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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Yoshiaki Bando Katsutoshi Itoyama Masashi Konyo Satoshi Tadokoro Kazuhiro Nakadai Kazuyoshi Yoshii Hiroshi G. Okuno Graduate School of Informatics Kyoto University Kyoto Japan Graduate School of Information Science Honda Research Institute Japan Co. Ltd Miyagi Japan Graduate School of Information Science and Engineering Tokyo Institute of Technology Saitama Japan Graduate Program for Embodiment Informatics Waseda University Tokyo Japan

This paper presents an online real-time method that enhances human voices included in severely noisy audio signals captured by microphones of a hose-shaped rescue robot. To help a remote operator of such a robot pick up a weak voice of a human buried under rubble, it is crucial to suppress the loud ego-noise caused by the movements of the robot in real time. We tackle this task by using online robust principal component analysis (ORPCA) for decomposing the spectrogram of an observed noisy signal into the sum of low-rank and sparse spectrograms that are expected to correspond to periodic ego-noise and human voices. Using a microphone array distributed on the long body of a hose-shaped robot, ego-noise suppression can be further improved by combining the results of ORPCA applied to the observed signal captured by each microphone. Experiments using a 3-m hose-shaped rescue robot with eight microphones show that the proposed method improves the performance of conventional ego-noise suppression using only one microphone by 7.4 dB in SDR and 17.2 in SIR.

关键词： Rescue robots Microphone arrays Matrix decomposition Radio frequency Mobile robots

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Hybrid Approaches of Verbal Decision Analysis in the Selection of Project Management Approaches

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Procedia Computer Science 2015年 55卷 1183-1192页

作者： Isabelle Tamanini Plácido Rogério Pinheiro Thais Cristina Sampaio Machado Adriano Bessa Albuquerque University of Fortaleza (UNIFOR) - Graduate Program in Applied Informatics Av. Washington Soares 1321 Bl. J Sl. 30 Fortaleza 60.811-905 Brazil

Decision support methods aim at assisting in the decision-making process by simplifying the analysis of the problem and justifying the choice of a particular potential action. Recent researches have shown that the hybridization of methods is able to overcome limitations presented by the methods when applied separately: the classification of alternatives before submitting them to an ordination methodology would be an e ective way of filtering the set to be ordered. Specific Practices of Capability Maturity Model Integration were analyzed through a decision making model, assisted by the methods SAC and ZAPROS III-i. The results will be compared to previous studies.

关键词： Hybrid Methodologies Verbal Decision Analysis SAC ZAPROS III-i Project Management Capability Maturity Model Integration Decision Support Systems

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I-DCOP: Train Classification Based on an Iterative Process Using Distributed Constraint Optimization

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Procedia Computer Science 2015年 51卷 2297-2306页

作者： Denise M.V. Sato André P. Borges Peter Márton Edson E. Scalabrin Federal Institute of Education Science and Technology of Parana Graduate Program in Informatics Pontifical Catholic University of Parana Curitiba Brazil Faculty of Management and Computer Science University of Zilina Zilina Slovakia

This paper presents an iterative process based on Distributed Constraint Optimization (I-DCOP), to solve train classification problems. The input of the I-DCOP is the train classification problem modelled as a DCOP, named Optimization Model for Train Classification (OMTC). OMTC generates a feasible schedule for a train classification problem defined by the inbound trains, the total of outbound trains and the cars assigned to them. The expected result, named feasible schedule, leads to the correct formation of the outbound trains, based on the order criteria defined. The OMTC also minimizes the schedule execution time and the total number of roll-ins (operation executed on cars, sometimes charged by the yards). I-DCOP extends the OMTC including the constraints of limited amount of classification tracks ant their capacity. However, these constraints are included iteratively by adding domain restrictions on the OMTC. Both OMTC and I-DCOP have been measured using scenarios based on real yard data. OMTC has generated optimal and feasible schedules to the scenarios, optimizing the total number of roll-ins. I-DCOP solved more complex scenarios, providing sub-optimal solutions. The experiments have shown that distributed constraint optimization problems can include additional constraints based on interactively defined domain.

关键词： Train Classification Classification Schedules I-DCOP OMTC DCOP.

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Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour

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Communications Psychology 2023年第1期1卷 1-16页

作者： Arzu Karakulak Beyza Tepe Radosveta Dimitrova Mohamed Abdelrahman Azzam Amin Plamen Akaliyski Rana Alaseel Batoul Bakkar Wassim Gharz Edine Fatema Mohsen Yousuf Abdulqader Alkamali Danny A. Lizarzaburu Aguinaga Andrii Andres John Jamir Benzon R. Aruta Marios Assiotis Olga Solomontos-Kountouri Hrant Avanesyan Maria Muradyan Norzihan Ayub Maria Bacikova-Sleskova Raushan Baikanova Aidos Bolatov Telman Seisembekov Sunčica Bartoluci Ksenija Bosnar Anna Janovská Darko Katović Franjo Prot David Benitez Rita Rivera Ivanna Bodnar Iuliia Pavlova Judyta Borchet Maria Kazmierczak Aleksandra Lewandowska-Walter Justyna Michalek-Kwiecien Katarzyna Skrzypińska Yunier Broche-Pérez Carmen Buzea Rosalinda Cassibba Pasquale Musso Fabiola Silletti Marta Martín Carbonell Bin-Bin Chen Gordana Ristevska Dimitrovska Dương Công Doanh Alejandra del Carmen Dominguez Espinosa Nelli Ferenczi Regina Fernández-Morales Jorge Gaete Yiqun Gan Suely Giolo Rubia Carla Formighieri Giordani Camila Muhl José Rasia Maria-Therese Friehs Shahar Gindi Biljana Gjoneska Juan Carlos Godoy Lorena Cecilia López Steinmetz Maria del Pilar Grazioso Marinés Mejía Camellia Hancheva Given Hapunda Shogo Hihara Russell Sarwar Kabir Tinka Delakorda Kawashima Kazumi Sugimura Brett Walter Mohd Saiful Husain Md Saiful Islam Sadia Sultana Nino Javakhishvili Ana Makashvili Veljko Jovanović Nor Ba’yah Abdul Kadir Johannes Karl Zhumaly Kauyzbay Richa Khanna Meetu Khosla Martina Klicperová-Baker Iva Poláčková Šolcová Ana Kozina Steven Eric Krauss Rodrigo Landabur Katharina Lefringhausen Yun-Hsia Liang Sadia Malik Denisse Manrique-Millones Stefanos Mastrotheodoros Breeda McGrath Enkeleint A. Mechili Samson Mhizha Diana Miconi Rodrigo Moreta-Herrera Andrej Naterer Arash Nemat Felix Neto Joana Neto Luz Marina Alonso Palacio Hassan Okati-Aliabad Carlos Iván Orellana Ligia María Orellana Sushanta Kumar Mishra Joonha Park Eddy Peralta Petro Petrytsa Saša Pišot Benedicta Prihatin Dwi Riyanti Laura Francisca Sudarnoto Angela Oktavia Suryani Adil Samekin Danielius Serapinas Rasa Pilkaus Istanbul Policy Center Sabanci University Istanbul Turkey Department of Psychology MEF University Istanbul Turkey Department of Psychology Stockholm University Stockholm Sweden Social Psychology Department Doha Institute for Graduate Studies Doha Qatar Mokhtass for Consultations and Research Doha Qatar Department of Sociology and Social Policy Lingnan University Hong Kong SAR China Faculty of Medicine Syrian Private University Damascus Syrian Arab Republic Ipswich Hospital Ipswich UK United Private School Athaiba Oman Universidad César Vallejo Trujillo Peru Department of Physical Education Lviv Polytechnic National University Lviv Ukraine Department of Psychology De La Salle University Manila Philippines Theological School Church of Cyprus Nicosia Cyprus General Psychology Chair Yerevan State University Yerevan Armenia Faculty of Psychology and Education University of Malaysia Sabah Sabah Malaysia Department of Educational Psychology and Health Psychology Pavol Jozef Šafárik University Košice Slovakia Astana Medical University Astana Kazakhstan University of Zagreb Zagreb Croatia Clinical Psychology Department Albizu University Miami FL USA Counseling & Psychological Services Duke University Durham NC USA Lviv State University of Physical Culture Lviv Ukraine Institute of Psychology University of Gdansk Gdansk Poland Polish Society for the Psychology of Religion University of Gdańsk Gdańsk Poland Department of Psychology Universidad Central Marta Abreu de Las Villas Santa Clara Cuba Department of Social Sciences and Communication Transilvania University of Brasov Brasov Romania Department of Education Psychology Communication University of Bari Bari Italy Faculty of Psychology Cooperative University of Colombia Santa Marta Colombia Psychology Department Fudan University Shanghai China Higher Medical School University St. Kliment Ohridski Bitola North Macedonia PHI Psihomedika Bitola North Macedonia National Economics University

With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviours was strongest when trust in the government was weak (both at individual- and country-level). Conversely, the association with empathic prosocial concern was strongest when trust in the government was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how motivations may be shaped by socio-cultural context, and outline how findings may contribute to a better understanding of collective action during global crises.

关键词： Human behaviour Infectious diseases

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Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction (vol 53, pg 65, 2021)

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NATURE GENETICS 2021年第3期53卷 413-413页

作者： Conti, David V. Darst, Burcu F. Moss, Lilit C. Saunders, Edward J. Sheng, Xin Chou, Alisha Schumacher, Fredrick R. Al Olama, Ali Amin Benlloch, Sara Dadaev, Tokhir Brook, Mark N. Sahimi, Ali Hoffmann, Thomas J. Takahashi, Atushi Matsuda, Koichi Momozawa, Yukihide Fujita, Masashi Muir, Kenneth Lophatananon, Artitaya Wan, Peggy Le Marchand, Loic Wilkens, Lynne R. Stevens, Victoria L. Gapstur, Susan M. Carter, Brian D. Schleutker, Johanna Tammela, Teuvo L. J. Sipeky, Csilla Auvinen, Anssi Giles, Graham G. Southey, Melissa C. MacInnis, Robert J. Cybulski, Cezary Wokolorczyk, Dominika Lubinski, Jan Neal, David E. Donovan, Jenny L. Hamdy, Freddie C. Martin, Richard M. Nordestgaard, Borge G. Nielsen, Sune F. Weischer, Maren Bojesen, Stig E. Roder, Martin Andreas Iversen, Peter Batra, Jyotsna Chambers, Suzanne Moya, Leire Horvath, Lisa Clements, Judith A. Tilley, Wayne Risbridger, Gail P. Gronberg, Henrik Aly, Markus Szulkin, Robert Eklund, Martin Nordstrom, Tobias Pashayan, Nora Dunning, Alison M. Ghoussaini, Maya Travis, Ruth C. Key, Tim J. Riboli, Elio Park, Jong Y. Sellers, Thomas A. Lin, Hui-Yi Albanes, Demetrius Weinstein, Stephanie J. Mucci, Lorelei A. Giovannucci, Edward Lindstrom, Sara Kraft, Peter Hunter, David J. Penney, Kathryn L. Turman, Constance Tangen, Catherine M. Goodman, Phyllis J. Thompson, Ian M., Jr. Hamilton, Robert J. Fleshner, Neil E. Finelli, Antonio Parent, Marie-Elise Stanford, Janet L. Ostrander, Elaine A. Geybels, Milan S. Koutros, Stella Freeman, Laura E. Beane Stampfer, Meir Wolk, Alicja Hakansson, Niclas Andriole, Gerald L. Hoover, Robert N. Machiela, Mitchell J. Sorensen, Karina Dalsgaard Borre, Michael Blot, William J. Zheng, Wei Yeboah, Edward D. Mensah, James E. Lu, Yong-Jie Zhang, Hong-Wei Feng, Ninghan Mao, Xueying Wu, Yudong Zhao, Shan-Chao Sun, Zan Thibodeau, Stephen N. McDonnell, Shannon K. Schaid, Daniel J. West, Catharine M. L. Burnet, Neil Barnett, Gill Maier, Christiane Schnoeller, Thomas Luedeke, Manuel Kibel, Adam S. Drake, Bettina F. Cussenot, Olivier Cancel-Tassin, Gerald Center for Genetic Epidemiology Department of Preventive Medicine Keck School of Medicine University of Southern California/Norris Comprehensive Cancer Center Los Angeles CA USA The Institute of Cancer Research London UK Royal Marsden NHS Foundation Trust London UK Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland OH USA Seidman Cancer Center University Hospitals Cleveland OH USA Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Strangeways Research Laboratory Cambridge UK Stroke Research Group Department of Clinical Neurosciences University of Cambridge Cambridge UK Fundación Pública Galega Medicina Xenómica Santiago De Compostela Spain Instituto de Investigación Sanitaria de Santiago de Compostela Santiago De Compostela Spain Centro de Investigación en Red de Enfermedades Raras (CIBERER) Madrid Spain Department of Epidemiology and Biostatistics University of California San Francisco CA USA Institute for Human Genetics University of California San Francisco CA USA Department of Urology University of California San Francisco San Francisco CA USA Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Genomic Medicine National Cerebral and Cardiovascular Center Research Institute Suita Japan Laboratory of Clinical Genome Sequencing Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan Biobank Tokyo Japan Laboratory for Genotyping Development RIKEN Center of Integrative Medical Sciences Yokohama Japan Laboratory for Cancer Genomics RIKEN Center of Integrative Medical Sciences Yokohama Japan Division of Population Health Health Services Research and Primary Care School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK Warwick Medical School University of Warwick C

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84–5.29) for men of European ancestry to 3.74 (95% CI, 3.36–4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14–2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71–0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

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Are folksonomies shared conceptualizations?

Are folksonomies shared conceptualizations?

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International Conference on Computer Supported Cooperative Work in Design

作者： Josiane M. Pinheiro Cesar A. Tacla Emerson Cabrera Paraiso Graduate Prog. in Electrical and Computer Engineering Federal Technological University of Parana Curitiba-Pr Brazil Graduate Program in Informatics Pontificia Universidade Catolica do Parana Curitiba-Pr Brazil

This article presents a method to assess whether folksonomies are useful shared conceptualizations for conceptual modeling. A method based on the tripartite model (actors, concepts and instances) induces from social applications a folksonomy related to a domain of knowledge. The hypothesis of this paper is that if folksonomies are shared conceptualizations, then using them in conceptual modeling should reduce the number of divergences between actors when they elicit terms to be part of a model (a concept map in this article). A controlled experiment of conceptual modeling was performed with experimental groups who received tags inducted from data of categorizations from Delicious and control groups who received terms extracted from Web pages categorized on Delicious. The results show that the experimental groups obtained less divergences (on average) in elicitation of terms when compared to control groups.

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Technical data analysis for movement prediction of Euro to USD using Genetic Algorithm-Neural Network

Technical data analysis for movement prediction of Euro to U...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Ary Sespajayadi Indrabayu Ingrid Nurtanio Artificial Intelligent and Multimedia Processing Research Group Makassar Indonesia Graduate of Informatics Engineering Study Programme Hasanuddin University Makassar Indonesia Informatics Engineering Study Program Hasanuddin University Makassar Indonesia Electrical Engineering Study Program Hasanuddin University Makassar Indonesia

In the foreign currency exchange (FOREX), a technical data analysis system for predicting currency movements is needed to help traders in decision making. Thus, this study proposes a system of technical data analysis to movement prediction of Euro to USD using Genetic Algorithm-Neural Network (GANN). To generate a predicted value, Genetic Algorithm searching for the best value of Feed Forward Neural Network (FFNN) trained with the Neural Network method that produced a net to predict. The Validation of predicted results with GANN method based on the degree of accuracy as follows. RMSE values of open is 0.00043; The RMSE values of high is 0.00068; The RMSE value of low is 0.00075; and RMSE values of close is 0.00070.

关键词： Genetic algorithms Biological cells Training Artificial neural networks Accuracy Prediction algorithms

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Predicting User Participation and Detecting User Role Diffusion in Online Health Communities

Predicting User Participation and Detecting User Role Diffus...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Xi Wang Zhiya Zuo Kang Zhao Nick Street Interdisciplinary Graduate Program in Informatics The University of Iowa Iowa City IA US Tippie College of Business The University of Iowa Iowa City IA US

Nowadays, healthcare issues draw people's attention globally. According to the report by the Pew Research Center [1], 35% of U.S. Adults have gone online particularly for information related to medical conditions. Besides getting information from healthcare professionals and friends, 24% of adults also sought information or support from peers who have the same health condition. A major venue where people find such peers is Online Health Communities (OHCs), such as "***". Compared with traditional health-related websites that only allow users to retrieve information, OHCs increased members' ability to interact with peers facing similar health problems and, as a result, better meet their needs for social support. Literatures on social support suggest that OHCs mainly feature three types of social support: informational support, emotional support, and companionship (a.k.a., Network support) [2]. As social support is a pillar of OHCs, a natural question to ask would be: when it comes to users' participations, are a user's online activities in different types of social support related to her/his participation in an OHC? If so, can we predict whether a user will churn from an OHC based on these social support activities? Despite the large amount of research on social support in OHCs, few studies have answered this question systematically by examining users' seeking, receiving, and provision of various types of social support from large-scale datasets. In 2012, Wang [3] suggested that receiving more emotional support is associated with users' longer stay in an OHC. However, the types of social support investigated were limited and only the receiving of support was considered, while that providing social support is also important and beneficial. Analyzing large-scale data from an OHC, we combined various data analytics techniques, including text mining, survival analysis, and predictive modelling. We found that receiving more emotional support or contributing

关键词： informatics Cities and towns Medical conditions Business Data analysis Text mining

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The Evolution and Diffusion of User Roles in Online Health Communities: A Social Support Perspective

The Evolution and Diffusion of User Roles in Online Health C...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Xi Wang Zhiya Zuo Kang Zhao Interdisciplinary Graduate Program in Informatics The University of Iowa Iowa City IA US Tippie College of Business The University of Iowa Iowa City IA US

Online Health Communities (OHCs) have become a major source of social support for people with health problems. Using a case study of an OHC among breast cancer survivors, we revealed the types of social support embedded in each post using text-mining techniques. Then we aggregated users' activities related to different types of social support and identified different roles that users play in an OHC via unsupervised machine learning techniques. By analyzing how users' roles change over time, we constructed a transition graph to illustrate the evolution of users' roles in the OHC. We also discovered that a user's reception of social support is correlated with the evolution of her role. In addition, by examining whom a user interacted with, we found evidence for "role diffusion"-users tend to adopt roles of her network neighbors. Our research has implications for OHC operators to manage and sustain an OHC.

关键词： Feature extraction Medical services Social network services Classification algorithms informatics Ports (Computers) Cities and towns

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MOS-only M-2M DAC for ultra-low voltage applications 6

MOS-only M-2M DAC for ultra-low voltage applications

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6th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2015

作者： Sperotto, Israel Klimach, Hamilton Bampi, Sergio Microelectronics Graduate Program Federal University of Rio Grande Do sul Porto Alegre RS Brazil Electrical Engineering Department Federal University of Rio Grande Do sul Porto Alegre RS Brazil Informatics Institute Federal University of Rio Grande Do sul Porto Alegre RS Brazil

ISBN: (纸本)9781479983322

This work presents a study of the CMOS based R-2R ladder network (also called M-2M) Digital-to-Analog Converter (DAC) as a potential candidate for ultra-low voltage (ULV) applications, i.e. supply voltages VDD lower than 200mV. MOS transistors operate in subthreshold under such very low VDD, and the circuit analysis must be done using an all-region continuous MOSFET model. Simulation results in a commercial 130 nm process corroborate the presented analysis, showing that it is possible to implement this DAC topology for 6 to 8 bits operating under supply voltages from 70 mV to 140 mV. © 2015 IEEE.

关键词： Digital to analog conversion

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