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Preliminary Design for Reference tracking feedback for Medical Cyber-Physical Pharmacokinetic Systems 26

Preliminary Design for Reference tracking feedback for Medic...

2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Oliveira, Pedro M. Bustos, Daniel Lobo, Rodolfo Lacerda, Marcio J. Palma, Jonathan M. Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Universidad Católica Del Maule Dept. of Translational Medicine Talca Chile Universidad Diego Portales Ingenieria en Telecomunicaciones e Informática Santiago Chile London Metropolitan University Centre for Communications Technology School of Computing and Digital Media London United Kingdom Universidad de Talca Faculty of Engineering Curicó Chile

ISBN: (纸本)9798350378115

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method. © 2024 IEEE.

关键词： State feedback

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Language-specific Characteristic Assistance for Code-switching Speech Recognition

arXiv

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arXiv 2022年

作者： Song, Tongtong Xu, Qiang Ge, Meng Wang, Longbiao Shi, Hao Lv, Yongjie Lin, Yuqin Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Graduate School of Informatics Kyoto University Sakyo-ku Kyoto Japan Japan Advanced Institute of Science and Technology Ishikawa Japan

Dual-encoder structure successfully utilizes two language-specific encoders (LSEs) for code-switching speech recognition. Because LSEs are initialized by two pre-trained language-specific models (LSMs), the dual-encoder structure can exploit sufficient monolingual data and capture the individual language attributes. However, most existing methods have no language constraints on LSEs and underutilize language-specific knowledge of LSMs. In this paper, we propose a language-specific characteristic assistance (LSCA) method to mitigate the above problems. Specifically, during training, we introduce two language-specific losses as language constraints and generate corresponding language-specific targets for them. During decoding, we take the decoding abilities of LSMs into account by combining the output probabilities of two LSMs and the mixture model to obtain the final predictions. Experiments show that either the training or decoding method of LSCA can improve the model's performance. Furthermore, the best result can obtain up to 15.4% relative error reduction on the code-switching test set by combining the training and decoding methods of LSCA. Moreover, the system can process code-switching speech recognition tasks well without extra shared parameters or even retraining based on two pre-trained LSMs by using our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

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Physical Review B 2019年第20期100卷 205411-205411页

作者： V. Torres P. Silva E. A. T. de Souza L. A. Silva D. A. Bahamon Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn 24210-340 RJ-Brazil MackGraphe – Graphene and Nano-Materials Research Center Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil School of Computing and Informatics & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil

The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort.

关键词： Ballistic transport Quantum transport Strain Graphene Landauer formula Machine learning Tight-binding model

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An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting

An Explainable Machine Learning-Based Phishing Website Detec...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Rizal Dwi Prayogo Alvin Rizqi Alfisyahrin Windy Gambetta Siti Amatullah Karimah Hidetaka Nambo Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan School of Computing Telkom University Bandung Indonesia Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Kanazawa Japan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

As the need for achieving optimal performance in prediction models, several recent and complex models have been developed. However, many of these models operate as black boxes, providing little insight into their predictive results. In recent years, three gradient-boosting methods based on Decision Trees have been recommended, i.e., XGBoost, CatBoost, and LightGBM. These methods have proven to deliver competitive performance with fast training times. Nevertheless, in critical domains like security, there is a growing need for increased transparency among stakeholders. One pressing concern in the security field is the proliferation of phishing websites. To address this issue, we propose an explainable machine learning-based approach using gradient-boosting methods with hyperparameter optimization on three phishing website datasets. Our best methods surpass the state-of-the-art phishing website detection methods, achieving accuracy rates of 97.45%, 99.16%, and $\mathbf{9 7. 8 5 \%}$ for the UCI (2015), Mendeley (2018), and Mendeley (2020) datasets, respectively. Subsequently, we implement posthoc explainability using SHAP and LIME for the selected dataset. The experimental results indicate that three features, i.e., length_url, directory_length, and time_domain_activation, are consistently identified as the most influential features in the dataset. Moreover, our proposed approach demonstrates promising results for detecting phishing websites with both high accuracy and explainability.

关键词： Training Technological innovation Accuracy Machine learning algorithms Phishing Closed box Predictive models Classification algorithms Security Tuning

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Communicating with multiple users for embodied conversational agents

Communicating with multiple users for embodied conversationa...

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International Conference on Telecommunications (ConTEL)

作者： H.H. Huang A. Cerekovic T. Furukawa Y. Yamaoka H. Ohashi I. Pandzic Y. Nakano T. Nishida Graduate School of Informatics Kyoto University Japan Paculty of Electrical Engineering and Computing University of Zagreb Croatia Department of Computer Information and Communication Sciences Tokyo University of Agriculture and Technology Japan Department of Computer and Information Science Seikei University Japan

One of the most critical reason that prevents embodied conversational agents to be used in practical applications can be considered as the lack of the capabilities to be aware of multiple users and to interact with them. This paper presents our investigations in an advanced tour guide agent system serving two users and a simpler quiz agent kiosk which is developed for public exhibitions. To improve the life likeness of the agents, we propose that the agent should be attentive to its users. To achieve the agent's attentiveness toward the users, two aspects are considered: its utterance strategy toward the users and its internal attitude should be adaptive to users' status. To explore these two aspects more throughly, two prototype systems are implemented and evaluated with subject experiments respectively. In addition to regular questionnaires evaluation, GNAT test that measures implicit preference was conducted as well. The preliminary results implied that the experiment participants perceived that the agents are attentive and natural.

关键词： Testing Humans Laboratories informatics Agriculture Information science Application software Prototypes Robustness Working environment noise

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A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

A Heuristic Rule Reduction Approach to Software Fault-pronen...

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Asia-Pacific Conference on Software engineering

作者： Akito Monden Jacky Keung Shuji Morisaki Yasutaka Kamei Ken-Ichi Matsumoto Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara Japan Department of Computing Hong Kong Polytechnic University Hong Kong China Faculty of Informatics Shizuoka University Hamamatsu Shizuoka Japan Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only. 015 (from. 757 down to. 742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from. 426 to. 441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

关键词： Measurement Association rules Explosions Software Predictive models Educational institutions

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Systolic Array for Parallel Solution of the Robust Kalman Filter Used for Attitude and Position Estimations in UAVs

Systolic Array for Parallel Solution of the Robust Kalman Fi...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leandro José Evilásio Campos Marco Henrique Terra Ricardo Menotti Roberto Santos Inoue Graduate Program in Electrical Engineering Federal University of São Carlos São Carlos SP Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos SP Brazil Department of Computing Federal University of São Carlos

The efficient Kalman filter has been widely used in recent decades to obtain air navigation information in UAVs. However, for a good performance of the Kalman filter, the model that describes the system dynamics must not contain uncertainties. This paper presents the implementation of a robust Kalman filter to estimate the attitude, velocity, and position of UAVs. The robust filter considers uncertainties in the sensor models. A mathematical structure based on the solution of linear systems synthesizes the predictor-corrector robust estimation algorithm. The main contribution of this study is the proposed QR decomposition based on Givens rotation to solve the linear system. The simulated experiments used sensory data collected in Zürich-Switzerland and ground truth referencing attitude, velocity, and position. The offline simulation results express the effectiveness of the robust Kalman filter for this application, with a reduction of up to 18.9% in the estimation error, in relation to the standard Kalman filter. The proposal to use systolic arrays for numerical solutions has shown promise for implementation in parallel processing platforms, such as FPGAs.

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A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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