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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

Journal of Physics: Conference Series 2020年第1期1453卷

作者： Yuxi Chen Xiaotong Zhang Qing Zhao Faheem Akhtar School of Statistics and Mathematics Country Central University of Finance and Economics Beijing 100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York 13902 America Faculty of Information Technology Beijing University of Technology Beijing 100124 China. Department of Computer Science Vrije Universiteit Amsterdam 1081 HV The Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing 100124 China Department of Computer Science Sukkur IBA University Sukkur 65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

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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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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： Computer aided diagnosis

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Pressure-Induced Superconductivity in Topological Heterostructure (PbSe)5(Bi2Se3)6

arXiv

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arXiv 2023年

作者： Pei, Cuiying Zhu, Peng Li, Bingtan Zhao, Yi Gao, Lingling Li, Changhua Zhu, Shihao Zhang, Qinghua Ying, Tianping Gu, Lin Gao, Bo Gou, Huiyang Yao, Yansun Sun, Jian Liu, Hanyu Chen, Yulin Wang, Zhiwei Yao, Yugui Qi, Yanpeng School of Physical Science and Technology ShanghaiTech University Shanghai201210 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Material Science Center Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314011 China State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Center for High Pressure Science and Technology Advanced Research Beijing100094 China Department of Physics and Engineering Physics University of Saskatchewan SaskatoonSKS7N 5E2 Canada National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom Shanghai Key Laboratory of High-Resolution Electron Microscopy ShanghaiTech University Shanghai201210 China

Recently, the natural heterostructure of (PbSe)5(Bi2Se3)6 has been theoretically predicted and experimentally confirmed as a topological insulator. In this work, we induce superconductivity in (PbSe)5(Bi2Se3)6 by implementing high pressure. As increasing pressure up to 10 GPa, superconductivity with Tc ~ 4.6 K suddenly appears, followed by an abrupt decrease. Remarkably, upon further compression above 30 GPa, a new superconducting state arises, where pressure raises the Tc to an unsaturated 6.0 K within the limit of our research. Combining XRD and Raman spectroscopies, we suggest that the emergence of two distinct superconducting states occurs concurrently with the pressure-induced structural transition in this topological heterostructure (PbSe)5(Bi2Se3)6. © 2023, CC BY-NC-ND.

关键词： Topology

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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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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Correction: Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas

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International Journal of Computational Intelligence systems 2024年第1期17卷 1-1页

作者： Jabbar, Ayesha Naseem, Shahid Li, Jianqiang Mahmood, Tariq Jabbar, Muhammad Kashif Rehman, Amjad Saba, Tanzila Faculty of Information Technology Beijing University of Technology Beijing China College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Information Sciences University of Education Lahore Pakistan Beijing Engineering Research Center for IoT Software and Systems Beijing China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Faculty of Information Sciences University of Education Vehari Pakistan

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 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 Pardalos, 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 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, CC BY-NC-ND.

关键词： Decision making

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Recent Advances in systems and Network Medicine: Meeting Report from the First International Conference in systems and Network Medicine

Recent Advances in Systems and Network Medicine: Meeting Rep...

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作者： Kurnat-Thoma, Emma Baranova, Ancha Baird, Pat Brodsky, Elia Butte, Atul J. Cheema, Amrita K. Cheng, Feixiong Dutta, Shuchismita Grant, Christina Giordano, James Maitland-Van Der Zee, Anke H. Fridsma, Douglas B. Jarrin, Robert Kann, Maricel G. Keeney, Jonathon Loscalzo, Joseph Madhavan, Guru Maron, Bradley A. Mcbride, Dennis K. Mckean, Maeve Mun, Seong K. Palmer, James C. Patel, Bakul Parakh, Kapil Pariser, Anne R. Pristipino, Christian Radstake, Timothy R.D.J. Rajasimha, Harsha K. Rouse, William B. Rozman, Damjana Saleh, Alif Schmidt, Harald H.H.W. Schultz, Nikolaus Sethi, Tavpritesh Silverman, Edwin K. Skopac, Jessica Svab, Igor Trujillo, Sylvia Valentine, James E. Verma, Dinesh West, Bruce J. Vasudevan, Sona BethesdaMD United States Genetics and Genomics Georgetown University School of Nursing and Health Studies WashingtonDC United States School of Systems Biology George Mason University FairfaxVA United States Global Software Standards Philips Pleasant PrairieWI United States Pine Biotech New OrleansLA United States San FranciscoCA United States Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center WashingtonDC United States Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Clinic ClevelandOH United States Institute for Quantitative Biomedicine Rutgers University The State University of New Jersey PiscatawayNJ United States Rare Disease Institute Division of Genetics and Metabolism Children's National Medical Center WashingtonDC United States Department of Neurology Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Biochemistry Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Respiratory Medicine Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands American Medical Informatics Association WashingtonDC United States Department of Biological Sciences University of Maryland Baltimore County BaltimoreMD United States Department of Biochemistry and Molecular Medicine George Washington University WashingtonDC United States Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States National Academy of Engineering The National Academies of Sciences Engineering and Medicine WashingtonDC United States WashingtonDC United States Georgetown University Global Health

The First International Conference in systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: systems and Network Medicine in Clinical Practice;the role of -omics technologies in Health Care;the role of Education and Ethics in Clinical Practice, systems Thinking, and Rare Diseases;and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances. © Emma Kurnat-Thoma et al. 2020 Published by Mary Ann Liebert, Inc. 2020.

关键词： Artificial intelligence

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Development of multimedia modules for education

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Computer Applications in engineering Education 1995年第2期3卷 97-97页

作者： Iskander, M.F. Catten, J.C. Jameson, R. Jones, A. Balcells, A. CAEME Center College of Engineering University of Utah Salt Lake City Utah 84112 Dr. Iskander edited a book onMicrowave Processing of Materialspublished by the Materials Research Society in 1994 and authored a textbook onElectromagnetic Fields and Waves published by Prentice Hall 1992. He edited theCAEME Software Book Vol. I 1991 Vol. II 1994 and coedited two other books onMicrowave Processing of Materials both published by the Materials Research Society in 1991 and 1992. Dr. Iskander edited two special issues of theJournal of Microwave Power one on “Electromagnetics and Energy Applications” March 1983 and the other on “Electromagnetic Techniques in Medical Diagnosis and Imaging” September 1983. He also edited a special issue of theACES Journalon computer-aided electromagnetics education. He has published over 150 papers in technical journals and made numerous presentations in technical conferences. Dr. Iskander is the editor of the journalComputer Aplications in Engineering Education (CAE) published by John Wiley & Sons Inc. He is a fellow of IEEE and a member of the National Research Council Committee on Microwave Processing of Materials. His present fields of interest include the use of numerical techniques in electromagnetics. Rex M. Jameson:received his BS in Physics from Stanford University in 1992 and his ME in Electrical Engineering from the University of Utah in 1995. He currently works for the CAEME Center as a programmer. Antony K. Jones:has extensive experience with three-dimensional computer-rendered graphics on Macintosh Amiga and PC platforms. He has had years of commercial experience with the design and printing of computer-generated graphics. Albert R. Balcells:received a Telecommunication Engineering degree in 1994 from the Superior School of Telecommunication of Barcelona Polytechnic University of Catalunya (EC) and is currently pursuing a MS degree in Electrical Engineering from the University of Utah. He has experience in DOS and UNIX-based systems and is fluent in C C++

Development of multimedia modules and courses for engineering education present a significant opportunity to modernize curricula and improve the effectiveness of teaching and learning. In this article we review the various hardware and software components required to develop multimedia lessons and discuss the role of each component in the structure of a multimedia module. Examples of multimedia lessons developed for CD-ROM distribution by the center for Multimedia Education and Technology (CAEME) at the University of Utah are described. These include multimedia lessons in electromagnetics, the Calculus Castle, and the Physics Museum. It is shown that although extensive resources and expertise are often needed to develop these modules, development software and associated technologies are readily available and educators are encouraged to take advantage of these opportunities to reform, restructure, and improve engineering education.

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