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Статті в журналах з теми "Training data recommendation":
1
Komurlekar, Runali. "Movie Recommendation Model from Data through Online Streaming." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1549–51. http://dx.doi.org/10.22214/ijraset.2021.37495.
Анотація:
Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern
2
Adnan, Muhammad, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, and Prashant J. Nair. "Accelerating recommendation system training by leveraging popular choices." Proceedings of the VLDB Endowment 15, no. 1 (September 2021): 127–40. http://dx.doi.org/10.14778/3485450.3485462.
Анотація:
Recommender models are commonly used to suggest relevant items to a user for e-commerce and online advertisement-based applications. These models use massive embedding tables to store numerical representation of items' and users' categorical variables (memory intensive) and employ neural networks (compute intensive) to generate final recommendations. Training these large-scale recommendation models is evolving to require increasing data and compute resources. The highly parallel neural networks portion of these models can benefit from GPU acceleration however, large embedding tables often cannot fit in the limited-capacity GPU device memory. Hence, this paper deep dives into the semantics of training data and obtains insights about the feature access, transfer, and usage patterns of these models. We observe that, due to the popularity of certain inputs, the accesses to the embeddings are highly skewed with a few embedding entries being accessed up to 10000X more. This paper leverages this asymmetrical access pattern to offer a framework, called FAE, and proposes a hot-embedding aware data layout for training recommender models. This layout utilizes the scarce GPU memory for storing the highly accessed embeddings, thus reduces the data transfers from CPU to GPU. At the same time, FAE engages the GPU to accelerate the executions of these hot embedding entries. Experiments on production-scale recommendation models with real datasets show that FAE reduces the overall training time by 2.3X and 1.52X in comparison to XDL CPU-only and XDL CPU-GPU execution while maintaining baseline accuracy.
3
Wang, Qingren, Min Zhang, Tao Tao, and Victor S. Sheng. "Labelling Training Samples Using Crowdsourcing Annotation for Recommendation." Complexity 2020 (May 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/1670483.
Анотація:
The supervised learning-based recommendation models, whose infrastructures are sufficient training samples with high quality, have been widely applied in many domains. In the era of big data with the explosive growth of data volume, training samples should be labelled timely and accurately to guarantee the excellent recommendation performance of supervised learning-based models. Machine annotation cannot complete the tasks of labelling training samples with high quality because of limited machine intelligence. Although expert annotation can achieve a high accuracy, it requires a long time as well as more resources. As a new way of human intelligence to participate in machine computing, crowdsourcing annotation makes up for shortages of machine annotation and expert annotation. Therefore, in this paper, we utilize crowdsourcing annotation to label training samples. First, a suitable crowdsourcing mechanism is designed to create crowdsourcing annotation-based tasks for training sample labelling, and then two entropy-based ground truth inference algorithms (i.e., HILED and HILI) are proposed to achieve quality improvement of noise labels provided by the crowd. In addition, the descending and random order manners in crowdsourcing annotation-based tasks are also explored. The experimental results demonstrate that crowdsourcing annotation significantly improves the performance of machine annotation. Among the ground truth inference algorithms, both HILED and HILI improve the performance of baselines; meanwhile, HILED performs better than HILI.
4
劉怡, 劉怡. "Research of Art Point of Interest Recommendation Algorithm Based on Modified VGG-16 Network." 電腦學刊 33, no. 1 (February 2022): 071–85. http://dx.doi.org/10.53106/199115992022023301008.
Анотація:
<p>Traditional point of interest (POI) recommendation algorithms ignore the semantic context of comment information. Integrating convolutional neural networks into recommendation systems has become one of the hotspots in art POI recommendation research area. To solve the above problems, this paper proposes a new art POI recommendation model based on improved VGG-16. Based on the original VGG-16, the improved VGG-16 method optimizes the fully connection layer and uses transfer learning to share the weight parameters of each layer in VGG-16 pre-training model for subsequent training. The new model fuses the review information and user check-in information to improve the performance of POI recommendation. Experiments on real check-in data sets show that the proposed model has better recommendation performance than other advanced points of interest recommendation methods.</p> <p> </p>
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Daniel, Thomas, Fabien Casenave, Nissrine Akkari, and David Ryckelynck. "Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics." Mathematical and Computational Applications 26, no. 1 (February 16, 2021): 17. http://dx.doi.org/10.3390/mca26010017.
Анотація:
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data come from numerical simulations and generally correspond to physical fields discretized on a mesh. Three challenging difficulties arise: the lack of training data, their high dimensionality, and the non-applicability of common data augmentation techniques to physics data. This article introduces two algorithms to address these issues: one for dimensionality reduction via feature selection, and one for data augmentation. These algorithms are combined with a wide variety of classifiers for their evaluation. When combined with a stacking ensemble made of six multilayer perceptrons and a ridge logistic regression, they enable reaching an accuracy of 90% on our classification problem for nonlinear structural mechanics.
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Salenko, A. A., and E. V. Morar. "DESIGN AND DEVELOPMENT OF A MOVIE RECOMMENDATION SERVICE." Applied Mathematics and Fundamental Informatics 8, no. 2 (2021): 046–53. http://dx.doi.org/10.25206/2311-4908-2021-8-1-46-53.
Анотація:
The article discusses two components of the service: the server part of the application for user interaction and the recommendation algorithm embedded in this service. In the process of work, training data was collected and processed, a neural network was designed and trained, recommendations were generated based on various filtering algorithms. The result of the work is a service for the selection of films
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Xu, Gaochao, Yan Ding, Yuqiang Jiang, Ming Hu, and Jia Zhao. "A Novel Distributed Recommendation Framework Using Big Data in Social Context." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1759015. http://dx.doi.org/10.1142/s0218001417590157.
Анотація:
Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.
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Zhang, Heng-Ru, Fan Min, and Xu He. "Aggregated Recommendation through Random Forests." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/649596.
Анотація:
Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy.
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Nanry, Charles. "Performance Linked Training." Public Personnel Management 17, no. 4 (December 1988): 457–63. http://dx.doi.org/10.1177/009102608801700409.
Анотація:
Linking training to employee performance and development is an important consideration in justifying particular programs for particular employees. The new Performance Assessment Review (PAR) system attempts to do just that for New Jersey state employees. PAR forces supervisors and managers “to go on record” in the recommendation of specified training to remedy the needs of individual employees. Through the aggregation of PAR generated data the new system also provides a powerful tool for the assessment of broad training needs across agencies and job classes.
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Zamani, Hamed. "Neural models for information retrieval without labeled data." ACM SIGIR Forum 53, no. 2 (December 2019): 104–5. http://dx.doi.org/10.1145/3458553.3458569.
Анотація:
Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval [9]. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or weakly supervised solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models [3] that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification [1, 2]. We further propose a standalone learning to rank model based on deep neural networks [5, 8]. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query [7]. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems [4]. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training [6]. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available.
Дисертації з теми "Training data recommendation":
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Labiadh, Mouna. "Méthodologie de construction de modèles adaptatifs pour la simulation énergétique des bâtiments." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSE1158.
Анотація:
La modélisation prédictive au sein des bâtiments est essentielle pour le contrôle intelligent, la coordination et la planification efficaces des réseaux d'énergie. L'un des moyens de modélisation prédictive utilise l'apprentissage automatique. En plus de leur bonne performance, ces approches sont rapides et permettent une intégration facile du bâtiment dans des systèmes intelligents. Cependant, un modèle d'apprentissage précis s'appuie essentiellement sur la disponibilité des données historiques en quantité suffisante, notamment quand l'apprentissage profond est utilisé. Dans le domaine d'énergie des bâtiments, les données historiques ne sont pas disponibles pour l'entraînement, notamment dans le cas des bâtiments nouvellement construits et nouvellement rénovés. En outre, il est fréquent d'évaluer l'efficacité énergétiques des bâtiments avant leur construction ou rénovation. Dans de tels cas, on dispose uniquement d'une description contextuelle du bâtiment futur et de sa conception. Cette thèse s'intéresse à la tâche de modélisation prédictive de la consommation énergétique des bâtiments quand aucune donnée historique n'est disponible. Pour cela, des données collectées à partir de plusieurs différents bâtiments sources sont exploitées. Ceci est de plus en plus pertinent compte tenu la croissance des initiatives de données ouvertes dans plusieurs secteurs, dont celui de l'énergie. Ainsi, l'idée est de transférer la connaissance entre les modèles de bâtiments. Peu de travaux de recherche sont menés à l'intersection des domaines de modélisation de l'énergie des bâtiments et le transfert d'apprentissage. Le traitement de données multi-sources constitue un défi majeur, vu l'écart de concept qui peut exister entre les différents sources et aussi entre chaque source et cible. Comme contribution, on propose une méthodologie de modélisation prédictive adaptative aux requêtes des utilisateurs. Le premier processus est responsable de la recommandation de données d'apprentissage pertinentes vis-à-vis un bâtiment cible, seulement en utilisant une description contextuelle minimale sur ce dernier (métadonnées). La description contextuelle est modélisée en tant que requête utilisateur. Pour permettre des recommandations spécifiques à la tâche cible, notre approche se base sur l'apprentissage profond de métrique de similarité. Le second processus est responsable de l'entraînement de plusieurs modèles prédictifs sur les données d'apprentissage recommandées par le processus précédent. Ces modèles sont combinés avec une méthode ensembliste pour assurer une bonne performance. L'implémentation de la méthodologie est basée sur les microservices. Les processus indépendants sont, par conséquent, modélisés en tant que microservices à but unique et à source de données séparée. Les métadonnées des bâtiments et leurs séries temporelles recueillies auprès de nombreuses sources sont intégrées au sein d'une vue unifiée et basée sur des ontologies. Les évaluations expérimentales de la méthodologie valident son efficacité et son applicabilité à la tâche de modélisation énergétique des bâtiments. Par ailleurs, vu le caractère générique de sa conception, la méthodologie peut être réutilisée dans d'autres applications dans divers secteursPredictive modeling of energy consumption in buildings is essential for intelligent control and efficient planning of energy networks. One way to perform predictive modeling is through machine learning approaches. Alongside their good performance, these approaches are time efficient and facilitates the integration of buildings into smart environments. However, accurate machine learning models rely heavily on collecting relevant building operational data in a sufficient amount, notably when deep learning is used. In the field of buildings energy, historical data are not available for training, such is the case in newly built or newly renovated buildings. Moreover, it is common to verify the energy efficiency of buildings before construction or renovation. For such cases, only a contextual description about the future building and its design is available. The goal of this dissertation is to address the predictive modeling tasks of building energy consumption when no historical data are available for the given target building. To that end, existing data collected from multiple different source buildings are leveraged. This is increasingly relevant with the growth of open data initiatives in various sectors, namely building energy. The main idea is to transfer knowledge across building models. There is little research at the intersection of building energy modeling and knowledge transfer. An important challenge arises when dealing with multi-source data, since large domain shift may exist between different sources and also between each source and the target. As a contribution, a two-fold query-adaptive methodology is developed for cross-building predictive modeling. The first process recommends relevant training data to a target building solely by using a minimal contextual description on it (metadata). Contextual descriptions are provided as user queries. To enable a task-specific recommendation, a deep similarity learning framework is used. The second process trains multiple predictive models based on recommended training data. These models are combined together using an ensemble learning framework to ensure a robust performance. The implementation of the proposed methodology is based on microservices. Logically independent workflows are modeled as microservices with single purposes and separate data sources. Building metadata and time series data collected from multiple sources are integrated into an unified ontology-based view. Experimental evaluation of the predictive model factory validates the effectiveness and the applicability for the use case of building energy modeling. Moreover, because of its generic design, the methodology for query-adaptive cross-domain predictive modeling can be re-used for a diverse range of use cases in different fields
Книги з теми "Training data recommendation":
1
Program, CIMMYT Economics. From agronomic data to farmer recommendations: An economics training manual. México, D.F., México: CIMMYT Economics Program, 1988.
2
Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Анотація:
The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".
The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.
Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
3
Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
Анотація:
Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI.
The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.
Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
4
Mandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov, and Aleksandr Legkih. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. ru: TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.
Анотація:
The training manual is devoted to the problem of herpetic infection in dentistry and was developed taking into account world scientific and clinical practice, experience working on clinical recommendations of the Ministry of Health of the Russian Federation, as well as experimental, laboratory and clinical data obtained by the authors. This manual presents materials related to modern ideas about the etiology and pathogenesis of herpetic infection, modern diagnostic methods are highlighted, and current complex treatment algorithms are proposed, and clinical cases are presented. Recommended as a guide for practitioners of various specialties, clinical residents, senior students.
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Basovskiy, Leonid, and Elena Basovskaya. Fundamentals of scientific research. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1192099.
Анотація:
The textbook outlines the concepts and methods of conducting scientific research and presenting their results. The fundamentals of scientific thinking and methodological foundations of scientific research are considered. The process of scientific research, methods of obtaining and analyzing data, collecting and summarizing scientific information, the sequence of the process of scientific research are described. The approaches, methods, rules and norms of preparation of reports on the results of scientific research, scientific articles and dissertations are described. The organization, financing of scientific research and training of scientific personnel in the Russian Federation, the organization of research work of students, the principles of scientific ethics are characterized. Recommendations for the preparation of final qualifying papers and dissertations are given.
The textbook includes topics and questions, the study of which is necessary to master the competencies provided for by the federal state educational standards of higher education of the latest generation.
It is intended for undergraduate, graduate and postgraduate students of the enlarged group of training areas 38.00.00 "Economics and Management".
6
Naumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.
Анотація:
The book describes the basic issues concerning consumer behavior on the basis of the simulation of the decision-making process on buying behavior of customers in the sales area of the store and shopping Internet sites.
The classification of models of consumer behavior, based on research in the area of economic, social and psychological theories and empirical evidence regarding decision-making by consumers when purchasing the goods, including online stores. Methods of qualitative and quantitative research of consumer behavior, fundamentals of statistical processing of empirical data.
Attention is paid to the processes of consumers ' perception of brands (brands) and advertising messages, the basic rules for the display of goods (merchandising) and its impact on consumer decision, recommendations on the use of psychology of consumer behavior in personal sales.
Presents an integrated model of consumer behavior in the Internet environment, the process of perception of the visitor of the company, the factors influencing consumer choice of goods online.
Is intended for preparation of bachelors in directions of preparation 38.03.02 "Management", 38.03.06 "trading business" and can be used for training of bachelors in direction of training 43.03.01 "Service", and will also be useful for professionals working in the field of marketing, distribution and sales.
7
Office, General Accounting. Financial management: Recommendations on Indian trust fund Strategic Plan proposals : report to the Secretary of the Interior. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1997.
8
Dallmeijer, Annet, and Jost Schnyder. Exercise capacity and training in cerebral palsy and other neuromuscular diseases. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0035.
Анотація:
Chapter 35 gives an understanding of the role of exercise in the functional assessment and clinical management of children with neuromuscular diseases, especially for children with CP and PMD. Current knowledge about exercise capacity and training possibilities with respect to the different fitness components (aerobic power, anaerobic power, muscular strength) will be described as well as the level of physical activity and training recommendations. Practical advice and suggestions are given on how to build up and execute an adapted programme for physical activity, sports, and exercise. Data will be summarized to recognize the possibilities as well as the limits of exercise, and also to permit a regular evaluation and a constant adaptation of a physical activity programme.
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Bellosta-López, Pablo, Priscila de Brito Silva, Palle S. Jensen, Morten S. Hoegh, Thorvaldur S. Palsson, Steffan Wittrup Mc Phee Christensen, Julia Blasco-Abadía, et al. Recommendations for implementation of the topic musculoskeletal disorders in the occupational health and safety postgraduate programmes at European Universities. Prevent4Work, 2021. http://dx.doi.org/10.54391/123456789/672.
Анотація:
Historically, the role of professionals specialized in occupational health and safety (OHS) has emerged from the need to protect employers working in major risk industries such as nuclear plants and large-scale chemical industries in Europe. More recently, a few studies highlighted that the range of activities linked to safety management responsibilities includes monitor and prepare reports, inspection and auditing, regulatory compliance, emergency response, incident investigation, hazard and risk assessment, and training. Additionally, there are some supplementary non-safety related duties, such as including environmental responsibility. Considering that work-related musculoskeletal disorders (WRMD) are a major burden worldwide, adding up to 1.3 billion cases, more than 100 million years loss of disability-adjusted life years and that such disorders are common causes of disability and sick leave, this topic is highly relevant to OHS professionals. In EU Member States for which data are available, a large majority of all workers report complaints related to musculoskeletal disorders as their most serious work-related health problems. The percentage of workers reporting such complaints as their most serious health problem ranges from 40 % in Luxembourg to 70 % in Czech Republic and Finland. Furthermore, more than half of workers with musculoskeletal disorders reported taking time off work in a 12-month period. In the EU, 26 % of workers with musculoskeletal long-lasting disorders, that is lasting over 3 months, combined with other health problems report more than 8 days of absence per year. Higher Education Institutions (HEI) have a key role in disseminating and increasing accessibility to the most up-to-date evidence available regarding the impact and management of musculoskeletal disorders, to facilitate translation of knowledge to implementation in practice. This way, the Knowledge Alliance Prevent4Work for Preventing Work-Related Musculoskeletal Disorders has elaborated this document with the most recent and relevant knowledge within the topic. HEI that offer courses within OHS as well as graduation and post-graduation courses for health professionals that work within the area, may benefit from the recommendations presented here.
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Nahir, Menachem, Doron Zahger, and Yonathan Hasin. Recommendations for the structure, organization, and operation of intensive cardiac care units. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0010.
Анотація:
Care for the critically ill cardiovascular patients and their families requires a unique environment that is structurally different from other clinical units. Coronary care units were introduced in the 1960s for the main purpose of prevention and prompt treatment of life-threatening cardiac arrhythmias related to acute myocardial infarction. Since then, major progress in cardiology in general and acute cardiac care, in particular, dictated a major change in the structure and organization of these units, symbolically expressed in the new title of ‘intensive cardiac care unit’. Contemporary intensive cardiac care units receive older and more complex patients, often with multiple comorbidities and diverse diagnoses. The modern intensive cardiac care unit incorporates sophisticated monitoring and up-to-date equipment to meet the changing needs of the patient with cardiovascular disease requiring critical care. The intensive cardiac care unit operates in the centre of the hospital’s cardiology service, receiving patients from the mobile care unit (directly or via an ST elevation myocardial infarction network), the emergency department, and other wards, including coronary, structural, and electrophysiology intervention laboratories and operating rooms. Patients are usually unstable and require immediate full attention by highly trained medical and nursing staff. The 2005 recommendations for the structure, organization, and operations of the intensive cardiac care unit were issued by Hasin et al. for the Working Group of Acute Cardiac Care of the European Society of Cardiology, which serves as basis for this chapter. The chapter will focus on the requirements for staffing, training, and accreditation, as well as the structure organization and equipment of the intensive and intermediate cardiac care units.
Частини книг з теми "Training data recommendation":
1
Sun, Hao, Yunzhuo Wang, Jingwei Sun, and Guangzhong Sun. "Fast Training of POI Recommendation Models Using Gradient Compression." In Spatial Data and Intelligence, 72–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69873-7_6.
2
Pohlmann, Andreas, Susan J. Back, Andrea Fekete, Iris Friedli, Stefanie Hectors, Neil Peter Jerome, Min-Chi Ku, et al. "Recommendations for Preclinical Renal MRI: A Comprehensive Open-Access Protocol Collection to Improve Training, Reproducibility, and Comparability of Studies." In Methods in Molecular Biology, 3–23. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_1.
Анотація:
AbstractRenal MRI holds incredible promise for making a quantum leap in improving diagnosis and care of patients with a multitude of diseases, by moving beyond the limitations and restrictions of current routine clinical practice. Clinical and preclinical renal MRI is advancing with ever increasing rapidity, and yet, aside from a few examples of renal MRI in routine use, it is still not good enough. Several roadblocks are still delaying the pace of progress, particularly inefficient education of renal MR researchers, and lack of harmonization of approaches that limits the sharing of results among multiple research groups.Here we aim to address these limitations for preclinical renal MRI (predominantly in small animals), by providing a comprehensive collection of more than 40 publications that will serve as a foundational resource for preclinical renal MRI studies. This includes chapters describing the fundamental principles underlying a variety of renal MRI methods, step-by-step protocols for executing renal MRI studies, and detailed guides for data analysis. This collection will serve as a crucial part of a roadmap toward conducting renal MRI studies in a robust and reproducible way, that will promote the standardization and sharing of data.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.
3
Levy, Raymond A., and Milton Kotelchuck. "Fatherhood and Reproductive Health in the Antenatal Period: From Men’s Voices to Clinical Practice." In Engaged Fatherhood for Men, Families and Gender Equality, 111–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75645-1_6.
Анотація:
AbstractThere is very limited literature on the experiences of fathers during Obstetric prenatal care (PNC), especially hearing from fathers’ voices directly. The MGH Fatherhood Project conducted two annual surveys—data combined for analysis—of all fathers who accompanied their partners to prenatal care visits over 2-week periods at a large, tertiary-care urban hospital in Boston, MA. The anonymous, voluntary close-ended survey was offered in multiple languages and self-administered on iPads.Results: Nine hundred fifty nine fathers participated, 86% of attending fathers, possibly making the study the largest research sample of fathers in PNC. Fathers are actively and deeply engaged with the impending birth; they have substantial physical health needs (obesity, family planning and lack of primary care), and mental health needs (stress, depressive symptoms, and personal isolation). Fathers perceived they were well treated during the PNC visit, but were desirous of more reproductive, relational, and infant health information and skills, which they preferred to receive from publications, social media, or health professionals; and they were very supportive of PNC fatherhood initiatives.Discussion: The results suggest five sets of practical recommendations to create a more father-friendly environment in Obstetric care-Staff Training; Father-Friendly Clinic Environment; Explicit Affirmation of Father Inclusion; Development of Educational Materials; and Specialized Father-Focused Health Initiatives, all with the goal of improving reproductive health outcomes for families.
4
Prosvetov, A. V. "Using the Generative Adversarial Network to Generate Recommendations." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200680.
Анотація:
Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.
5
Xing, Hao, Zhike Han, and Yichen Shen. "ClothNet: A Neural Network Based Recommender System." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200706.
Анотація:
The traditional collaborative filtering recommendation systems have many deficiencies, which make them incompetent in the domain of clothing recommendation; we proposed a new ClothNet model based on CNN, RNN, collaborative filtering and the characteristics of the fashion industry. The accuracy and generalization performance of this model are improved compared with traditional systems. The visual information integrated into the ClothNet model enables the recommendation system to alleviate the cold start problem, and new clothes can be added to the recommendation list faster through the visual information. The addition of temporal information enables ClothNet sharply capturing the impact of seasonal and time changes on user preferences. However, because RNN and CNN have the disadvantage of requiring a large amount of data, combining RNN and CNN will make the model more difficult to converge, so we have adopted the LearningToRank training mode and obtained good results.
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S., Aravindha Ramanan. "Recommender System Techniques and Approaches to Improve the Modern Learning Challenges." In Machine Learning Approaches for Improvising Modern Learning Systems, 114–43. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5009-0.ch005.
Анотація:
Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top ‘N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.
7
Goncalves, Marlene, Patrick Rengifo, Daniela Andreina Rodríguez, and Ivette C. Martínez. "A Route Recommender System Based on Current and Historical Crowdsourcing." In Social Media Data Extraction and Content Analysis, 114–36. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0648-5.ch005.
Анотація:
Due to the rise of the social networks it's possible to use techniques based on crowdsourcing to easily gather real-time information directly from citizens in order to create recommendation systems capable to employ knowledge that is shared from the crowd. Particularly, in Twitter, the users publish a big amount of short messages; however, to automatically extract useful information from Twitter is a complex task. In order to provide an informed recommendation of the current best route between two city points, this chapter introduces a workflow that integrates natural language techniques to build an vector of features for training two linear classifiers which obtain current information from Twitter, and integrates that information with historical information about possible routes using exponential smoothing; current and historical data to feed a route selection algorithm based on Dijkstra. The effectiveness of the proposed workflow is shown with routes between two interest points in Caracas (Venezuela).
8
Chen, Shaoquan, Jianping Cai, and Lan Sun. "Rényi Differential Privacy Protection Algorithm for SVD Recommendation Model." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210171.
Анотація:
With the widely use of recommendation systems in various mobile applications, privacy leakage has been a longstanding threat, for which many researchers have come up with a great number of methods that achieve the protective effect to a certain extent. However, the protection scope of these methods is limited, especially in the protection of original data. To address this issue, we propose a data perturbation based Rényi differential privacy algorithm to protect the SVD recommendation model. This paper uses the data perturbation method to perturb the original training dataset in the data preprocessing stage, then leverages the perturbed data to train the SVD model, and the unperturbed data is used as a test set to verify the accuracy of the model. Compared with the objective perturbation, gradient perturbation, and output perturbation, the data perturbation can protect a broader range and realize the corresponding functions of the other three perturbed methods by using the post-processing property of differential privacy. Experimental results show that the proposed method can effectively protect user privacy, improve the effectiveness of data, and generate better recommendation results without seriously affecting the accuracy of the model.
9
Tu, Zhiwen, Yawen Yin, and Xianan Qin. "Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220006.
Анотація:
Intelligent manufacturing for the fabric dyeing industry requires high-performance dyeing recipe recommendation systems. Nowadays, recommending dyeing recipes by mining dyeing manufacturing data has become a new direction for the development of recipe recommendation systems. As one of the indispensable parts in the system development, data pre-processing needs more than routine steps such as the removal of missing data and outliers. Considering that dyes can have very different coloration properties on different fabrics, dyeing manufacturing records for a given dye combination to different fabric types should be properly categorized before they are used for training regression models for dyeing recipe prediction. In this paper, we propose a simple but effective method for this categorization work. Our method uses conventional K-means clustering analysis to find fabric types that have similar coloration properties for a given dye combination. We have applied the method on a dye combination formed by Colvaceton reactive dye-navy blue CF (CRD-navy blue), Colvaceton reactive dye-bright red 3BSN150% (CRD-red) and Colvaceton reactive dye-yellow 3RS150% (CRD-yellow) on 28 different types of fabrics. We show that these 28 types of fabrics can be well categorized into 8 groups based on the coloration properties. Our proposed method can be listed as one of the standard data pre-processing steps in the development of data-mining based recipe recommendation systems.
10
Kari, Tuomas, Miia Siutila, and Veli-Matti Karhulahti. "An Extended Study on Training and Physical Exercise in Esports." In Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations, 270–92. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7461-3.ch010.
Анотація:
This chapter is an extended revision of the authors' earlier study (2016) on the training routines of professional and high-level esport players, with added focus on their physical exercise. The study is methodologically mixed with a quantitative survey sample (n=115) and a qualitative interview sample (n=7). Based on this data, high-level esport players train approximately 5.28 hours every day around the year, and professional esport players at least the same amount. Approximately 1.08 hours of that training is physical exercise. More than half (55.6%) of the professional and high-level esport players believe that integrating physical exercise into their training programs has a positive effect on esport performance; however, no less than 47.0% do the physical exercise chiefly to maintain their overall state of health. Accordingly, the study indicates that professional and high-level esport players are physically active as well: those of age 18 and older exercising more than three times the daily 21-minute physical activity recommendation given by the World Health Organization.
Тези доповідей конференцій з теми "Training data recommendation":
1
Srivastava, Rajiv, Girish Keshav Palshikar, and Saheb Chourasia. "What's Next? A Recommendation System for Industrial Training." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.35.
2
Yu, Junliang, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. "Socially-Aware Self-Supervised Tri-Training for Recommendation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467340.
3
Yu, Junliang, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, and Qinyong Wang. "Generating Reliable Friends via Adversarial Training to Improve Social Recommendation." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00087.
4
Li, Xiangkun, and Fenghao Sun. "Sports Training Recommendation Method under the Background of Data Analysis." In 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE, 2021. http://dx.doi.org/10.1109/hpbdis53214.2021.9658481.
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Huang, Yuzhen, Xiaohan Wei, Xing Wang, Jiyan Yang, Bor-Yiing Su, Shivam Bharuka, Dhruv Choudhary, Zewei Jiang, Hai Zheng, and Jack Langman. "Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467084.
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Lo, Kachun, and Tsukasa Ishigaki. "Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00057.
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Yuan, Fajie, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. "Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380116.
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Ding, Jingtao, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. "Reinforced Negative Sampling for Recommendation with Exposure Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/309.
Анотація:
In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we improve the negative sampler by integrating the exposure data. We propose to generate high-quality negative instances by adversarial training to favour the difficult instances, and by optimizing additional objective to favour the real negatives in exposure data. However, this idea is non-trivial to implement since the distribution of exposure data is latent and the item space is discrete. To this end, we design a novel RNS method (short for Reinforced Negative Sampler) that generates exposure-alike negative instances through feature matching technique instead of directly choosing from exposure data. Optimized under the reinforcement learning framework, RNS is able to integrate user preference signals in exposure data and hard negatives. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our RNS method. Our implementation is available at: https://github. com/dingjingtao/ReinforceNS.
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Pang, Guangyao, Xiaoying Zhu, Keda Lu, Zizhen Peng, and Weitao Deng. "A simulator for reinforcement learning training in the recommendation field." In 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2020. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00156.
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Liu, Zhiyong, and Junping Zhang. "Research on Key Algorithms of Intelligent Recommendation System for Retired soldiers’ Employment Training." In BDSIC 2021: 2021 3rd International Conference on Big-data Service and Intelligent Computation. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3502300.3502308.
Звіти організацій з теми "Training data recommendation":
1
Gehlhaus, Diana, Luke Koslosky, Kayla Goode, and Claire Perkins. U.S. AI Workforce: Policy Recommendations. Center for Security and Emerging Technology, October 2021. http://dx.doi.org/10.51593/20200087.
Анотація:
This policy brief addresses the need for a clearly defined artificial intelligence education and workforce policy by providing recommendations designed to grow, sustain, and diversify the U.S. AI workforce. The authors employ a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals. Their recommendations are designed to leverage opportunities within the U.S. education and training system while mitigating its challenges, and prioritize equity in access and opportunity to AI education and AI careers.
2
Guest, Arlene A., Peter S. Guest, Paul A. Frederickson, and Tom Murphree. Evaluation of JSAF EM Propagation Prediction Methods for Navy Continuous Training Environment/Fleet Synthetic Training, Results and Recommendations: Part 3 - An Overview of JSAF's Environmental Capabilities and Data. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada570940.
3
Haddock, John E., Reyhaneh Rahbar-Rastegar, M. Reza Pouranian, Miguel Montoya, and Harsh Patel. Implementing the Superpave 5 Asphalt Mixture Design Method in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317127.
Анотація:
Recent research developments have indicated that asphalt mixture durability and pavement life can be increased by modifying the Superpave asphalt mixture design method to achieve an in-place density of 95%, approximately 2% higher than the density requirements of conventionally designed Superpave mixtures. Doing so requires increasing the design air voids content to 5% and making changes to the mixture aggregate gradation so that effective binder content is not lowered. After successful laboratory testing of this modified mixture design method, known as Superpave 5, two controlled field trials and one full scale demonstration project, the Indiana Department of Transportation (INDOT) let 12 trial projects across the six INDOT districts based on the design method. The Purdue University research team was tasked with observing the implementation of the Superpave 5 mixture design method, documenting the construction and completing an in-depth analysis of the quality control and quality assurance (QC/QA) data obtained from the projects. QC and QA data for each construction project were examined using various statistical metrics to determine construction performance with respect to INDOT Superpave 5 specifications. The data indicate that, on average, the contractors achieved 5% laboratory air voids, which coincides with the Superpave 5 recommendation of 5%. However, on average, the as-constructed mat density of 93.8% is roughly 1% less than the INDOT Superpave 5 specification. It is recommended that INDOT monitor performance of the Superpave 5 mixtures and implement some type of additional training for contractor personnel, in order to help them increase their understanding of Superpave 5 concepts and how best to implement the design method in their operation.
4
Korobeinikova, Tetiana I., Nataliia P. Volkova, Svitlana P. Kozhushko, Daryna O. Holub, Nataliia V. Zinukova, Tetyana L. Kozhushkina, and Sergei B. Vakarchuk. Google cloud services as a way to enhance learning and teaching at university. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3854.
Анотація:
The article is devoted to the issue of a cloud-based learning system implementation as a powerful strategy for future specialists’ training at higher educational establishments. Using cloud computing in self-work management of the university courses is essential to equip students with a workload of appropriate educational materials and variable activities for professional training. Theoretical and empirical research methods were applied to select the appropriate services and tools for organizing students’ self-work at university. Critical analysis of scientific literature, synthesis of the data, didactic observation of the educational process, designing of the skeleton for university courses, questionnaires enabled to facilitate the study of the issue. G Suite has been chosen to enhance the quality of training of prospective specialists at a higher educational establishment. This paper introduces the outcomes of the project on applying Google Classroom in the management of students’ self-work while studying university courses. The focus of the first stage of the project was on testing pilot versions of the courses with the aim to work out the requirements and recommendations for incorporation general blended learning model of university courses. Particular attention is drawn to the designed model of the university course based on the curriculum with the necessary components of blended learning in the G Suite virtual environment. Cloud-based higher education is considered as a prospective tool for design of university courses with the need for further research and implementation.
5
Braun, Lindsay, Jesus Barajas, Bumsoo Lee, Rebecca Martin, Rafsun Mashraky, Shubhangi Rathor, and Manika Shrivastava. Construction of Pedestrian Infrastructure along Transit Corridors. Illinois Center for Transportation, March 2021. http://dx.doi.org/10.36501/0197-9191/21-004.
Анотація:
The availability and quality of pedestrian infrastructure play key roles in enabling access to transit. Many transit operators face challenges in facilitating this access, however, because they lack land use authority and encounter other institutional and programmatic impediments to effecting changes in the pedestrian environment. This report identifies the barriers to pedestrian access to transit in suburban communities located in the Pace Suburban Bus service area in northeastern Illinois and suggests potential solutions to overcome these barriers. The research team led several activities to collect data, including: conducting an academic literature review; reviewing pedestrian plans, policies, and programs in the region; surveying and interviewing key stakeholders; reviewing pedestrian funding sources; surveying and conducting case studies of peer transit agencies; conducting physical audits of pedestrian infrastructure; and interviewing residents of six municipalities about their transit access experiences. Lack of adequate funding, difficulties planning across jurisdictional boundaries, and conflicts in transportation priorities are major impediments to building pedestrian infrastructure. While planners and decision-makers tend to value pedestrian planning, challenges such as funding constraints and the need to retrofit suburban infrastructure are key barriers to implementation. Peer transit agencies face similar barriers to Pace and use strategies such as plan and policy development, diverse funding opportunities, and collaborative partnerships with stakeholder agencies and advocacy groups to overcome these barriers. Transit riders generally reported positive experiences with pedestrian access to transit in their communities. Many locations had robust infrastructure, but common deficiencies included poor sidewalk connectivity, incomplete crossings, lack of lighting and transit shelters, and deficiencies in Americans with Disabilities Act (ADA) infrastructure. A suite of policy recommendations for Pace and other partners that focus on planning, policy, funding, interagency coordination, education and training, infrastructure prioritization, and transit amenities address the full range of physical and institutional barriers identified in the research.
6
Perera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.
Анотація:
Floods are major water-related disasters that affect millions of people resulting in thousands of mortalities and billiondollar losses globally every year. Flood Early Warning Systems (FEWS) - one of the floods risk management measures - are currently operational in many countries. The UN Office for Disaster Risk Reduction recognises their importance and strongly advocates for an increase in their availability under the targets of the Sendai Framework for Disaster Risk Reduction, and Sustainable Development Goals (SDGs). However, despite widespread recognition of the importance of FEWS for disaster risk reduction (DRR), there’s a lack of information on their availability and status around the world, their benefits and costs, challenges and trends associated with their development. This report contributes to bridging these gaps by analyzing the responses to a comprehensive online survey with over 80 questions on various components of FEWS (risk knowledge, monitoring and forecasting, warning dissemination and communication, and response capabilities), investments into FEWS, their operational effectiveness, benefits, and challenges. FEWS were classified as technologically “basic”, “intermediate” and “advanced” depending on the existence and sophistication of FEWS` components such as hydrological data = collection systems, data transfer systems, flood forecasting methods, and early warning communication methods. The survey questionnaire was distributed to flood forecasting and warning centers around the globe; the primary focus was developing and least-developed countries (LDCs). The questionnaire is available here: https://inweh.unu.edu/questionnaireevaluation-of-flood-early-warning-systems/ and can be useful in its own right for similar studies at national or regional scales, in its current form or with case-specific modifications. Survey responses were received from 47 developing (including LDCs) and six developed countries. Additional information for some countries was extracted from available literature. Analysis of these data suggests the existence of an equal number of “intermediate” and “advanced” FEWS in surveyed river basins. While developing countries overall appear to progress well in FEWS implementation, LDCs are still lagging behind since most of them have “basic” FEWS. The difference between types of operational systems in developing and developed countries appear to be insignificant; presence of basic, intermediate or advanced FEWS depends on available investments for system developments and continuous financing for their operations, and there is evidence of more financial support — on the order of USD 100 million — to FEWS in developing countries thanks to international aid. However, training the staff and maintaining the FEWS for long-term operations are challenging. About 75% of responses indicate that river basins have inadequate hydrological network coverage and back-up equipment. Almost half of the responders indicated that their models are not advanced and accurate enough to produce reliable forecasts. Lack of technical expertise and limited skilled manpower to perform forecasts was cited by 50% of respondents. The primary reason for establishing FEWS, based on the survey, is to avoid property damage; minimizing causalities and agricultural losses appear to be secondary reasons. The range of the community benefited by FEWS varies, but 55% of FEWS operate in the range between 100,000 to 1 million of population. The number of flood disasters and their causalities has declined since the year 2000, while 50% of currently operating FEWS were established over the same period. This decline may be attributed to the combined DRR efforts, of which FEWS are an integral part. In lower-middle-income and low-income countries, economic losses due to flood disasters may be smaller in absolute terms, but they represent a higher percentage of such countries’ GDP. In high-income countries, higher flood-related losses accounted for a small percentage of their GDP. To improve global knowledge on FEWS status and implementation in the context of Sendai Framework and SDGs, the report’s recommendations include: i) coordinate global investments in FEWS development and standardise investment reporting; ii) establish an international hub to monitor the status of FEWS in collaboration with the national responsible agencies. This will support the sharing of FEWS-related information for accelerated global progress in DRR; iii) develop a comprehensive, index-based ranking system for FEWS according to their effectiveness in flood disaster mitigation. This will provide clear standards and a roadmap for improving FEWS’ effectiveness, and iv) improve coordination between institutions responsible for flood forecasting and those responsible for communicating warnings and community preparedness and awareness.
7
McKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, June 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
Анотація:
Emergency Relief (ER) is a Department of Social Services (DSS) funded program, delivered by 197 community organisations (ER Providers) across Australia, to assist people facing a financial crisis with financial/material aid and referrals to other support programs. ER has been playing this important role in Australian communities since 1979. Without ER, more people living in Australia who experience a financial crisis might face further harm such as crippling debt or homelessness. The Emergency Relief National Coordination Group (NCG) was established in April 2020 at the start of the COVID-19 pandemic to advise the Minister for Families and Social Services on the implementation of ER. To inform its advice to the Minister, the NCG partnered with the Institute for Governance at the University of Canberra to conduct research to understand the issues and challenges faced by ER Providers and Service Users in local contexts across Australia. The research involved a desktop review of the existing literature on ER service provision, a large survey which all Commonwealth ER Providers were invited to participate in (and 122 responses were received), interviews with a purposive sample of 18 ER Providers, and the development of a program logic and theory of change for the Commonwealth ER program to assess progress. The surveys and interviews focussed on ER Provider perceptions of the strengths, weaknesses, future challenges, and areas of improvement for current ER provision. The trend of increasing case complexity, the effectiveness of ER service delivery models in achieving outcomes for Service Users, and the significance of volunteering in the sector were investigated. Separately, an evaluation of the performance of the NCG was conducted and a summary of the evaluation is provided as an appendix to this report. Several themes emerged from the review of the existing literature such as service delivery shortcomings in dealing with case complexity, the effectiveness of case management, and repeat requests for service. Interviews with ER workers and Service Users found that an uplift in workforce capability was required to deal with increasing case complexity, leading to recommendations for more training and service standards. Several service evaluations found that ER delivered with case management led to high Service User satisfaction, played an integral role in transforming the lives of people with complex needs, and lowered repeat requests for service. A large longitudinal quantitative study revealed that more time spent with participants substantially decreased the number of repeat requests for service; and, given that repeat requests for service can be an indicator of entrenched poverty, not accessing further services is likely to suggest improvement. The interviews identified the main strengths of ER to be the rapid response and flexible use of funds to stabilise crisis situations and connect people to other supports through strong local networks. Service Users trusted the system because of these strengths, and ER was often an access point to holistic support. There were three main weaknesses identified. First, funding contracts were too short and did not cover the full costs of the program—in particular, case management for complex cases. Second, many Service Users were dependent on ER which was inconsistent with the definition and intent of the program. Third, there was inconsistency in the level of service received by Service Users in different geographic locations. These weaknesses can be improved upon with a joined-up approach featuring co-design and collaborative governance, leading to the successful commissioning of social services. The survey confirmed that volunteers were significant for ER, making up 92% of all workers and 51% of all hours worked in respondent ER programs. Of the 122 respondents, volunteers amounted to 554 full-time equivalents, a contribution valued at $39.4 million. In total there were 8,316 volunteers working in the 122 respondent ER programs. The sector can support and upskill these volunteers (and employees in addition) by developing scalable training solutions such as online training modules, updating ER service standards, and engaging in collaborative learning arrangements where large and small ER Providers share resources. More engagement with peak bodies such as Volunteering Australia might also assist the sector to improve the focus on volunteer engagement. Integrated services achieve better outcomes for complex ER cases—97% of survey respondents either agreed or strongly agreed this was the case. The research identified the dimensions of service integration most relevant to ER Providers to be case management, referrals, the breadth of services offered internally, co-location with interrelated service providers, an established network of support, workforce capability, and Service User engagement. Providers can individually focus on increasing the level of service integration for their ER program to improve their ability to deal with complex cases, which are clearly on the rise. At the system level, a more joined-up approach can also improve service integration across Australia. The key dimensions of this finding are discussed next in more detail. Case management is key for achieving Service User outcomes for complex cases—89% of survey respondents either agreed or strongly agreed this was the case. Interviewees most frequently said they would provide more case management if they could change their service model. Case management allows for more time spent with the Service User, follow up with referral partners, and a higher level of expertise in service delivery to support complex cases. Of course, it is a costly model and not currently funded for all Service Users through ER. Where case management is not available as part of ER, it might be available through a related service that is part of a network of support. Where possible, ER Providers should facilitate access to case management for Service Users who would benefit. At a system level, ER models with a greater component of case management could be implemented as test cases. Referral systems are also key for achieving Service User outcomes, which is reflected in the ER Program Logic presented on page 31. The survey and interview data show that referrals within an integrated service (internal) or in a service hub (co-located) are most effective. Where this is not possible, warm referrals within a trusted network of support are more effective than cold referrals leading to higher take-up and beneficial Service User outcomes. However, cold referrals are most common, pointing to a weakness in ER referral systems. This is because ER Providers do not operate or co-locate with interrelated services in many cases, nor do they have the case management capacity to provide warm referrals in many other cases. For mental illness support, which interviewees identified as one of the most difficult issues to deal with, ER Providers offer an integrated service only 23% of the time, warm referrals 34% of the time, and cold referrals 43% of the time. A focus on referral systems at the individual ER Provider level, and system level through a joined-up approach, might lead to better outcomes for Service Users. The program logic and theory of change for ER have been documented with input from the research findings and included in Section 4.3 on page 31. These show that ER helps people facing a financial crisis to meet their immediate needs, avoid further harm, and access a path to recovery. The research demonstrates that ER is fundamental to supporting vulnerable people in Australia and should therefore continue to be funded by government.
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Gendered effects of COVID-19 school closures: Pakistan case study. Population Council, 2022. http://dx.doi.org/10.31899/sbsr2022.1002.
Анотація:
This brief summarizes a case study conducted to assess the gendered impacts of COVID-19 school closures on adolescent girls and boys in three districts in the province of Punjab in Pakistan. Data as well as discussions and interviews with adolescents, teachers, and parents shed light on difficulties in accessing and adjusting to remote learning, learning loss, deterioration of behaviors and health, and other effects. Based on these findings and further reflections by stakeholders on the successes and gaps of mitigation measures, the case study proposes recommendations for improved teacher training, digital access, alternative learning options, and a gendered focus in interventions.