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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： An, Zizhen Deng, Mingcong Tokyo University of Agriculture and Technology Graduate School of Engineering Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350366204

In this note, a new structure of Right Coprime Factorization (RCF) for nonlinear systems with uncertainty has been proposed based on a time-varying Bezout identity. This is inspired from the concept of dilation from homogeneous theory. However, this time-varying dilation in right coprime factorized uncertain nonlinear systems is not limited to homogeneous plants but is integrated into Bezout identity so as to ensure the bounded input bounded output (BIBO) stability of uncertain systems. Additionally, simulation results of two numerical examples are exhibited to validate the proposition. © 2024 IEEE.

关键词： Nonlinear simulations

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Design of an Automatic Price Tag System for Web-Based Retail Business 9

Design of an Automatic Price Tag System for Web-Based Retail...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

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A Comparative Study of DeepLabCut and Other Open-Source Pupillometry Data Analysis Algorithms – Which to Choose?

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Machine Graphics and Vision 2024年第2期33卷 77-90页

作者： Badkul, Amitesh Mishra, Sonakshi Kommajosyula, Srinivasa P. Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Department of Pharmacy Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Ph.D. Program in Computer Science The Graduate Center The City University of New York New YorkNY United States

Pupillometry measures pupil size, and several open-source algorithms are available to analyse pupillometry data. However, only a few studies compared these algorithms’ accuracy and computational resources. This study aims to compare the accuracy of computer vision-based algorithms (Swirski, Starburst, PuRe, ElSe, ExCuSe algorithms) and the machine learning algorithm, DeepLabCut, to the double-blinded human examiners (gold-standard). Training of DeepLabCut with different architectures and a variable number of markers (2-9 markers) was done on an open-source dataset. The duration of training was statistically longer for the ResNet152 model compared to the MobileNet model. The pupil diameters in computer vision-based software such as PuRe, Starburst, and Swirski were statistically different from human measurements. MobileNet 2 and 3 marker models were the closest to the human measurements. In conclusion, this work highlights the efficiency of lower marker models based on MobileNet architecture in DeepLabCut, which consumes fewer computational resources and is more accurate. © 2024 Institute of Information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Adversarial machine learning

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Fractional Linear Matroid Matching Is in Quasi-NC 32

Fractional Linear Matroid Matching Is in Quasi-NC

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Gurjar, Rohit Oki, Taihei Raj, Roshan Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9783959773386

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma (2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients. © Rohit Gurjar, Taihei Oki, and Roshan Raj;licensed under Creative Commons License CC-BY 4.0.

关键词： Parallel algorithms

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Hoax Detection in Social Media: A Literature Review 4

Hoax Detection in Social Media: A Literature Review

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Saputra, Carsten Sebastian Wijaya, Norbert Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news. © 2023 IEEE.

关键词： Digital storage

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Reliable deep learning in anomalous diffusion against out-of-distribution dynamics

NATURE COMPUTATIONAL SCIENCE

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NATURE COMPUTATIONAL science 2024年第11期4卷 877-877页

作者： Feng, Xiaochen Sha, Hao Zhang, Yongbing Su, Yaoquan Liu, Shuai Jiang, Yuan Hou, Shangguo Han, Sanyang Ji, Xiangyang School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Shenzhen International Graduate School Tsinghua University Shenzhen China Institute of Systems and Physical Biology Shenzhen Bay Laboratory Shenzhen China Department of Automation Tsinghua University Beijing China

Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.

关键词： Silver nanoparticles

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CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

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Multimedia Tools and Applications 2024年第39期83卷 86557-86591页

作者： Geetha, R. Jegatheesan, A. Dhanaraj, Rajesh Kumar Vijayalakshmi, K. Nayyar, Anand Arulkumar, V. Velmurugan, J. Thavasimuthu, Rajendran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Pune India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77% accuracy, 99.92% specificity, 99.60% precision, 99.81% detection rate, and a 99.72% F1-score in the CIC IDS dataset, and 99.68% accuracy, 99.30% precision, 99.83% specificity, 99.11% detection rate, and a 99.21% F1-score in evaluating the BoT-IoT dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Efficient Expressive Attribute-Based Encryption with Keyword Search over Prime-Order Groups

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Intelligent Automation & Soft Computing 2023年第3期35卷 2737-2754页

作者： Qing Miao Lan Guo Yang Lu Zhongqi Wang School of Computer and Electronic Information Nanjing Normal UniversityNanjing210046China Graduate School of Science and Technology University of Tsukuba TsukubaIbaraki305-8577Japan

Attribute-based encryption with keyword search(ABEKS)is a novel cryptographic paradigm that can be used to implementﬁne-grained access control and retrieve ciphertexts without disclosing the sensitive *** is a perfect combination of attribute-based encryption(ABE)and public key encryption with keyword search(PEKS).Nevertheless,most of the existing ABEKS schemes have limited search capabilities and only support single or simple conjunctive keyword *** to the weak search capability and inaccurate search results,it is difﬁcult to apply these schemes to practical *** this paper,an efﬁ-cient expressive ABEKS(EABEKS)scheme supporting unbounded keyword uni-verse over prime-order groups is designed,which supplies the expressive keyword search function supporting the logical connectives of“AND”and“OR”.The proposed scheme not only leads to low computation and communica-tion costs,but also supports unbounded keyword *** the standard model,the scheme is proven to be secure under the chosen keyword attack and the cho-sen plaintext *** comparison analysis and experimental results show that it has better performance than the existing EABEKS schemes in the storage,com-putation and communication costs.

关键词： Searchable encryption expressive keyword search attribute-based encryption unbounded keyword universe prime-order group

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Applications of Artificial Intelligence in Human Resource Management: Theoretical Framework and Future Research Directions 4

Applications of Artificial Intelligence in Human Resource Ma...

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4th International Conference on computer science and Management technology, ICCSMT 2023

作者： Zhang, Xuming Wang, Shilong Peng, Ling School of Management Guangdong University of Science and Technology Dongguan523083 China Graduate School of Computer Science and Technology Harbin Institute of Technology Harbin150000 China

ISBN: (纸本)9798400709517

With the rapid advancement of artificial intelligence (AI) technologies, their incorporation into human resource management (HRM) has grown increasingly prevalent. AI tools have substantially enhanced the precision of talent acquisition, predictive performance analytics, and the formulation of responsive HR strategies and human resource management (HRM). However, challenges persist, including data privacy concerns, potential biases in AI algorithms, and retaining a human-centric approach amidst automation. This paper delves into the diverse applications of AI within HRM, encompassing areas such as recruitment screening, employee engagement analysis, and forward-thinking HR planning. Looking ahead, organizations must deeply contemplate the ethical implications of these technologies, endeavoring to design transparent, accountable, and unbiased AI systems while striking a balance between technological efficiency and human intuition and judgment. © 2023 ACM.

关键词： Human resource management

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