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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Iinuma, Takahiro Ebato, Yudai Nobukawa, Sou Tran, Anh Tu Wagatsuma, Nobuhiko Inagaki, Keiichiro Doho, Hirotaka Yamanishi, Teruya Nishimura, Haruhiko Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Japan Toho University Faculty of Science Department of Information Science Chiba Japan Chubu University Department of Artificial Intelligence and Robotics Aichi Japan Kouchi University Faculty of Education Teacher Training Division Kochi Japan Osaka Seikei University Faculty of Data Science Osaka Japan Yamato University Faculty of Informatics Osaka Japan

ISBN: (纸本)9798350337020

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications. © 2023 IEEE.

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare 2

Realizing the Potential of Metaverse to Revolutionize the Fu...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ishtaiwi, Abdelraouf Hadi, Wael Al-Qerem, Ahmad University Of Petra Department Of Data Science And Artificial Intelligence Amman Jordan University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350394962

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery. © 2024 IEEE.

关键词： Augmented reality

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A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

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Chinese Physics C 2024年第9期48卷 176-187页

作者： Ming-Xiang Fu Yu Song Jia-Meng Lv Liang Cao Peng Jia Nan Li Xiang-Ru Li Ji-Feng Liu A-Li Luo Bo Qiu Shi-Yin Shen Liang-Ping Tu Li-Li Wang Shou-Lin Wei Hai-Feng Yang Zhen-Ping Yi Zhi-Qiang Zou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Key lab of Space Astronomy and Technology National Astronomical ObservatoriesBeijing 100101China School of Computer Science South China Normal UniversityGuangzhou 510631China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China University of Chinese Academy of Sciences NanjingNanjing 211135China University of Science and Technology Beijing Beijing 100083China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Computer and Information Dezhou UniversityDezhou 253023China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 30024China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Nanjing University of Posts&Telecommunications Nanjing 210023China School of Science University of Science and Technology LiaoningAnshan 114051China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the ***,effectively analyzing this vast amount of data poses a significant *** response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate *** overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and *** the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images *** proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging *** particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 *** addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

关键词： artificial intelligence large vision model human-in-the-loop astronomy galaxies

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer 46

STFormer: Learning to Explore Spot Relationships for Spatial...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhan, Gan Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies. © 2024 IEEE.

关键词： WSI circuits

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on information Management and technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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Telematics Work Field Review Text Classification Using the Naïve Bayes Method

Telematics Work Field Review Text Classification Using the N...

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2022 IEEE Creative Communication and Innovative technology, ICCIT 2022

作者： Maesya, Aries Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Indonesia

ISBN: (纸本)9781665468763

Indonesia will benefit from a demographic boom in 2030 with a higher labor supply than in earlier decades. Then in industrial revolution 4.0 robotics and artificial intelligence will take the place of low-skilled or menial employment that don't require specialized expertise (AI). To aim research is Telematics Work Field Review Text Classification Using the Naïve Bayes Method. The method using Multinomial Naïve Bayes model which is trained to learn from patterns in training data set without being programmed explicitly. Then, based on the Term Frequency - Inverse Document Frequency, consider the weighting of the word used (TF-IDF). The text classification stage is then carried out using the multinominal nave bayes classification method with evaluation using the confusion matrix, following the acquisition of the TF-IDF value. In the study it took data with web crawling techniques on social media sites twitter. The data collected was 936 data consisting of 7,8% negative sentiments, 26,4% positive sentiments, and 65,8% neutrals. The results of accuracy testing using the Confusion Matrix. And from the results of such tests resulted in an accuracy of 66%, precision 73%, and recall 85%. © 2022 IEEE.

关键词： Telematics

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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2023 International Conference on Informatics, Multimedia, Cyber and information Systems, ICIMCIS 2023

作者： Yulianto, Semi Krisnanik, Erly Hartawan, Muhammad Syarif Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia University of Pembangunan Nasional Veteran Information System Department Jakarta12450 Indonesia University of Krisnadwipayana Faculty of Technology Jakarta13077 Indonesia Asia e University Ict Department Malaysia

ISBN: (纸本)9798350307306

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain. © 2023 IEEE.

关键词： Cybersecurity

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