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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Nowadays, stationary and locomotion activity recognition, also known as SLAR, is becoming increasingly important in a variety of domains, such as indoor localization, fitness activity tracking, and elderly care. Currently used methods typically involve handcrafted feature extraction, a process that is both difficult and requires specialized knowledge, and results can still be subpar. We proposed a deep learning technique for SLAR called LSTM-XGB that uses data from inertial sensors in smartphones to reduce the effort required for feature development and selection. The proposed LSTM-XGB consists of multiple stacked LSTM layers to automatically learn the temporal features of the input, followed by XGBoost for label prediction in the final layer. The results showed that the proposed LSTM-XGB technique, which automatically extracts features, outperforms conventional machine learning that requires manual feature extraction. We also showed that sensor data from three sensors (accelerometer, linear acceleration, and gyroscope) can be combined. This achieved higher accuracy than other combinations or single sensors. © 2022 IEEE.

关键词： Feature extraction

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Process Mining and Perceived Privacy Violations: A Pilot-Study 25

Process Mining and Perceived Privacy Violations: A Pilot-Stu...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Zuidema-Tempel, Evelyn Bukhsh, Faiza Allah Effing, Robin van Hillegersberg, Jos Research Group Digital Intelligence & Business Saxion University of Applied Sciences M. H. Tromplaan 28 Enschede Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Drienerlolaan 5 Enschede Netherlands Faculty of Behavioural Management and Social Sciences University of Twente Drienerlolaan 5 Enschede Netherlands

ISBN: (纸本)9789897586484

Despite the existence of various methods and abstraction techniques to reduce the privacy risk of process models generated by process mining algorithms, it is unclear how process mining stakeholders perceive privacy violations. In this pilot-study various process model visualisations were shown to 6 stakeholders of a travel expense claim process. While changing the abstraction levels of these visualisations, the stakeholders were asked whether they perceived a violation of their privacy. The results show that there are differences in how individual stakeholders perceive privacy violations of process models generated via process mining algorithms. Results differ per type of visualization, type of privacy risk reducing methods, changes of abstraction level and stakeholder role. To reduce the privacy risk, the interviewees suggested to include an authorization table in the process mining tool, communicate the goal of the analysis with all stakeholders, and validate the analysis with a privacy officer. It is suggested that future research focuses on discussing and validating process visualisations and privacy risk reducing methods and techniques with various process mining stakeholders in organisations. This is expected to reduce perceived violations and prevents developing techniques that are aimed at reducing privacy risk but are not considered as such by stakeholders. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Visualization

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Monitoring System of Wearable Sensor Signal in Rehabilitation Using Efficient Deep Learning Approaches 26

Monitoring System of Wearable Sensor Signal in Rehabilitatio...

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26th International computer science and engineering Conference, ICSEC 2022

作者： Jantawong, Ponnipa Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665491983

Recognition of human activity has utilized inputs from wearable sensors, which has significant implications for rehabilitative medicine and cognitive neuroscience. Unfortunately, some crucial dynamic data on upper-limb movements need to be included in the feature extraction procedure for wearable sensor data. The issue is that only a few rehabilitative motions can be recognized, and classification precision is readily compromised. We study several convolution neural networks to extract valuable characteristics from multichannel wearable sensor inputs automatically and precisely identify rehabilitation operations. We gathered wearable sensor signal data for six physiotherapy exercises to assess identification effectiveness using the SPARS9x standard rehabilitation dataset. Experiments showed that the PyramidNet18 model had the highest F1-score on the benchmark dataset, 99.15%. © 2022 IEEE.

关键词： Physical therapy

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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Graphical models for inference: A model comparison approach for analyzing bacterial conjugation

arXiv

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arXiv 2024年

作者： Kendal-Freedman, Nat Meleshko, Joseph Victor Fiorillo Yip, Aaron Ingalls, Brian Department of Applied Mathematics University of Waterloo WaterlooON Canada David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Department of Chemical Engineering University of Waterloo WaterlooON Canada

We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics. © 2024, CC BY.

关键词： Markov processes

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Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario 13

Refined LSTM Network for Sensor-based Human Activity Recogni...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Sensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognition is employed in surveillance systems, patient health monitoring, and many other systems involving the interaction between human and intelligent wearable devices, including smartphones and smartwatches. The primary objective of this study work is to identify human behavior in the actual world. We proposed an improved long short-term memory network called RLSTM that uses a squeeze-and-excitation module to efficiently identify human actions and enhance action identification systems' interpretation. A publicly available real-world dataset known as REALWORLD16 was used to train and validate the model five times to analyze the proposed network. The proposed RLSTM achieved the highest accuracy of 98.04% and F1-score of 97.76%, as determined by several investigations. © 2022 IEEE.

关键词： Wearable sensors

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Oriented matroids from triangulations of products of simplices

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Selecta Mathematica, New Series 2024年第3期30卷 47-47页

作者： Celaya, Marcel Loho, Georg Yuen, Chi Ho School of Mathematics Cardiff University Cardiff United Kingdom Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu City Taiwan

We introduce a construction of oriented matroids from a triangulation of a product of two simplices. For this, we use the structure of such a triangulation in terms of polyhedral matching fields. The oriented matroid is composed of compatible chirotopes on the cells in a matroid subdivision of the hypersimplex, which might be of independent interest. In particular, we generalize this using the language of matroids over hyperfields, which gives a new approach to construct matroids over hyperfields. A recurring theme in our work is that various tropical constructions can be extended beyond tropicalization with new formulations and proof methods. © The Author(s) 2024.

关键词： 05E45 12K99 14T15 52B40 52C40

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Vision-guided robotic automation of vat polymerization additive manufacturing production: design, calibration and verification

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Journal of Intelligent Manufacturing and Special Equipment 2023年第2期4卷 85-98页

作者： Wenzhen Yang Johan K. Crone Claus R. Lønkjær Macarena Mendez Ribo Shuo Shan Flavia Dalia Frumosu Dimitrios Papageorgiou Yu Liu Lazaros Nalpantidis Yang Zhang Department of Mechanical Engineering Technical University of Denmark Lyngby Denmark School of Mechanical Engineering Jiangnan University Wuxi China Department of Electrical Engineering Technical University of Denmark Lyngby Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Purpose This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process. Design/methodology/approach In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system. Findings By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process. Originality/value The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.

关键词： Vision-guided robotic system Automation Vat photopolymerization Soft tooling process

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Anomaly detection with variance stabilized density estimation 24

Anomaly detection with variance stabilized density estimatio...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Amit Rozner Barak Battash Henry Li Lior Wolf Ofir Lindenbaum Faculty of Engineering Bar Ilan University Ramat-Gan Israel Department of Applied Mathematics Yale University New Haven Connecticut School of Computer Science Tel Aviv University Tel-Aviv Israel

We propose a modified density estimation problem that is highly effective for detecting anomalies in tabular data. Our approach assumes that the density function is relatively stable (with lower variance) around normal samples. We have verified this hypothesis empirically using a wide range of real-world data. Then, we present a variance-stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples. To obtain a reliable anomaly detector, we introduce a spectral ensemble of autoregressive models for learning the variance-stabilized distribution. We have conducted an extensive benchmark with 52 datasets, demonstrating that our method leads to state-of-the-art results while alleviating the need for data-specific hyperparameter tuning. Finally, we have used an ablation study to demonstrate the importance of each of the proposed components, followed by a stability analysis evaluating the robustness of our model.

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Extending the Q-Score to an Application-Level Quantum Metric Framework

Extending the Q-Score to an Application-Level Quantum Metric...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Ward van der Schoot Robert Wezeman Niels Neumann Frank Phillipson Rob Kooij The Netherlands Organisation for Applied Scientific Research The Hague The Netherlands Maastricht University School of Business and Economics Maastricht The Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice. The great number of available quantum metrics and the different hardware technologies used to develop quantum computers complicate this evaluation. In addition, different computational paradigms implement quantum operations in different ways. A prominent quantum metric is given by the Q-score metric of Atos. This metric was originally introduced as a standalone way to benchmark devices using the Max-Cut problem. In this work, we show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers. To showcase the applicability of the framework, we showcase a second Q-score in this framework, called the Q-score Max-Clique. This yields, to our knowledge, the first application-level metric capable of natively comparing three different paradigms of quantum computing. This metric is evaluated on these computational quantum paradigms – quantum annealing, gate-based quantum computing, and photonic quantum computing – and the results are compared to those obtained by classical solvers.

关键词： Measurement Performance evaluation computers Quantum computing Annealing Benchmark testing Logic gates Quantum annealing Hardware Photonics

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