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1st International Symposium on Intelligent computing and Networking, ISICN 2024

作者： Bolaños, César Bollman, Dorothy Doctoral Program in Computing and Information Sciences and Engineering University of Puerto Rico at Mayagüez Mayagüez00680 Puerto Rico Department of Mathematical Sciences University of Puerto Rico at Mayagüez Mayagüez00680 Puerto Rico

ISBN: (纸本)9783031674464

We construct new families of multidimensional periodic arrays that can be used for digital watermarking videos and images. Each member of each family is composed of columns of cyclic shifts of a Sidelnikov sequence in which the shifts are given by a multidimensional m1×m2×…×mn "grid" that is determined by applying a logarithmic quadratic map to the elements of a direct product Zm1×Zm2×…×Zmn. These arrays have peak auto-correlation on the order of p2l and non-peak auto- and cross-correlation on the order of pl, where l≥2 and m1m2…mn=pl-1, thereby improving the peak auto-correlation to nonpeak auto- and cross-correlations of other known constructions. Additionally, we show that the low cross-correlation values are optimal with respect to the Welch bound. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Watermarking

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Classification of Multivariate Time Series Signals Using Self-Supervised Representation Learning for Condition Monitoring 30

Classification of Multivariate Time Series Signals Using Sel...

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30th International Symposium on Semiconductor Manufacturing, ISSM 2024

作者： Sumiya, Koichi Arima, Sumika University of Tsukuba Doctoral Program in Policy and Planning Sciences Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan

ISBN: (纸本)9798331510589

In the analysis of real-world data, two significant challenges often arise: high-dimensional signals and their temporal interactions. To address these issues and identify transitions in process conditions through end-to-end learning, we propose a self-supervised representation learning framework that conceptualizes signals as images. This straightforward approach classifies imaged signals as time-specific conditions by maximizing mutual information within a domain-specific feature space. We applied this methodology to two labeled open datasets and one unlabeled real-world process dataset, yielding promising results. © 2024 IEEE.

关键词： Self-supervised learning

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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

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Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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A Player Position Tracking Method Based on a Wide-Area Pan-Tilt-Zoom Video 12

A Player Position Tracking Method Based on a Wide-Area Pan-T...

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Proceedings of the 12th International Conference on Sport sciences Research and Technology Support, icSPORTS 2024

作者： Yamagishi, Shunzo Xie, Chun Shishido, Hidehiko Kitahara, Itaru Master's and Doctoral Program in Intelligent Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Center for Computational Sciences University of Tsukuba Ibaraki Japan Faculty of Science and Engineering Department of Information Systems Engineering Soka University Tokyo Japan

ISBN: (纸本)9789897587191

This paper proposes a method to estimate the posture of an athlete moving on a vast field in a sporting event using a pan-tilt-zoom camera. In order to estimate the posture of an athlete on a sports field from a dynamic video sequence, our method extracts image features to search correspondence points among successive frames and computes the homography transformation matrix that compensates for changes the camera parameters (e.g., angle of view and posture). The effectiveness of this method is qualitatively verified using MLB TV broadcast video. The accuracy and error factors are also quantitatively verified by CG simulation. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Pan-Tilt-Zoom Camera Player Tracking Sports Analytics Video Processing Wide-Area

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Comprehensive Evaluation of the Lakeside Café Point of Sale (POS) Application: A Synergistic Analysis Using SUS and EUCS Models 25

Comprehensive Evaluation of the Lakeside Café Point of Sale...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Wibowo, Jonathan Arya Yasirandi, Rahmat Utomo, Rio Guntur Pratama Anom, Rahmat Indra Telkom University School of Industrial Engineering Bandung Indonesia King Mongkut's Institute of Technology Ladkrabang Faculty of Information Technology Bangkok Thailand Telkom University School of Computing Bandung Indonesia Trisakti Institute of Tourism Doctoral Program of Tourism Jakarta Indonesia

ISBN: (纸本)9798350378573

Lakeside Café, located in the Telkom University, processes approximately 80-100 drink transactions daily. Having used their current point-of-sale (POS) application for over a year, an urgent evaluation of its performance and user satisfaction is necessary to ensure ongoing efficiency. This study employs the System Usability Scale (SUS) model and the Extended Unified Theory of Acceptance and Use of Technology (EUCS) model. This combined approach enhances the understanding of the application's strengths and identifies areas for improvement, forming a solid foundation for evaluating user satisfaction. The study reveals notable usability of the Lakeside Café POS application, evidenced by an SUS score of 78.8, ranking it as "ACCEPTABLE"and "GOOD."The EUCS model analysis further highlights potential enhancements, with the Content and Functionality dimensions scoring 3.5 and 3.65, respectively, indicating room for workflow process development. These findings emphasize the need for improved content delivery and strengthened application functionality to boost user satisfaction and maximize system performance. This research enriches the dialogue on technological acceptance, providing valuable insights for developers and stakeholders focused on advancing POS applications. The use of SUS and EUCS models offers a comprehensive evaluation method, fostering continuous advancements and innovations in digital solutions within the food and beverage sector. © 2024 IEEE.

关键词： Sales

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Technology-Enhanced Learning for User Security Awareness Using AI-based Naive RAG: A Design and Prototype

Technology-Enhanced Learning for User Security Awareness Usi...

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2025 International Conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Pratama, I. Putu Agus Eka Widyantara, I. Made Oka Linawati Gunantara, Nyoman Suakanto, Sinung Irawan, Eka Tresna Udayana University Doctoral Program of Engineering Sciences Faculty of Engineering Bali Badung Indonesia Udayana University Dept. of Electrical Engineering Faculty of Engineering Bali Badung Indonesia Telkom University Dept. of Information System West Java Bandung Indonesia Mayar International Pte. Ltd. Singapore

ISBN: (纸本)9798331513320

The increasing use of information Technology (IT) in all areas of life, followed by increasing cybersecurity threats on the user side, makes cybersecurity awareness among users an obligation that must be met. Several solutions currently available (guides, discussion forums, workshops) have not been able to provide good results and are lacking in terms of user engagement, adaptability, and optimizing available resources, so technology-based learning is needed, especially AI. This paper introduces the design and prototype of an AI-based learning system, especially with the Naive Retrieval-Augmented Generation (Naive RAG) method with three steps, including Retrieval, Augmented, and Generation. The system combines contextual user questions and answers with internal and domain knowledge systems to explain cybersecurity awareness to users dynamically and personally. The prototype that has been developed is then tested using the Black Box Testing method to evaluate functionality and usability. The test results show that the functionality of the system built has run well with a percentage of success of 100%, so it can be used as a technologybased learning medium for education and learning of user security awareness. © 2025 IEEE.

关键词： Adversarial machine learning

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 IEEE.

关键词： Deep learning

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Classification of Multivariate Time Series Signals Using Self-Supervised Representation Learning for Condition Monitoring

Classification of Multivariate Time Series Signals Using Sel...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Koichi Sumiya Sumika Arima Doctoral Program in Policy and Planning Sciences University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

关键词： Representation learning Furnaces Time series analysis Self-supervised learning Glass Medical services Semiconductor device manufacture Turning Wool Mutual information

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks 4

A Novel Blockchain-Based Strategy for Energy Conservation in...

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4th International Conference on Blockchain Technology and information Security, ICBCTIS 2024

作者： Sharma, Khemraj Goyal, Rohit Bhagat, Sukesh Kumar Agarwal, Sheenu Bisht, Girish Singh Hussien, Mohemmed Nanhua University Department of Business Administration Doctoral Program in Management Sciences Dalin62248 Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Uttaranchal University Department of Computer Science & Engineering Dehradun India Uttarakhand Technical University Department of Electronics and Communication Engineering Dehradun India Tula's Institute Dehradun India Department of Computer Science and Engineering Dehradun248001 India The Islamic University Najaf Iraq

ISBN: (纸本)9798331528324

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency. © 2024 IEEE.

关键词： Blockchain Network Network Communication security Wireless Sensor

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