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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Survey of reduced workload and crewing strategies for ocean patrol vessels

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NAVAL ENGINEERS JOURNAL 2001年第2期113卷 29-44页

作者： Baker, C Krull, R Snyder, G Lincoln, W Malone, TB Clifford C. Baker CIE CHFEP is a senior staff scientist at Carlow International Incorporated. He has applied most of his 24 years of experience in the application of human engineering technology to maritime systems. Mr. Baker has directed much of Carlow's efforts to reduce ship workload and to improve human performance and maritime safety through application of human factors methods and data. He is a Certified Industrial Ergonomist (CIE) as well as a Certified Human Factors Engineering Professional (CHFEP). Both certifications were granted by Oxford Research where Mr. Baker also serves as an Advisory Board member. Russell D. Krull P.E. is a senior engineer with A&T/Proteus Engineering with more than 18 years of experi-ence in marine engineering naval architecture and program management including 16 years of active duty in the U.S. Coast Guard. Recent experience includes advanced ship design studies engineering software development technical support for the USMC Advanced Amphibious Assault Vehicle propulsion systems analyses ship structural engineering and cargo handling systems engineering. Mr. Krull has an M.S.E. in naval architecture and marine engineering and an M.S.E. in industrial and operations engineering from University of Michigan and a B.S. in ocean engineering from the U.S. Coast Guard Academy. Capt. Glenn L. Snyder USCG. Regrettably since this paper was originally written Capt. Snyder has passed away. At the time of his death he was an operations specialist assigned to the Coast Guard's Deepwater Capabilities Replacement Project as Chief of Human Systems Integration. He served as commanding officer of the patrol boat Cape George (WPB-95306) the icebreaking tug Biscayne Bay (WTGB-104) and the cutter Legare (WMEC-911). A 1975 graduate of the U.S. Coast Guard Academy Capt. Snyder held an M.A. in national security and strategic studies from the U.S. Naval War College and an M.A. in international relations from Salve Regina College. In addition he was a 1998 fellow of the Foreign Service

The U.S. Coast Guard is in the concept exploration phase of its Integrated Deepwater System (IDS) acquisition project. This project will define the next generation of surface, air and command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) assets used to perform the Coast Guard's missions in the IDS environment (>50 NM off the U. S. coastline). As part of early technology investigations, the needs exist to: (1) analyze the workload requirements of the IDS, (2) identify alternative means to perform ship's work, and (3) optimize ship manning consistent with ship workload, performance criteria, and the available tools and equipment aboard. To reduce shipboard work requires an understanding of the mission and support requirements placed on the vessel and crew, how these requirements are currently met, and how requirements might otherwise be met to reduce workload and crew size. This study examined currently implemented workload and manpower reducing approaches of commercial maritime fleets, U.S. and foreign navies, and foreign coastguards. These approaches were analyzed according to evaluation criteria approved by the IDS acquisition project team. From this, strategies for shipboard work reduction that may be considered for adoption by the IDS were identified and analyzed according to performance and costs factors. Ten workload-reducing strategies were identified: damage control, bridge, multiple crewing, engineering, risk acceptance, modularity, deck, enabling technologies, ship/personnel readiness, and operability and maintainability.

关键词： Naval vessels

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STATISTICAL PROCESS-CONTROL FOR software IN THE MAINTENANCE PHASE

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NAVAL ENGINEERS JOURNAL 1993年第3期105卷 192-200页

作者： LUKE, SR JACOBS, DA REED, BM Stephen R. Luke:is a registered professional engineer in the Commonwealth of Virginia. He received his B.S. in mechanical engineering from Virginia Polytechnic Institute in 1983 and is presently pursuing a master's degree in engineering management at Old Dominion University. Mr. Luke is experienced as a test engineer at the Norfolk Naval Shipyard (1978–1989) and as a reliability/quality engineer at the Naval Undersea Warfare Center Detachment Norfolk (1989-Present). Currently he is the group leader of the RMA/QA group for the AN/SQQ-89(V) Antisubmarine Warfare System at Naval Undersea Warfare Center Detachment Norfolk. Mr. Luke is a member of ASNE IEEE (Reliability Society) and ASQC and is an ASQC certified quality engineer and reliability engineer. Derya Alasya Jacobs:is an assistant professor in the Engineering Management Department at Old Dominion University (ODU). She has a B.S. in computer science and an M.S. and Ph.D in engineering management from the University of Missouri-Rolla. Prior to joining ODU Dr. Jacobs worked as a research engineer at the Center for Technology Transfer and Economic Development at the University of Missouri-Rolla. During the same period she served as a consultant in software development for Missouri Enterprise Rolla Missouri. Dr. Jacobs' research interests are in the areas of applied artificial intelligence decision support systems operations research and computer integrated manufacturing. Billie M. Reed:is an assistant professor in the Engineering Management Department at Old Dominion University. He received a B.S. degree in mechanical engineering from Auburn University in 1971. After completing undergraduate studies Dr. Reed was commissioned as an officer in the U.S. Navy where he served in the nuclear submarine force until 1982. Following his military career Dr. Reed worked for nine years as a systems engineer and human-machine interface designer of complex interactive computer based systems for use in aircraft space and ship systems. He received his M.S. and Ph

As technology advances and our dependency on software increases, the requirement to develop the correct means to improve quality in both the deveiopment phase and the maintenance phase of software life cycle support becomes increasingly significant. The focus of this paper is on the dilemma facing software engineering in maintaining quality within the constraints of the maintenance phase. The paper further proposes that the software maintenance process can also be analvzed and improved using statistical process control (SPC) techniques. The methods discussed in this paper have been proposed for use in the software Maintenance project at NUWC Detachment Norfolk. Upon successful testing at NUWC Detachment Norfolk, they will be forwarded to the AN/SQQ-89(V) (interagencv) software Quality Evaluation Committee for use by other agencies which are developing and maintaining AN/SQQ-89(V) software.

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An Interdisciplinary Survey on Information Flows in Supply Chains

arXiv

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arXiv 2023年

作者： Pennekamp, Jan Matzutt, Roman Klinkmüller, Christopher Bader, Lennart Serror, Martin Wagner, Eric Malik, Sidra Spib, Maria Rahn, Jessica Gürpinar, Tan Vlad, Eduard Leemans, Sander J.J. Kanhere, Salil S. Stich, Volker Wehrle, Klaus RWTH Aachen University Ahornstrasse 55 Aachen North Rhine-Westphalia 52074 Germany BPMotion 10 Gore Ave KirraweeNSW2232 Australia Cyber Analysis & Defense Fraunhofer FKIE Fraunhofer-straße 20 North Rhine-Westphalia Wachtberg53343 Germany Software Systems Research Group Data61 CSIRO Level 5/13 Garden St EveleighNSW2015 Australia Institute for Industrial Management RWTH Aachen University Campus-Boulevard 55 North Rhine-Westphalia Aachen52074 Germany Supply Chain Development and Strategy Fraunhofer IML Joseph-von-Fraunhofer-Straße 2-4 North Rhine-Westphalia Dortmund44227 Germany School of Computer Science and Engineering University of New South Wales Engineering Rd SydneyNSW2052 Australia

Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chain management

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On the implementation of the Opus coordination language

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Concurrency and Computation: Practice and Experience 2000年第4期12卷

作者： Erwin Laure Matthew Haines Piyush Mehrotra Hans Zima Institute for Software Technology and Parallel Systems University of Vienna Liechtensteinstrasse 22 A-1090 Vienna Austria Department of Computer Science University of Wyoming Laramie WY 82071-3682 U.S.A. Institute for Computer Applications in Science and Engineering NASA Langley Research Center Mail Stop 132C Hampton VA 23681-0001 U.S.A.

Opus is a new programming language designed to assist in coordinating the execution of multiple, independent program modules. With the help of Opus, coarse grained task parallelism between data parallel modules can be expressed in a clean and structured way. In this paper we address the problems of how to build a compilation and runtime support system that can efficiently implement the Opus constructs. Our design considers the often-conflicting goals of efficiency and modular construction through software re-use. In particular, we present the system requirements for an efficient Opus implementation, the Opus runtime system, and describe how they work together to provide the underlying services that the Opus compiler needs for a broad class of machines. Copyright © 2000 John Wiley & Sons, Ltd.

关键词： Opus task parallelism data parallelism coordination

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Hydraulically coupled power-plants commitment within short-term operation planning in mixed hydro-thermal power systems

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EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 1997年第5期7卷 323-330页

作者： Rakic, MV Markovic, ZM Dr. Milan V. Rakic (1938) received his electrical engineering degree in 1962 his M.S. degree in 1972 and his Ph.D. degree in 1993 from the University of Belgrade Yugoslavia. He made additional studies in operations research and system science at Stanford University Department of Operations Research Stanford/California USA and Western Reserve University System Research Center. Cleveland/Ohio USA in 1969/1970. He joined the Mihajlo Pupin Institute Department of Automatic Control Belgrade/Yugoslavia in 1962. He was engaged in large-scale system control studies and research. Since 1975 he has been working on the development of methods and software for power system operation planning. He is currently a senior research associate at the Mihajlo Pupin Institute. (Mihajlo Pupin Institute Volgina 15 POBox 15 11050 Belgrade Yugoslavia Phone: +381-11-773-503 Fax: +381-11-773-835) Zoran M. Markovic(1962) received his B.S. degree in electrical engineering in 1988 from the School of Electrical Engineering University of Belgrade Yugoslavia. He joined the Department of Automatic Control of the Mihajlo Pupin Institute Belgrade/Yugoslavia. in 1988. His principal fields of work are development and application of optimization techniques in power system control development of information systems for power system control functions development and application of forecast techniques etc. He is currently a research associate at the Mihajlo Pupin Institute. (Mihajlo Pupin Institute. Volgina 15 PO Box 15 11050 Belgrade Yugoslavia Phone: +381-11-773-503 Fax: +381-11-773-835)

A new method for short-term operation planning and power exchange scheduling of interconnected hydrothermal power systems is proposed in the paper. The criterion function is the total variable profit obtained sales of energy to customers within the system, exchange of energy with other systems, diminished by the cost of system operation. The method is based on Lagrangian relaxation, decomposition technique and successive solution of dual and primal problems. The dual and primal problems are decomposed and solved using appropriate optimization methods, while Lagrangian multipliers are corrected using subgradient method. Dual hydro subproblems are solved using linearization and network flow method. Special modification of the algorithm is made so as to enable commitment of hydro power plants, taking into account bilogical constraints on minimal flow of water downstream of the hydro power plant. The efficiency of the method is illustrated on a test problem.

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

arXiv

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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A system for work efficiency improvement by user activity analysis and feedback

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IOP Conference Series: Materials Science and engineering 2020年第1期940卷

作者： Dmitrii Timofeev Alexander Samochadin Mobile Systems Management Laboratory Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Polytekhnicheskaya ul. 29 St.Petersburg Russia Higher School of Software Engineering Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Polytekhnicheskaya ul. 29 St.Petersburg Russia

The efficiency of work processes is an important factor in company success and economic growth. Many companies are still using inefficient processes due to a lack of knowledge or defective implementation. The processes that rely upon intellectual labor, such as software development or management activities, are often undocumented, which makes them difficult for optimization. We present a method of tracking of the psychophysical state and activities of employees and providing them feedback intended to optimize the work process for work efficiency and user well-being. We use recorded traces of the user actions, including the created artifacts, and detect the psychophysical states on the base of data acquired from portable devices, including fitness wrist bands and EEG sensors. The method has been implemented in a hardware and software system whose architecture we describe in this paper. The system has been developed for the software engineering industry, but the method itself applies to other domains as well. We show how to adapt the proposed approach to different types of working environments and business processes. As the efficiency of the work process is a major factor contributing to the industrial and economic modernization, we discuss the ways to apply the method to business processes that are important for the Arctic region of Russia.

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