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IEEE Symposium on Intelligent Vehicle

作者： Kenichi Takasaki Yuka Sasaki Shoichiro Watanabe Yasutaka Nishimura Mari Abe Tokyo Software & Systems Development Laboratory IBM Japan Ltd. Tokyo Japan Product Engineering MODE Inc. Tokyo Japan

In the domain of connected vehicles or advanced driver assistance systems, electroencephalogram (EEG) data is measured in vehicles and used for applications in driver safety. These analysis modules are designed to detect abnormal driver states such as drowsiness, fatigue, and dangerous driving by using EEG data in real-time on edge devices since these conditions reflect a driver’s current cognitive state. However, there are few approaches to forecasting EEG data to prevent dangerous driving in advance using recent deep learning techniques. In this paper, we propose a novel generative adversarial network (W-GAN) which aims to forecast EEGs as a multivariate multi-step times series data. It consists of dilated causal convolutional layers to maintain EEG characteristics. We also propose a new performance measure reflecting the reproducibility of frequency components which confirms the feasibility of the forecasted EEG data. We conducted an experiment to evaluate our proposed model using EEG analysis research data. In the experiment, it was shown that our model outperformed several deep learning models in reproducibility of both EEG waveform and frequency components.

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(WIP) IoT context descriptor: Situation detection and action invocation model for real-time high-volume transactions 3

(WIP) IoT context descriptor: Situation detection and action...

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3rd IEEE International Congress on Internet of Things, ICIOT 2018

作者： Abe, Mari Yamamoto, Gaku Furuichi, Sanehiro Tokyo Software and Systems Development Laboratory Watson IoT IBM Japan Ltd. 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo Japan

ISBN: (纸本)9781538672440

In our previous work, a runtime-specific rule was introduced that described how to detect situations and invoke actions of connected vehicles in the surrounding context. However, rule developers were required to check carefully whether each rule was consistent to the other rules or not. Our next challenge is how to simplify the validation of the application logics when the number of rules is increasing. In this paper, we propose IoT Context Descriptor, a context model for situation detection and action invocation for IoT that is aiming at accelerating easy development of situation detection and action invocation while securing the processing of real-time high-volume transactions. © 2018 IEEE.

关键词： Multi agent systems

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Improving the performance of backup candidate file selection using inode bitmap 33

Improving the performance of backup candidate file selection...

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33rd International Conference on Massive Storage systems and Technology, MSST 2017

作者： Matsui, Sosuke Miyamura, Tsuyoshi Tanemura, Noriko Watanabe, Terue Iwasaki, Norie Software and Systems Development Laboratory IBM Tokyo Japan

An incremental backup copies data and metadata of created, updated, and deleted files since the previous backup. The performance of finding candidate files for an incremental backup becomes a limiting factor on the number of files in a file system. We propose a method for finding backup candidate files using inode bitmaps as intermediate files to improve the performance of incremental backups. Our method reduces the size of intermediate files used by the backup candidate file selection and efficiently finds deleted files by calculating the bitwise operation of inode bitmaps. We implemented our method with a file system backup function supported by ibm Spectrum Scale (formerly known as the General Parallel File System, GPFS) 4.2, and showed that our method improves the performance of backup candidate file selection up to 44.5%. © 33rd International Conference on Massive Storage systems and Technology, MSST 2017. All rights reserved.

关键词： Digital storage

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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A fine-grained data set and analysis of tangling in bug fixing commits

arXiv

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arXiv 2020年

作者： Herbold, Steffen Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher Ahmed Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel G. Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James C. Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes Institute for Software and Systems Engineering TU Clausthal Clausthal-Zellefeld Germany Institute of Computer Science University of Goettingen Goettingen Germany Department of Computer Engineering Sharif University of Technology Tehran Iran School of Computing Queen's University Kingston Canada Department of Computer Science Guru Nanak Dev University Amritsar India Karlsruhe Germany Brunel University London Uxbridge United Kingdom Institute of Computer Science University of Goettingen Germany University of Tehran Tehran Iran Ericsson Hungary Ltd. Budapest Hungary Simula Research Laboratory Fornebu Norway Technical University of Košice Košice Slovakia LUT University Finland National University of Defense Technology Changsha China University of British Columbia Kelowna Canada Østfold University College Halden Norway Vrije Universiteit Amsterdam Amsterdam Netherlands University of Auckland Auckland New Zealand University of Saskatchewan Saskatoon Canada IBM Boulder United States University of Missouri-Kansas City Kansas City United States Università della Svizzera italiana Lugano Switzerland Trent University Peterborough Canada University of Maryland Baltimore County United States Imam Abdulrahman Bin Faisal University Saudi Arabia The Hebrew University Acumen Jerusalem Israel University of Oulu Oulu Finland Monash University Melbourne Australia University of Würzburg Würzburg Germany Technische Universität Darmstadt Darmstadt Germany University of Tennessee Knoxville United States University of Macedonia Thessaloniki Greece Department of Computer Engineering Bilkent University Ankara Turkey University of Melbourne Melbourne Australia Independent Researcher Tehran Iran University of Trento Trento Italy University of Stuttgart Stuttgart Germany Purdue University West LafayetteIN United States Eindhoven University of Technology Eindhoven Netherlands Softtech Inc. Research and Development Center Istanbul34947 Turkey Radboud Universi

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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An Inductive method to select simulation points

An Inductive method to select simulation points

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作者： Choi, MinSeong Fukuda, Takashi Goshima, Masahiro Sakai, Shuichi Graduate School of Information Science and Technology University of Tokyo Tokyo113-8656 Japan Tokyo Software and Systems Development Laboratory IBM Japan Tokyo103-8510 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

The time taken for processor simulation can be drastically reduced by selecting simulation points, which are dynamic sections obtained from the simulation result of processors. The overall behavior of the program can be estimated by simulating only these sections. The existing methods to select simulation points, such as SimPoint, used for selecting simulation points are deductive and based on the idea that dynamic sections executing the same static section of the program are of the same phase. However, there are counterexamples for this idea. This paper proposes an inductive method, which selects simulation points from the results obtained by pre-simulating several processors with distinctive microarchitectures, based on assumption that sections in which all the distinctive processors have similar istructions per cycle (IPC) values are of the same phase. We evaluated the first 100G instructions of SPEC 2006 programs. Our method achieved an IPC estimation error of approximately 0.1% by simulating approximately 0.05% of the 100G instructions. © Copyright 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Program processors

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Analyzing Business Processes by Automatically Detecting KPI Thresholds

Analyzing Business Processes by Automatically Detecting KPI ...

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IEEE International Conference on Services Computing (SCC)

作者： Mari Abe Michiharu Kudo Tokyo Software & Systems Development Laboratory IBM Japan Ltd Tokyo Japan IBM Research - Tokyo IBM Japan Ltd Tokyo Japan

This paper describes a method for automatically detecting key performance indicator (KPI) thresholds by dividing and aggregating process instances on the basis of differences in process models. The thresholds can be used as an analysis axis of data exploration to investigate process models that are discovered from huge logs. The proposed method enables users to minimize the time needed to detect KPI thresholds through trial and error. We applied the method to real-life logs and experiment results showed that thresholds were detected for two types of KPIs. Although one type did not correlate with process patterns, the other highly correlated with them. Such findings are usually obtained from the domain knowledge of business users and analysis results acquired by data analysts with technical expertise. However, with our approach the thresholds can be detected automatically and this helps to expand process analysis for end users.

关键词： Monitoring Analytical models Context Data models Engines Insurance

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Improving the performance of asynchronous replication with mixed-mode copying 15

Improving the performance of asynchronous replication with m...

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Posters and Demos Session of the 16th International Middleware Conference, MIDDLEWARE 2015

作者： Matsui, Sosuke Miyoshi, Hiroyuki Araki, Hiroshi Iwasaki, Norie Bish, Thomas W. Tokyo Software and Systems Development Laboratory IBM Japan Global Technology Service IBM Japan

ISBN: (纸本)9781450337298

ibm Scale Out Network Attached Storage (SONAS) sup- ports asynchronous replication to copy files to a remote site for disaster recovery. In SONAS R1.3.1, We developed mixed-mode copying by combining size-based copying with directory-based copying to improve the performance of asyn- chronous replication for typical real-world file tree structures where large files reside in the same directory. This com- bined method balances the workload of copying large files and prevents the performance degradation due to concur- rent copying of small files in a single directory. We veri- fied our mixed-mode copying increased the performance of asynchronous replication by up to 148% compared to either approach by itself on both virtual and physical test environ- ments. © 2015 ACM.

关键词： Trees (mathematics)

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Realization of a highly extensible agent framework for business applications

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Transactions of the Japanese Society for Artificial Intelligence 2011年第1期26卷 127-135页

作者： Yamamoto, Gaku Department of Computational Intelligence and Systems Science Tokyo Institute of Technology IBM Japan Software Development Laboratory Japan

Recently, highly extensible IT systems are required. It is said that an agent programming model provides high efficiency on application development, however, modification of both data structure and message handler program of agents are needed to add a new service. The issue is the same as the issue that other programming models have. ibm Agent Framework which we developed provides a highly extensible capability. By using the framework, a new service can be added withoutmodifying existing data structure and programming code. In this paper, we introduce the capability and explain how the framework provides the capability. We also discuss why the framework can provide the capability.

关键词： Multi agent systems

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THE ROLE OF WORK MANAGEMENT IN APPLICATION development

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ibm systems JOURNAL 1990年第2期29卷 189-208页

作者： CHROUST, G GOLDMANN, H GSCHWANDTNER, O IBM Programming Systems Vienna Software Development Laboratory

Quality is probably one of the most serious concerns of today"s software *** software applica- tions exhibiting a certain complexity, the quality of a product can only be guaranteed by a methodological approach, using appropriate administration and tools. The methodology andadministration must be mani- fested in a well-definedand well-observedapplication development *** must integrate the human activity, the tools, and the intermediateand final work products into a coherent flow of actions. In this regard, the developmentof applications follows patterns that are well establishedin other industries wherean application development (AD)process model is defined andthen executed via an interpretation mechanism. Thecomplexity of the developmentproc- ess makesit necessary tosupport and integrate all of its aspects by means ofon-lineinteractive computer support. Computer-aidedprocess support in the gen- eral sense we call work management. This paper ex- plains the concepts of an application development process model andof work management forapplica- tion development underADlCycIe- and its relation to project management.

关键词： .computer support project management process model AD/Cycle application development process work management Vienna software development

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