracy of 98.8% and detecting attacks in just 11 seconds, the hybrid DL model demonstrates promising potential for enhancing security monitoring and identifying malicious behavior. © 2024 IEEE.

关键词： Bot (Internet)

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