Gotowe bibliografie tematyczne / Relevance feedback

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Relevance feedback”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Relevance feedback"

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Raiber, Fiana, and Oren Kurland. "Relevance Feedback." ACM Transactions on Information Systems 37, no. 4 (2019): 1–28. http://dx.doi.org/10.1145/3360487.

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Giorgi, D., P. Frosini, M. Spagnuolo, and B. Falcidieno. "3D relevance feedback via multilevel relevance judgements." Visual Computer 26, no. 10 (2010): 1321–38. http://dx.doi.org/10.1007/s00371-010-0524-0.

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Zhou, Dong, Mark Truran, Jianxun Liu, and Sanrong Zhang. "Collaborative pseudo-relevance feedback." Expert Systems with Applications 40, no. 17 (2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.

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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique wa
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Chen, Zhaofeng, Naixuan Guo, Jiu Sun, et al. "Pseudo-Relevance Feedback Method Based on the Topic Relevance Model." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–6. http://dx.doi.org/10.1155/2022/1697950.

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In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback d
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Panyr, Jiri. "Conceptual Clustering and Relevance Feedback." KNOWLEDGE ORGANIZATION 14, no. 3 (1987): 133–37. http://dx.doi.org/10.5771/0943-7444-1987-3-133.

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Crouch, Carolyn J. "Relevance feedback at INEX 2005." ACM SIGIR Forum 40, no. 1 (2006): 58–59. http://dx.doi.org/10.1145/1147197.1147208.

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MR., SHINDE SURESH GOROBA, and C. M. JADHAV PROF. "MINING USER NAVIGATION PATTERNS FOR EFFICIENT RELEVANCE FEEDBACK FOR CBIR." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 2 (2018): 4–6. https://doi.org/10.5281/zenodo.1464589.

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 In today’s modernized world, content based image retrieval (CBIR) is considered as a bastion in image retrieval system. For making CBIR most suitable and productive technique, relevance feedback technique is used in conjunction with CBIR for producing more specific results which are obtained by taking feedback from user. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks for production of best search results, particularly in huge database. But this seems of no use in real world applications. In this paper, we propose
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Widyanto, M. Rahmat, and Tatik Maftukhah. "Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (2010): 34–38. http://dx.doi.org/10.20965/jaciii.2010.p0034.

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Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are:
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Algarni, Abdulmohsen, Yuefeng Li, Sheng-Tang Wu, and Yue Xu. "Text mining in negative relevance feedback." Web Intelligence and Agent Systems: An International Journal 10, no. 2 (2012): 151–63. http://dx.doi.org/10.3233/wia-2012-0238.

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Rozprawy doktorskie na temat "Relevance feedback"

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Zhang, Peng. "Approximating true relevance model in relevance feedback." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/808.

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Relevance is an essential concept in information retrieval (IR) and relevance estimation is a fundamental IR task. It involves not only document relevance estimation, but also estimation of user's information need. Relevance-based language model aims to estimate a relevance model (i.e., a relevant query term distribution) from relevance feedback documents. The true relevance model should be generated from truly relevant documents. The ideal estimation of the true relevance model is expected to be not only effective in terms of mean retrieval performance (e.g., Mean Average Precision) over all
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Xu, Zuobing. "Active relevance feedback algorithms /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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Yamout, Fadi. "Relevance Feedback Using Weight Propagation." Thesis, University of Sunderland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485442.

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A new relevance feedback technique called Weight Propagation has been developed which provides greater retrieval effectiveness than previously described techniques. Documents judged relevant by the user propagate positive Weights to documents close by in vector similarity space, while documents judged not relevant propagate Negative Weights to such neighbouring documents. Variants of Weight Propagation are also described, namely WPI and WPR (inspired by Ide and Rocchio respectively), and WPY which is the main focus of this thesis, where only the maximum weight propagated to each document is co
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Ruthven, Ian. "Abduction, explanation and relevance feedback." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392605.

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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.

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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability
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Vinay, V. "The relevance of feedback for text retrieval." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446146/.

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Relevance Feedback is a technique that helps an Information Retrieval system modify a query in response to relevance judgements provided by the user about individual results dis played after an initial retrieval. This thesis begins by proposing an evaluation framework for measuring the effectiveness of feedback algorithms. The simulation-based method in volves a brute force exploration of the outcome of every possible user action. Starting from an initial state, each available alternative is represented as a traversal along one branch of a user decision tree. The use of the framework is illust
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Gusenbauer, Stefan. "Relevance feedback in information retrieval a comparison including a practical evaluation of several approaches to relevance feedback in information retrieval." Saarbrücken VDM Verlag Dr. Müller, 2006. http://d-nb.info/989497259/04.

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Jin, Lan. "Relevance feedback in the retrieval of reusable software components." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22831.pdf.

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Djordjevic, Divna. "User relevance feedback, search and retrieval of visual content." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432897.

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Ng, Chee Un. "Image retrieval using experience-based relevance feedback and visualisation." Thesis, University of Warwick, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425556.

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Książki na temat "Relevance feedback"

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Koenemann-Belliveau, Jürgen. Relevance feedback: Usage, usability, utility. UMI Dissertation Services, 2001.

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Lavrenko, Victor. A generative theory of relevance. Springer, 2009.

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Kelly, Barry. Information retrieval on the WWW and the issue of relevance feedback. The Author], 1998.

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E, Robertson S. An operational evaluation of weighting, ranking, and relevance feedback, via a front-end system. City University, Dept. of Information Science, 1987.

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Srivastava, Tripti. Formative Assessment and Feedback: Recognizing the Relevance. LAP LAMBERT Academic Publishing, 2013.

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Lavrenko, Victor. Generative Theory of Relevance. Springer, 2010.

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Lavrenko, Victor. Generative Theory of Relevance. Springer London, Limited, 2008.

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Enhancing performance of full-text retrieval systems using relevance feedback. University Microfilms International, 1993.

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Rangayyan, Rangaraj, and Paulo Mazzoncini de Azevedo-Marques. Content-Based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback. Springer International Publishing AG, 2012.

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Rangayyan, Rangaraj, and Paulo Mazzoncini de Azevedo-Marques. Content-Based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback. Morgan & Claypool Publishers, 2013.

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Części książek na temat "Relevance feedback"

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He, Ben. "Relevance Feedback." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_931.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, et al. "Relevance Feedback." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_724.

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Fang, Hui, and Cheng Xiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_462-2.

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Fang, Hui, and ChengXiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_462.

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Zhang, Hongjiang, and Zhong Su. "Relevance Feedback in CBIR." In Visual and Multimedia Information Management. Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35592-4_3.

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Fang, Hui, and Cheng Xiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_462.

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Vechtomova, Olga. "Relevance Feedback for Text Retrieval." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_949-2.

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Vechtomova, Olga. "Relevance Feedback for Text Retrieval." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_949.

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Pan, Hanglin. "Relevance Feedback in XML Retrieval." In Current Trends in Database Technology - EDBT 2004 Workshops. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30192-9_18.

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Crucianu, Michel. "Image Retrieval and Relevance Feedback." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1016.

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Streszczenia konferencji na temat "Relevance feedback"

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Haas, Micha, Joachim Rijsdam, Bart Thomee, and Michael S. Lew. "Relevance feedback." In the 6th ACM SIGMM international workshop. ACM Press, 2004. http://dx.doi.org/10.1145/1026711.1026737.

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Harper, David J., and Diane Kelly. "Contextual relevance feedback." In the 1st international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1164820.1164847.

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Harman, Donna. "Relevance feedback revisited." In the 15th annual international ACM SIGIR conference. ACM Press, 1992. http://dx.doi.org/10.1145/133160.133167.

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Aalbersberg, IJsbrand Jan. "Incremental relevance feedback." In the 15th annual international ACM SIGIR conference. ACM Press, 1992. http://dx.doi.org/10.1145/133160.133169.

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Crane, Matt. "Diversified relevance feedback." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2013. http://dx.doi.org/10.1145/2484028.2484227.

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Lv, Yuanhua, and ChengXiang Zhai. "Positional relevance model for pseudo-relevance feedback." In Proceeding of the 33rd international ACM SIGIR conference. ACM Press, 2010. http://dx.doi.org/10.1145/1835449.1835546.

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Brondwine, Elinor, Anna Shtok, and Oren Kurland. "Utilizing Focused Relevance Feedback." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911451.2914695.

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Amati, G. "Learning by examples as relevance feedback and relevance feedback as learning by examples." In IEE Two-day Seminar. Searching for Information: Artificial Intelligence and Information Retrieval Approaches. IEE, 1999. http://dx.doi.org/10.1049/ic:19990889.

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Iwayama, Makoto. "Relevance feedback with a small number of relevance judgements." In the 23rd annual international ACM SIGIR conference. ACM Press, 2000. http://dx.doi.org/10.1145/345508.345538.

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Gemmell, Carlos, Federico Rossetto, and Jeffrey Dalton. "Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2020. http://dx.doi.org/10.1145/3397271.3401215.

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Raporty organizacyjne na temat "Relevance feedback"

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Demner-Fushman, Dina, Daqing He, and Douglas W. Oard. Exploring Interactive Relevance Feedback With a Two-Pass Study Design. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada453566.

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Diaz, Fernando, and James Allan. When Less is More: Relevance Feedback Falls Short and Term Expansion Succeeds at HARD 2005. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada444098.

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van Gestel, Natasja, Kees Jan van Groenigen, Craig Osenberg, Jeffrey Dukes, and Paul Dijkstra. Biogeochemical Responses and Feedbacks to Climate Change: Synthetic Meta-Analyses Relevant to Earth System Models. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1429337.

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Hedge, Jerry W., Walter C. Borman, Mark J. Bourne, and Mark C. Butler. A Review of Navy Survey Data Relevant to Implementation and Evaluation of the Human Performance Feedback and Development Model. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada524847.

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Coultas, Mimi, Ruhil Iyer, and Jamie Myers. Handwashing Compendium for Low Resource Settings: A Living Document, Edition 3. Institute of Development Studies (IDS), 2020. http://dx.doi.org/10.19088/slh.2020.001.

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Frequent and proper handwashing with soap is vital to prevent the spread of COVID-19. In response, the Sanitation Learning Hub at the Institute of Development Studies has developed this Handwashing Compendium for Low Resource Settings. Bringing existing information from different organisations into one place, the compendium provides guidance, local examples of and further resources on accessible low-cost handwashing facilities, environmental cues and physically distanced hygiene promotion. The compendium is a living document which will be updated regularly as learning emerges. It has been deve
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Coultas, Mimi, Ruhil Iyer, and Jamie Myers. Handwashing Compendium for Low Resource Settings: A Living Document, Edition 3. Institute of Development Studies (IDS), 2020. http://dx.doi.org/10.19088/slh.2020.008.

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Frequent and proper handwashing with soap is vital to prevent the spread of COVID-19. In response, the Sanitation Learning Hub at the Institute of Development Studies has developed this Handwashing Compendium for Low Resource Settings. Bringing existing information from different organisations into one place, the compendium provides guidance, local examples of and further resources on accessible low-cost handwashing facilities, environmental cues and physically distanced hygiene promotion. The compendium is a living document which will be updated regularly as learning emerges. It has been deve
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Pereiro, Diego, Oleg Belyaev Korolev, Martha B. Dunbar, Garbriel Navarro, and Caroline Cusack. Best practice on creating "Extreme Marine Events" Hazard maps & forecasts Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d6.3.

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This report presents steps for the design and implementation of a marine observatory providing current and forecasted oceanic conditions relevant to the aquaculture sector, with particular focus on “Extreme Marine Events”. Examples of successful implementation of these guidelines in the framework of the EuroSea project are presented for two aquaculture sites: Deenish Island in Ireland and El Campello in Spain. The process starts with stakeholder interaction to understand their main needs and concerns and is followed by the design of the software architecture that carries out the data acquisiti
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Cole, Amanda L., and Dawn Reid. The Living Room Peer Support Training: Leading the Way in Unlocking Mental Health Benefits for University Students in Peer-led Service Models in Higher Education. Journal of the Australian and New Zealand Student Services Association, 2024. http://dx.doi.org/10.30688/janzssa.2024-2-01.

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This paper presents the development and execution of a specialised university training program designed to support Peer Support Advisors (PSAs) within The Living Room: a low-barrier peer-led mental health service. The primary objective was to investigate the training program's impact on the knowledge and attitudes of PSAs. Two questionnaires were administered: a pre-training questionnaire to establish participants' initial attitudes and knowledge levels, and a post-training questionnaire to assess any changes following completion of the program. Additionally, a feedback questionnaire was used
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Agrawal, Asha Weinstein, Jon Canapary, Carol Anne Carroll, Tam Guy, and Anastasia Loukaitou-Sideris. Learning about Street Harassment on Transit: A Survey Instrument for Transit Agencies. Mineta Transporation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2301.

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This study was conducted in accordance with California Senate Bill 1161 (2022), legislation that directed the Mineta Transportation Institute to develop a reliable, easy-to-use survey instrument for collecting information from public transit passengers about the extent, location, and characteristics of any street harassment they experience. Although the legislation is focused on large California transit agencies, the study findings are equally relevant to smaller agencies and agencies outside the state. To develop the recommended questionnaire, we reviewed literature about street harassment, a
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Carter, Becky. Integrating Local Voices into Programme Governance in Fragile and Conflict-Affected Settings. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.110.

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This rapid literature review explores how local people’s views and perspectives on their concerns, needs and capabilities (beyond asking about their experiences with aid) have been integrated into the national-level governance mechanisms of humanitarian assistance and development programmes in fragile and conflict-affected settings. There is limited systematic evidence available on this topic. There are a few cases of including civil society in national-level programme or sector governance bodies; there is more documented experience of including local actors in humanitarian response coordinati
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