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Experimental Study of Situation-Adaptive Human-Automation Collaboration for Takeoff Safety

IFAC Proceedings Volumes 1998年第26期31卷 323-328页

作者： Makoto Itoh Yasuhiko Takae Toshiyuki Inagaki Neville Moray Institute of Information Sciences and the Center for TARA University of Tsukuba Tsukuba 305-8573 Japan Doctoral Program in Engineering University of Tsukuba Tsukuba 305-8573 Japan Department of Psychology University of Surrey Guildford Surrey GU2 5XH United Kingdom

This paper investigates usefulness of dynamic and flexible allocation of authority for decision and control during takeoffofan aircraft. Even though one of the authors' previous mathematical analysis claims that the authority should be exchanged between humans and automated systems depending on situations, it is not obvious whether human pilots are willing to accept the trading of authority. This paper givesan experimental study for the efficacy ofsuch situation-adaptive autonomy

关键词： Aircraft operations Automation Decision making Hurnan-centered design Human factors Man/machine systems Safety Supervisory control

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Transparent, low-autofluorescence microECoG device for simultaneous Ca2+ imaging and cortical electrophysiology in vivo

Transparent, low-autofluorescence microECoG device for simul...

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作者： Zátonyi, A. Madarász, M. Szabó, O. Lőrincz, T. Hodován, R. Rózsa, B. Fekete, Z. Research Group for Implantable Microsystems Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary Microsystems Laboratory Institute for Technical Physics and Material Sciences Centre for Energy Research HAS Budapest Hungary Doctoral School of Chemical Engineering and Material Sciences University of Pannonia Veszprém Hungary Laboratory of 3D Functional Network and Dendritic Imaging Institute of Experimental Medicine HAS Budapest Hungary János Szentágothai PhD Program Semmelweis University Budapest Hungary Roska Tamás Doctoral School of Sciences and Technology Pázmány Péter Catholic University Budapest Hungary

Objective. Multimodal neuroimaging approaches are beneficial to discover brain functionalities at high spatial and temporal resolution. In our work, a novel material composition of a microECoG device relying on Parylene HT and indium-tin-oxide (ITO) is presented, which facilitates two-photon imaging of Ca2+ signals and concurrent recording of cortical EEG. Approach. Long-term stability of the interfaces of the transparent microdevice is confirmed in vitro by electrochemical and mechanical tests. The outstanding optical properties, like high transmittance and low auto-fluorescent are proven by fluorimetric measurements. Spatial resolution of fluorescent two-photon imaging through the microECoG device is presented in transgenic hippocampal slices, while concurrent recording of Ca2+ signals and cortical EEG is demonstrated in vivo. Photoartefacts and photodegradation of the materials are also investigated in detail to provide safety guidelines for further use in two-photon in vivo imaging schemes. Main results. Two-photon imaging of Ca signals can be safely performed through the proposed transparent ECoG device, without significant distortion in the dimensions of detected neuronal structures or in the temporal signaling. In chronic use, we demonstrated that fluorescent Ca signals of individual neurons can be clearly recorded even after 51 d. Significance. Our results give a firm indication that highly transparent microECoG electrode arrays made of Parylene HT/ITO/Parylene HT multilayer are excellent candidates for synergetic recording of optical signals and EEG from intact brains with high resolution and are free of electrical and optical artefacts. © 2020 IOP Publishing Ltd.

关键词： Electrophysiology

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

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

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

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

ISBN: (数字)9798350378573

ISBN: (纸本)9798350378580

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.

关键词： Seminars Analytical models Technological innovation System performance Solids Stakeholders Beverage industry

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Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge computing

arXiv

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arXiv 2024年

作者： Moore, Ervin Imteaj, Ahmed Hossain, Md Zarif Rezapour, Shabnam Amini, M. Hadi MiamiFL33199 United States Center FIU School of Computing Southern Illinois University United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Enterprise and Logistics Engineering program at FIU

Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant’s data remains on the participants’ devices and only the local model generated utilizing the local computational power is transmitted throughout the database. However, the distributed computational nature of FL creates the necessity to develop a mechanism that can remotely trigger any network agents, track their activities, and prevent threats to the overall process posed by malicious participants. Particularly, the FL paradigm may become vulnerable due to an active attack from the network participants, called a poisonous attack. In such an attack, the malicious participant acts as a benign agent capable of affecting the global model quality by uploading an obfuscated poisoned local model update to the server. This paper presents a cross-device FL model that ensures trustworthiness, fairness, and authenticity in the underlying FL training process. We leverage trustworthiness by constructing a reputation-based trust model based on agents’ contributions toward model convergence. We ensure fairness by identifying and removing malicious agents from the training process through an outlier detection technique. Additionally, we establish authenticity by generating a token for each participating device through a distributed sensing mechanism and storing that unique token in a blockchain smart contract. Further, we insert the trust scores of all agents into a blockchain and validate their reputations using various consensus mechanisms that consider the computational task. © 2024, CC BY.

关键词： Federated learning

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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

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

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Muchamad Galih Anggara Ahmad Hindasyah Julfa Muhammad Amda A. A. Waskita 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: (数字)9798331542313

ISBN: (纸本)9798331542320

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.

关键词： Fungi Training Digital control Adaptation models Accuracy Microscopy Transformers Informatics Tuning Image classification

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Kernel-based pattern recognition hardware: its design methodology using evolved truth tables

Kernel-based pattern recognition hardware: its design method...

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NASA/DoD Workshop on Evolvable Hardware

作者： M. Yasunaga T. Nakamura J.H. Kim I. Yoshihara Institute of Information Sciences and Electronics. University of Tsukuba Tsukuba Ibaraki Japan Doctoral Program of Engineering University of Tsukuba Tsukuba Ibaraki Japan Center for Advanced Computer Studies University of Louisiana Lafayette LA USA Miyazaki University Miyazaki Japan

We propose a new logic circuit design methodology for kernel-based pattern recognition hardware using a genetic algorithm. In the proposed design methodology, pattern data are transformed into the truth tables and the truth tables are evolved to represent kernels in the discrimination functions for pattern recognition. The evolved truth tables are then synthesized to logic circuits. Because of this data direct implementation approach, no floating point numerical circuits are required and the intrinsic parallelism in the pattern data set is embedded into the circuits. Consequently, high speed recognition systems can be realized with acceptable small circuit size. We have applied this methodology to the image recognition and the sonar spectrum recognition tasks, and implemented them onto the newly developed FPGA-based reconfigurable pattern recognition board. The developed system demonstrates higher recognition accuracy and much faster processing speed than the conventional approaches.

关键词： Pattern recognition Hardware Design methodology Kernel Image recognition Electronic mail Genetic algorithms Circuit synthesis Sonar applications Genetic engineering

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Comparative Study of Deep Learning Methods through Improvement of Skin Image Quality of Basal Cell Carcinoma

Comparative Study of Deep Learning Methods through Improveme...

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International Seminar on Research of information Technology and Intelligent Systems (ISRITI)

作者： Ade Miftahul Jannah Kahlil Muchtar Maulisa Oktiana Aulia Rahman Yagus Cahyadi Novi Maulina Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Aceh Indonesia Dept. of Information System Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Indonesia Aceh Indonesia

Basal cell carcinoma (BCC) or commonly known as basal cell cancers is the most common non-melanoma skin disease in the world. BCC is the result of an interaction between a combination of genetic and environmental factors that are directly related to prolonged exposure to ultraviolet (UV) rays, such as the neck and head area. This study proposes to use the CLAHE (Contrast Limited Adaptive Histogram Equalization) method on skin images to improve image quality. CLAHE is an image quality improvement method that aims to obtain images with better contrast than the original image without reducing the quality of the image itself. The experimental results show that the CLAHE image are more accurate than the original image. For quantitative measurements, the performance is seen from the value of accuracy, precision, recall, specificity, and F-score. To be more specific, the experiments utilized the well-known public dataset called ISIC 2018 Dataset (for BCC image), while the normal skin used our own USK-Normal Skin dataset. The results for original image using VGG-16 architecture is 98,3%, ResNet-34 is 97,5%, EfficientNetB0 is 98,2%, and EfficientNet-B7 is 99,7%, respectively. Meanwhile, in the CLAHE image, the accuracy values obtained are higher, for the VGG-16 is 100%, ResNet-34 99.5%, EfficientNetB0 100% and EfficientNet-B7 100%, respectively. The normal skin dataset is available through this link: https://***/USK-NORMAL-SKIN-DATASETI.

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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