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arXiv 2018年

作者： Amado, Leonardo Aires, Joao Paulo Pereira, Ramon Fraga Magnaguagno, Mauricio C. Granada, Roger Meneguzzi, Felipe Brazil Postgraduate Programme in Computer Science School of Technology

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones. Copyright © 2018, The Authors. All rights reserved.

关键词： Domain Knowledge

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Heuristic approaches for goal recognition in incomplete domain models

arXiv

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arXiv 2018年

作者： Pereira, Ramon Fraga Meneguzzi, Felipe Brazil Postgraduate Programme in Computer Science School of Technology

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: This is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using incomplete (and possibly incorrect) domain theories. We show the efficiency and accuracy of our approaches empirically against a large dataset of goal and plan recognition problems with incomplete domains. Copyright © 2018, The Authors. All rights reserved.

关键词： Large dataset

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Polarimetric SAR Calibration and Processing Tool V1.0: A Tool for Polarimetric Calibration and Processing of SAR Data

Polarimetric SAR Calibration and Processing Tool V1.0: A Too...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Shashi Kumar Varsha Kumari Alice Alice Bhanu Prakash M.E. Arun Babu Abhisek Maiti Shefali Agrawal Photogrammetry and Remote Sensing Indian Institute of Remote Sensing ISRO Dehradun India Computer Science Banasthali Vidyapith Vanasthali India Microwave and Radar Institute German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt DLR) Wessling Germany Faculty of Geoinformation Science and Earth Observation (ITC) University of Twente City Netherlands Geospatial Technology and Outreach Programme Group Indian Institute of Remote Sensing ISRO Dehradun India

ISBN: (数字)9781728154756

ISBN: (纸本)9781728154763

Polarimetric SAR (PolSAR) has been widely used as an active imaging remote sensing technique to characterize manmade and natural features. The PolSAR data needs to be corrected from the polarimetric distortions to minimize the ambiguities for the accurate information retrieval of scatterers. The present work is focused on the minimization of system-induced and atmosphere-induced errors from PolSAR data. A new software Polarimetric SAR Calibration & Processing Tool v1.0 (PSCP v1.0) has been introduced for processing of fully polarimetric and hybrid polarimetric SAR data. The PSCP v1.0, a software tool will be available for the scientific community to calibrate and process the fully polarimetric and the hybrid polarimetric space borne and airborne SAR data of current and future missions of ISRO, India and also for other space agencies.

关键词： Synthetic aperture radar Calibration Distortion Scattering Tools Software Signal processing algorithms

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A framework for ensemble classification and sensitivity analysis in privacy preserving data mining

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International Journal of Computational Systems Engineering 2019年第5-6期5卷 260-276页

作者： Chandrakanth, P. Anbarasi, M.S. Under Quality Improvement Programme Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry605014 India Department of Information Technology Pondicherry Engineering College Pondicherry605014 India

The perturbation mechanism for data streams is a challenging task. In the emerging world, data is erupting from various sources. The core applications need care on the data streams for further analysis and experimentation. As the micro data available with the core applications shall not be revealed to the public without taking any chance of breach, the perturbation challenges the analysis to get through the like results as of on the original datasets. In this paper, we have applied a concept of Perlin noise to distract the original data from the eyes of the analysts, however allowing them to perform their activities well. Applying security dynamically on such data is a challenging task. This paper deals about the concepts of generation of smooth noise and syntactic perturbation mechanism on the selective tuples as selective perturbation. © 2019 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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Comparative Analysis of Eigenface and Learning Vector Quantization (LVQ) to Face Recognition

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： R Chandra S An-Nissa E M Zamzami Master Programme in Informatics Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Face recognition is a topic most often discussed in this era because it can be applied and developed for several needs that can be useful in daily life. Face recognition always use learning method like eigenface and learning vector quantization (LVQ). The learning process is using the face of a digital image taken from a camera in five angles for one person that will apply for dataset learning (training and learning data set), and the live image is taken from the camera for testing (testing data image). The first step image will be detected with a haar cascade and captured by a camera. It will use to processing images to get the best image and prepare to be input into the network. From the experiments with 10 testings with various parameter values, the experiment results obtained the LVQ is more accurate than eigenface to identifying faces with average accuracy 66.29% and eigenface is 56.67% with comparison 9.62%, but eigenface is faster in running time with average time 4.39 seconds and 7.38 seconds for LVQ with comparison 2.98 seconds.

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Conservation changed but not divided

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Trends in Ecology & Evolution 2025年第5期40卷 424-425页

作者： Sam A. Reynolds Sara Beery Neil Burgess Mark Burgman Stuart H.M. Butchart Steven J. Cooke David Coomes Finn Danielsen Enrico Di Minin América Paz Durán Amy Hinsley Sadiq Jaffer Julia P.G. Jones Binbin V. Li Anil Madhavapeddy Lloyd Peck Nathalie Pettorelli Jon Paul Rodríguez William J. Sutherland Conservation Science Group Department of Zoology Cambridge University Cambridge CB2 3QZ UK Faculty of Artificial Intelligence and Decision Making Massachusetts Institute of Technology Cambridge MA USA United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) Cambridge CB3 0DL UK Center for Macroecology Evolution and Climate University of Copenhagen DK-2100 Copenhagen Denmark School of Life Sciences University of Hawai'i at Mānoa Honolulu HI 96822 USA BirdLife International The David Attenborough Building Cambridge CB2 3QZ UK Department of Zoology University of Cambridge Downing Street Cambridge CB2 3EJ UK Department of Biology and Institute of Environmental and Interdisciplinary Science Carleton University Ottawa ON K1S 5B6 Canada Conservation Research Institute and Department of Plant Sciences Cambridge University Cambridge CB2 3QZ UK Nordic Foundation for Development and Ecology (NORDECO) Copenhagen DK-1159 Denmark Department of Geosciences and Geography FI-00014 University of Helsinki Helsinki Finland Helsinki Institute of Sustainability Science (HELSUS) FI-00014 University of Helsinki Helsinki Finland School of Life Sciences University of KwaZulu-Natal Durban 4041 South Africa Instituto de Ecología y Biodiversidad (IEB-Chile) Santiago Chile Department of Biology University of Oxford Oxford OX1 3SZ UK Department of Computer Science and Technology University of Cambridge Cambridge CB3 0FD UK School of Environmental and Natural Sciences Bangor University Bangor UK Environmental Research Centre Duke Kunshan University Kunshan Jiangsu 215316 China Nicholas School of the Environment Duke University Durham NC 27708 USA British Antarctic Survey Natural Environment Research Council (NERC) Cambridge CB3 0ET UK Institute of Zoology Zoological Society of London London NW1 4RY UK International Union for Conservation of Nature (IUCN) Species Survival Commission Calle La Joya Edificio Unidad Técnica del Este Chacao Carac

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Findings from the Tushirikiane-4-MH (Supporting Each Other for Mental Health) Mobile Health-Supported Virtual Reality Randomized Controlled Trial Among Urban Refugee Youth in Kampala, Uganda

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Global Mental Health 2025年 12卷 e12-e12页

作者： Logie, Carmen H. Okumu, Moses Admassu, Zerihun MacKenzie, Frannie Gittings, Lesley Kortenaar, Jean-Luc Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada United Nations University Institute for Water Environment & Health Hamilton ON Canada Centre for Gender and Sexuality Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign United States Uganda Christian University Mukono Uganda School of Health Studies Western University London ON Canada Centre for Social Science Research University of Cape Town South Africa Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development (YARID) Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada

Background: Virtual reality (VR) for mental health promotion remains understudied in low income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. Methods: This randomized controlled trial assessed VR alone (Arm 1), VR followed by group problem management plus (GPM+) (Arm 2), and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16-25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental wellbeing, and adaptive coping. Analyses were conducted at three timepoints (baseline, 8-weeks, 16-weeks) using generalized estimating equations (GEE). Results: Among participants (n=335, mean age: 20.77, standard deviation: 3.01;cisgender women: n=158, cisgender men: n=173, transgender women: n=4) we found no depression reductions for Arms 1 or 2 at 16-weeks compared with Arm 3. At 16-weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self compassion was significantly higher in Arms 1 and Arm 2 compared with Arm 3. Discussion: VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression. © The Author(s), 2025. Published by Cambridge University Press.

关键词： depression refugees self-compassion Uganda virtual reality youth

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Distinct post-harvest deterioration pathways caused by light vs dark storage of fresh-cut lettuce packed in modified atmosphere.

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Postharvest Biology and Technology 2025年 229卷

作者： Priscille Steensma Fanny Widjaja Sitaram Rajaraman Leevi Annala Arto Klami Jarkko Salojärvi Mari Lehtonen Kirsi S. Mikkonen Saijaliisa Kangasjärvi Department of Food and Nutrition University of Helsinki P.O. Box 66 Helsinki 00014 Finland Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki P.O. Box 65 Helsinki 00014 Finland Department of Computer Science University of Helsinki P.O. Box 68 Helsinki 00014 Finland School of Biological Sciences Nanyang Technological University 60 Nanyang Drive Singapore 637551 Singapore Singapore Centre for Environmental Life Sciences Engineering Nanyang Technological University 60 Nanyang Drive 637551 Singapore Helsinki Institute of Sustainability Science (HELSUS) University of Helsinki P.O. Box 65 Helsinki 00014 Finland Department of Agricultural Sciences Faculty of Agriculture and Forestry University of Helsinki P.O. Box 65 Helsinki 00014 Finland Viikki Plant Science Center University of Helsinki P.O. Box 65 Helsinki 00014 Finland

Modified atmosphere packaging (MAP) combined with low temperatures is a proven method for maintaining the quality of fresh-cut lettuce ( Lactuca sativa ), particularly in relation to cut surface discoloration. However, even though fresh-cut products in retail environments can be influenced by even low levels of photosynthetic light, few studies have focused on the impact of light on MAP products. In this study, we found that constant low light illumination undermines the effectiveness of MAP for fresh-cut lettuce. This is associated with a slight temperature increase in the storage cabinet and photosynthetic gas exchange restoring normal atmospheric gas compositions, accelerating quality loss. These light-induced changes are correlated with transcriptional hallmarks of senescence. In contrast, even with extended storage beyond typical retail standards, these hallmarks were absent in darkness. Instead, there were transcriptional and metabolite profile signatures related to low oxygen and oxylipin metabolism. Furthermore, the phenylpropanoid pathway, which is central to cut-surface discoloration, appeared to be dynamically responsive to lighting and increasing storage time throughout the storage period. In conclusion, this study reveals temporal transcriptomic and metabolomic responses that are highly conditioned by lighting during MAP-storage of fresh-cut lettuce.

关键词： Transcriptomics Metabolomics Senescence Phenylpropanoids Oxylipins

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Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

Research Square

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Research Square 2020年

作者： Shams, Abdullah Bin Apu, Ehsanul Hoque Rahman, Ashiqur Siddika, Nazeeba Raihan, Mohsin Sarker Hussein, Molla Rashied Mostari, Shabnam Kabir, Russell The Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Department of Biomedical Engineering Michigan State University East LansingMI48824 United States The Intervention Centre Oslo University Hospital Oslo0372 Norway Department of Computer Science Northern Illinois University DeKalbIL60115-2828 United States Faculty of Medicine University of Oulu Oulu90014 Finland Khulna9203 Bangladesh Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Programme ICT Division Dhaka Bangladesh Faculty of Health Education Medicine and Social Care School of Allied Health Anglia Ruskin University Chelmsford Essex CM1 1SQ United Kingdom

Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand;it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues. © 2020, CC BY.

关键词： COVID-19

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Embodied emission profiles of building types: guidance for emission reduction in the early phases of construction projects

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IOP Conference Series: Earth and Environmental science 2020年第1期410卷

作者： Eirik Resch Helge Brattebø Inger Andresen Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Department of Architecture and Technology Norwegian University of Science and Technology Trondheim Norway Industrial Ecology Programme Norwegian University of Science and Technology Trondheim Norway

The embodied emissions of the construction materials in buildings are a significant contributor to climate change but have only rarely been systematically studied by statistical methods. In the early phases of a building project, empirical results of statistical emission profiles of different building types can act as useful guiding information to inform decisions regarding reduced embodied emissions from construction materials. However, engineers and architects do not have such information at disposition. In this paper, the embodied emissions from the production and transport of initial and recurring building material use in 7 Norwegian case studies of low-emission buildings are made comparable and then studied statistically to find out how the impact varies with building types. The building types studied are timber residential, concrete office, concrete school, and concrete swimming hall. Statistics are produced for each building type and are broken down by the impact contribution from different building elements and material categories. This results in embodied emission profiles and material use profiles for these four building types, which, when based on a larger dataset, can be used by architects and engineers to make informed decisions when aiming for reduced embodied emissions in the early phases of a construction project. Additionally, these profiles can be used as benchmarks by which the final building can be compared when the building is constructed. The statistical results are preliminary and based on a limited dataset, which makes them applicable only as an indication for Norwegian low-emission buildings of these four building types. Future work includes expansion of the dataset on which the profiling is based, further development of the statistical method, and applying the methodology to additional building types.

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