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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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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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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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Secure data sharing for mobile cloud computing using RSA

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IOP Conference Series: Materials science and Engineering 2021年第1期1055卷

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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