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arXiv 2019年

作者： Vargas, Danilo Vasconcellos Kotyan, Shashank Matsuki, Moe Faculty of Information Science and Electrical Engineering Kyushu University Japan Department of Computer Science and Engineering Dr. SPM IIIT-NR India Department of Applied Science for Integrated System Engineering Graduate School of Engineering Kyushu Institute of Technology University Japan

Neural networks have been shown vulnerable to adversarial samples. Slightly perturbed input images are able to change the classification of accurate models, showing that the representation learned is not as good as previously thought. To aid the development of better neural networks, it would be important to evaluate to what extent are current neural networks' representations capturing the existing features. Here we propose a test that can evaluate neural networks using a new type of zero-shot test, entitled Raw Zero-Shot. This test is based on the principle that some features are present on unknown classes and that unknown classes can be defined as a combination of previous learned features without learning bias. To evaluate the soft-labels of unknown classes, two metrics are proposed. One is based on clustering validation techniques (Davies-Bouldin Index) and the other is based on soft-label distance of a given correct soft-label. Experiments show that such metrics are in accordance with the robustness to adversarial attacks and might serve as a guidance to build better models as well as be used in loss functions to improve the models directly. Interestingly, the results suggests that dynamic routing networks such as CapsNet have better representation while some DNNs might be trading off representation quality for accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Performance comparison of partitioned and global approaches for weakly hard real-time systems

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IOP Conference Series: Materials science and engineering 2020年第1期884卷

作者： Habibah Ismail Dayang N A Jawawi Ismail Ahmedy Mohd Adham Isa Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Department of Computer System and Technology Faculty of Computer Science and Technology University of Malaya

One way to minimizing resource requirements is through the careful management and allocation, in example, scheduling. Research on weakly hard real-time scheduling on multiprocessor has been extremely limited; most prior research on real-time scheduling on weakly hard real-time has been confined to uniprocessors. The need for multiprocessor is due to issues that impose limits on the performance of a single processor. As real-time application systems increasingly come to be implemented upon multiprocessor environments, thus, this study applies multiprocessor scheduling approach for verification of weakly hard real-time tasks and to guaranteeing the timing requirements of the tasks. In fact, within the multiprocessor, the task allocation and migration problems seem even harder than in uniprocessor case; thus, in order to cater that problem, the sufficient and efficient scheduling algorithm supported by accurate schedulability analysis technique is present to provide weakly hard real-time guarantees. The proposed approach involves the two existing multiprocessor real-time scheduling algorithms combining with the hyperperiod analysis and deadline models; weakly hard constraints and μ-pattern. In this paper, the Matlab simulation tool is used in order to validate the result of analysis. From the experimental and performance evaluation results, it proved that the proposed approach is satisfied the tasks deadlines with less number of misses.

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Noise bias compensation for reconstructed HDR images in two layer coding

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

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Boundary value analysis testing on augmented reality of indonesian fruit recognition at mekarsari tourist park using cloud method on android mobile devices

Boundary value analysis testing on augmented reality of indo...

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International Conference on Information system, computer science and engineering 2018, ICONISCSE 2018

作者： Dewi Agushinta, R. Hustinawaty Jatnika, Ihsan Medyawati, Henny Dept. of Information System Faculty of Computer Science and Information Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia Dept. of Informatics Engineering Faculty of Industrial Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia

Software testing is a process of implementing a program with the aim of finding an error. A good test case is if the test has the possibility of finding an uncovered error. A successful test is if the test finds an error that was not initially found. One of the testing types available is black box testing. This paper proposes testing using black box testing technique. The black box testing method consists of several ways including equivalence partitioning, boundary value analysis, comparison testing, sample testing, robustness testing, and others. Among the many methods of testing, Boundary Value Analysis was chosen in this study. Boundary Value Analysis is a method of testing by determining the value of the lower limit and upper limit of the data that will be tested. This test is performed on the functions of Augmented Reality prototype of Indonesia fruit recognition by using the cloud method on Android mobile devices. From testing the distance of marker objects to Android mobile devices on cloud recognition using an Android camera shows that the higher the augmentable rating of the target image and the more the number of markers features detected, the easier the image will be tracked by AR. If the distance between the camera and the real object gets farther away, then the virtual object cannot be displayed. Testing with mobile devices using HSDPA, 3G and WIFI networks connecting a cloud database server to display virtual objects shows the average results of devices that use WIFI networks provide the fastest performance. © Published under licence by IOP Publishing Ltd.

关键词： Testing

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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and Computing 2022年 vii-viii页

作者： Johri, Prashant Diván, Mario José Khanam, Ruqaiya Marciszack, Marcelo Will, Adrián Yamuna Expressway School of Computing Science & Engineering Galgotias University G.B.Nagar Plot No 2 Sector 17 A Greater Noida Uttar Pradesh India Computer Science and Engineering Department SET Sharda University Greater Noida Uttar Pradesh India Information System Department National Technological University-Tucumán Regional Faculty Tucumán Tucumán Argentina Data Science Research Group at Economy School National University of La Pampa Santa Rosa Argentina National Technological University-Córdoba Regional Faculty Córdoba Argentina

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Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

arXiv

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arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Author Correction: On the longevity and inherent hermeticity of silicon-ICs: evaluation of bare-die and PDMS-coated ICs after accelerated aging and implantation studies

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Nature communications 2025年第1期16卷 1907页

作者： Kambiz Nanbakhsh Ahmad Shah Idil Callum Lamont Csaba Dücső Ömer Can Akgun Domonkos Horváth Kinga Tóth Domokos Meszéna István Ulbert Federico Mazza Timothy G Constandinou Wouter Serdijn Anne Vanhoestenberghe Nick Donaldson Vasiliki Giagka Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands. Department of Medical Physics and Biomedical Engineering University College London London UK. Department of Electrical & Electronic Engineering Imperial College London London UK. UK Dementia Research Institute Care Research and Technology Centre London UK. Mint Neurotechnologies Ltd London UK. Centre for Energy Research HUN-REN Budapest Hungary. Nikhef - Dutch National Institute for Subatomic Physics Amsterdam the Netherlands. Research Centre for Natural Sciences Institute of Cognitive Neuroscience and Psychology HUN-REN Budapest Hungary. Pazmany Peter Catholic University Faculty of Information Technology and Bionics Budapest Hungary. Department of Neuroscience Erasmus Medical Center Rotterdam The Netherlands. School of Biomedical Engineering & Imaging Sciences King's College London London UK. Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands. v.giagka@tudelft.nl. Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany. v.giagka@tudelft.nl.

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HARNESSING ARTIFICIAL INTELLIGENCE TO INFER NOVEL SPATIAL BIOMARKERS FOR THE DIAGNOSIS OF EOSINOPHILIC ESOPHAGITIS

arXiv

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arXiv 2022年

作者： Larey, Ariel Aknin, Eliel Daniel, Nati Osswald, Garrett A. Caldwell, Julie M. Arva, Nicoleta C. Rochman, Mark Wasserman, Tanya Collins, Margaret H. Yang, Guang-Yu Rothenberg, Marc E. Savir, Yonatan Department of Computer Science Technion IIT Haifa3200003 Israel Department of Industrial Engineering Technion IIT Haifa3200003 Israel Department of Physiology Biophysics and System Biology Faculty of Medicine Technion IIT Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology Cincinnati Children's Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States Department of Pathology and Laboratory Medicine Ann and Robert H. Lurie Children's Hospital of Chicago Feinberg School of Medicine Northwestern University ChicagoIL United States Department of Pathology Northwestern University Feinberg School of Medicine ChicagoIL United States

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies, a somewhat subjective task that is time-consuming, thus reducing the ability to process the complex tissue structure. Previous artificial intelligence (AI) approaches that aimed to improve histology-based diagnosis focused on recapitulating identification and quantification of the area of maximal eosinophil density. However, this metric does not account for the distribution of eosinophils or other histological features, over the whole slide image. Here, we developed an artificial intelligence platform that infers local and spatial biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions. Besides the maximal density of eosinophils (referred to as Peak Eosinophil Count [PEC]) and a maximal basal zone fraction, we identify two additional metrics that reflect the distribution of eosinophils and basal zone fractions. This approach enables a decision support system that predicts EoE activity and classifies the histological severity of EoE patients. We utilized a cohort that includes 1066 biopsy slides from 400 subjects to validate the system's performance and achieved a histological severity classification accuracy of 86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approach highlights the importance of systematically analyzing the distribution of biopsy features over the entire slide and paves the way towards a personalized decision support system that will assist not only in counting cells but can also potentially improve diagnosis and provide treatment prediction. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision support systems

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Utilization of GPS Technology in The Maritime Sector on Motor Sailing Yachts

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IOP Conference Series: Earth and Environmental science 2021年第1期698卷

作者： Melani Quintania Danny Faturachman T. Husain Bayu Pasupati Ahmad Taufik Management Department Faculty of Economy Darma Persada University Indonesia Marine Engineering Department Faculty of Marine Technology Darma Persada University Indonesia Information System Department School of Management and Computer Science STMIK Widuri Jakarta – Indonesia and Student of Doctoral Management YAI Persada University Indonesia Student of Doctoral Management YAI Persada University Indonesia Information System Department School of Management and Computer Science STMIK Widuri Jakarta – Indonesia

The utilization of technology only aims to be fast, precise, and accurate in each implementation and find a better solution at that time in several sectors. This study aims to identify the use of Global Positioning Technology (GPS) technology to track the position of the motor sailing yachts, specifically with the features used on a device or smart-phone during a trip or expedition. This research uses a type of applied research to solve problems that are faced clearly, and the results can be used as a basis for solving corrective steps. The results found that GPS-Technology on motor sailing yachts in 'Pelayaran Rakyat' maritime can receive coordinates point and then send these coordinates to the vessel-tracking systems to determine the position of the motor sailing yachts between users.

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A Compact Low Power AI Module Mounted on Drone for Plant Monitor system

A Compact Low Power AI Module Mounted on Drone for Plant Mon...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Tomoki Kobayashi Tomoyuki Yokogawa Nao Igawa Yoichiro Sato Satoshi Fujii Kazutami Arimoto Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama Japan Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan Information and Communication Systems Engineering National Institute of Technology Okinawa College Nago Okinawa Japan

This paper describes the compact and low power AI DNN discriminator which is mounted on the small drone like a hobby type. By adopting this AI module, the effective plant monitoring system using the drone has been ach...

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