Academic literature on the topic 'Scarce knowledge'
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Journal articles on the topic "Scarce knowledge"
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Feng, Lingyun, Minghui Qiu, Yaliang Li, Hai-Tao Zheng, and Ying Shen. "Learning to Augment for Data-scarce Domain BERT Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7422–30. http://dx.doi.org/10.1609/aaai.v35i8.16910.
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Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.
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Buhr, Russell G., Ruby Romero, and Lauren E. Wisk. "Promotion of Knowledge and Trust Surrounding Scarce Resource Allocation Policies." JAMA Health Forum 5, no. 10 (October 18, 2024): e243509. http://dx.doi.org/10.1001/jamahealthforum.2024.3509.
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ImportanceThe COVID-19 pandemic prompted rapid development of scarce resource allocation policies (SRAPs) in case demand for critical health services eclipsed capacity.ObjectiveTo test whether a brief educational video could improve knowledge of how the University of California Health’s SRAP would be implemented and trust in health systems to implement such policies in accordance with ethical principles during the pandemic.Design, Setting, and ParticipantsThis randomized clinical trial used an educational video intervention embedded in a longitudinal web-based survey and was conducted between May and December 2020 and analyzed during March 2024. A total of 1971 adult participants were enrolled, of whom 939 completed follow-up; participants with matched baseline and follow-up responses were analyzed. California residents were randomized to view the intervention (n = 345) or not (n = 353) and stratified by age, sex, education, racial identity, and self-reported health care worker status. Non-California residents were allocated to the control group (n = 241).InterventionsA brief (6-minute) “explainer” video that provided an overview of mechanics and ethical principles underpinning the University of California Health SRAP, subtitled in 6 languages.Main Outcomes and MeasuresSelf-reported survey assessment of knowledge of components of SRAP, graded as correct vs incorrect, and trust graded on a 10-point Likert scale. Anxiety about such policies was graded on a 10-point Likert scale with an a priori noninferiority margin of half of a standard deviation. Participants answered items at baseline and follow-up (approximately 10 weeks after baseline), with randomization occurring between administrations.ResultsOf 770 randomized participants with responses at both points, 566 (73.5%) were female, and the median (IQR) age was 43.5 (36-57) years. Intervention participants demonstrated improvement of 5.6 (95% CI, 4.8-6.4; P < .001) more correct knowledge items of 20 vs controls, as well as significant improvements in reported trust in fairness/consistency and honesty/transparency about SRAP implementation. There was no significant change in reported anxiety surrounding SRAP in either treatment or control groups.Conclusions and RelevanceThe trial found that a brief educational video is sufficient to explain complex ethical tenets and mechanics of SRAP and improved knowledge of such policies and trust in health systems to implement them equitably while not exacerbating anxiety about potential policy implications. This informs practice by providing a framework for educating people about the use of these policies during future situations necessitating crisis standards of care.Trial RegistrationClinicalTrials.gov Identifier: NCT04373135
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Zhou, Jie, Weixin Zeng, Hao Xu, and Xiang Zhao. "Active Temporal Knowledge Graph Alignment." International Journal on Semantic Web and Information Systems 19, no. 1 (February 16, 2023): 1–17. http://dx.doi.org/10.4018/ijswis.318339.
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Entity alignment aims to identify equivalent entity pairs from different knowledge graphs (KGs). Recently, aligning temporal knowledge graphs (TKGs) that contain time information has aroused increasingly more interest, as the time dimension is widely used in real-life applications. The matching between TKGs requires seed entity pairs, which are lacking in practice. Hence, it is of great significance to study TKG alignment under scarce supervision. In this work, the authors formally formulate the problem of TKG alignment with limited labeled data and propose to solve it under the active learning framework. As the core of active learning is to devise query strategies to select the most informative instances to label, the authors propose to make full use of time information and put forward novel time-aware strategies to meet the requirement of weakly supervised temporal entity alignment. Extensive experimental results on multiple real-world datasets show that it is important to study TKG alignment with scarce supervision, and the proposed time-aware strategy is effective.
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Baird, Theodore. "Knowledge of practice: A multi-sited event ethnography of border security fairs in Europe and North America." Security Dialogue 48, no. 3 (March 27, 2017): 187–205. http://dx.doi.org/10.1177/0967010617691656.
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This article takes the reader inside four border security fairs in Europe and North America to examine the knowledge practices of border security professionals. Building on the border security as practice research agenda, the analysis focuses on the production, circulation, and consumption of scarce forms of knowledge. To explore situated knowledge of border security practices, I develop an approach to multi-sited event ethnography to observe and interpret knowledge that may be hard to access at the security fairs. The analysis focuses on mechanisms for disseminating and distributing scarce forms of knowledge, technological materializations of situated knowledge, expressions of transversal knowledge of security problems, how masculinities structure knowledge in gendered ways, and how unease is expressed through imagined futures in order to anticipate emergent solutions to proposed security problems. The article concludes by reflecting on the contradictions at play at fairs and how to address such contradictions through alternative knowledges and practices.
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Tune, Kula Kekeba, and Vasudeva Varma. "Building CLIA for Resource-Scarce African Languages." International Journal of Information Retrieval Research 5, no. 1 (January 2015): 48–67. http://dx.doi.org/10.4018/ijirr.2015010104.
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Since most of the existing major search engines and commercial Information Retrieval (IR) systems are primarily designed for well-resourced European and Asian languages, they have paid little attention to the development of Cross-Language Information Access (CLIA) technologies for resource-scarce African languages. This paper presents the authors' experience in building CLIA for indigenous African languages, with a special focus on the development and evaluation of Oromo-English-CLIR. The authors have adopted a knowledge-based query translation approach to design and implement their initial Oromo-English CLIR (OMEN-CLIR). Apart from designing and building the first OMEN-CLIR from scratch, another major contribution of this study is assessing the performance of the proposed retrieval system at one of the well-recognized international Cross-Language Evaluation Forums like the CLEF campaign. The overall performance of OMEN-CLIR was found to be very promising and encouraging, given the limited amount of linguistic resources available for severely under-resourced African languages like Afaan Oromo.
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Sheng, Yang, Jiahan Zhang, Chunhao Wang, Fang-Fang Yin, Q. Jackie Wu, and Yaorong Ge. "Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381987478. http://dx.doi.org/10.1177/1533033819874788.
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Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively ( P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction ( P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario ( P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.
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Machado, Andreia, Araci Hack, and Maria José Sousa. "Globalization: Intersection Between Communication, Innovation and Knowledge." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 4, no. 4 (2019): 22–27. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.44.3003.
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Advances in technological possibilities have made communication present in different media and spaces. By enabling interaction between different countries, by becoming a facilitator between knowledge and innovation in the globalized world, it has opened frontiers by providing innovations in various sectors of the knowledge society. In this sense, the objective in this article is to map the intersection of communication, innovation and knowledge in the globalized world. To that end, the methodology used in the research was the systematic search of literature that pointed out that the intersection is motivated by the use of innovative technologies in the process of knowledge sharing, and studies are still scarce in this area. It is possible to perceive, further, that this intersection is branched out, through Social Sciences, Business, Management and Accounting, Computer Science, Medicine, Engineering, Decision Sciences, Nursing, Arts and Humanities, Economics, Econometrics and Finance, Psychology, aligned Health Professions, Agricultural and Biological Sciences, Biochemistry, Genetics and Molecular Biology, Energy, Environmental Science, Mathematics, Materials Science, Multidisciplinary, Neuroscience, Pharmacology, Toxicology and Pharmaceutical and Veterinary.
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Rodríguez-Baiget, María José, Alexander Maz Machado, José Carlos Casas del Rosal, and Arnaldo Vergara-Romero. "The scarce representation of women university professors in research groups." International Journal of Evaluation and Research in Education (IJERE) 13, no. 3 (June 1, 2024): 1384. http://dx.doi.org/10.11591/ijere.v13i3.27291.
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Women university teachers in Spain simultaneously have teaching and research careers that interrelate to develop their competencies in both fields. However, as in other fields, there is not yet gender equality in representation and leadership. This paper presents a descriptive analysis of the presence and role of female university teachers-researchers in the different research groups of public universities in the Autonomous Community of Andalusia that apply for competitive calls for projects, according to the different fields of knowledge to which they belong. A total of 2,445 research groups in nine universities were identified. The largest number of groups belonging to the Humanities field. Among the members of all the groups, there is a lower presence of women compared to men. There is also a negative gender gap in the roles of responsibility and management of research groups, which is reflected in both horizontal and vertical segregation and the existence of a glass ceiling for Andalusian female teachers.
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Serafini, M. "Rise and falls of dietary antioxidants for disease prevention: Magic bullets, false myth or scarce knowledge?" European Journal of Pharmacology 668 (September 2011): e5. http://dx.doi.org/10.1016/j.ejphar.2011.09.203.
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Merriman, Juanitas, Pete Keohane, and Emma Hodges. "A scarce resource: Psychiatrists’ perceptions of referring over 75s for psychological therapy." FPOP Bulletin: Psychology of Older People 1, no. 144 (October 2018): 64–69. http://dx.doi.org/10.53841/bpsfpop.2018.1.144.64.
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This study sought to understand why referrals for psychological therapies reduce with age within an older person’s mental health service. Individual interviews took place with six psychiatrists and a thematic analysis was conducted. Findings suggest that being over 75 signalled the presence of other factors such as; suitability of alternative interventions, cohort characteristics and doubts over therapeutic efficacy. Referral behaviour did not seem to be driven by prejudice but was influenced by the referrers’ knowledge and bias, service availability and age related complexities. This highlights the risk of inadvertently disadvantaging older people and suggests more psychology provisions are required to meet their needs.
Dissertations / Theses on the topic "Scarce knowledge"
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Zola, Nazo. "Organisational learning through scarce skills transfer : a case study in the Eastern Cape Province." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86560.
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Thesis (MPhil)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Knowledge Transfer is one of the key knowledge management practices that organisations employ to ensure cross-pollination of knowledge across their various divisions. It should be a cornerstone of a learning organisation and should pervade the entire organisation in all its manifestations. In general it is a question whether public sector organisations in South African are employing such practices in their quest to render services effectively, efficiently and economically. This thesis focuses on an attempt at knowledge transfer in a department in an underdeveloped province, i.e. the Department of Roads and Public Works in the Eastern Cape. It centres on a case study of Cuban engineers who were contracted by the South African government to design and build infrastructure. The thesis is divided into the following chapters: Chapter 1: deals with the problem of knowledge transfer in a developing context. The chapter focuses on the objectives of the research and sketches a contextual backdrop to the study. Chapter 2: discusses the key concepts of Learning, Organisational Learning, Knowledge, Knowledge Transfer, and Knowledge Transfer Strategies. It also identifies barriers to knowledge transfer and highlights a few suggestions on how to deal with those barriers. Chapter 3: deals with the case study of six Cuban engineers and presents the results of the case study. Chapter 4: describes some of the local initiatives taken by the Department to cater for the needed skills in their sector. Chapter 5: evaluates the topic by bringing the literature discussed in chapter two to bear on the findings of the case study.
AFRIKAANSE OPSOMMING: Kennisoordrag is een van die kern kennisbestuurspraktyke waardeur organisasies kruisbestuiwing van kennis oor ‘n verskeidenheid onderafdelings moontlik maak. Dit behoort die basis van ‘n ‘learning organisation’ te wees en die hele organisasie te deursuur. In die algemeen is dit ‘n vraag of publieke sektor organisasies in Suid-Afrika sodanige praktyke aanwend in hulle pogings om dienste te lewer. Hierdie tesis fokus op ‘n poging tot kennisoordrag in ‘n departement wat in ‘n onderontwikkelde provinsie in Suid-Afrika geleë is, naamlik die departement Paaie en Openbare Werke in die Oos-Kaap. Die tesis draai om ‘n gevallestudie van Kubaanse ingenieurs wat deur die Suid-Afrikaanse regering gekontrakteer was om infrastruktuur te ontwerp en te bou. Die tesis is verdeel in die volgende hoofstukke: HOOFSTUK 1 handel oor die probleem van kennisoordrag binne ‘n ontwikkelingskonteks. Dit sit die doel van die studie uiteen en beskryf die sosiale konteks daarvan. HOOFSTUK 2 bespreek die kernkonsepte, naamlik Leer, Organisatorise Leer, Kennis, Kennisoordrag en Kennisoordragstrategieë. Dit identifiseer ook faktore wat kennisoordrag teenwerk en bespreek moontlike oplossings vir laasgenoemde probleem. HOOFSTUK 3 behels ‘n gevallestudie van 6 Kubaanse ingenieurs en bied die resultate daarvan aan. HOOFSTUK 4 beskryf sommige lokale inisiatiewe deur die Department om kennisoordrag te bevorder. HOOFSTUK 5 evalueer die onderwerp deur die literatuur in hoofstuk 2 in verband te bring met die gevallestudie.
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Deng, Weikun. "Amélioration du diagnostic et du pronostic dans des conditions de données rares et de connaissances limitées par l'apprentissage automatique informé par la physique et auto-supervisé." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP107.
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Cette thèse aborde le défi des « données éparses et des connaissances rares » dans le développement d’un modèle générique de pronostic et de gestion de la santé (PHM). Elle met en lumière l'efficacité des modèles hybrides combinant la modélisation basée sur la physique (PBM) et l'apprentissage automatique (ML), notamment l'apprentissage automatique informé par la physique (PIML) et l'apprentissage auto-supervisé (SSL) pour apprendre à partir de données non étiquetées. La thèse apporte ainsi des contributions significatives aux théories PIML et SSL et à leurs applications pratiques dans le PHM.La première contribution est une solution générique d'architecture et de stratégie d'apprentissage pour le PIML. Diverses approches sont analysées et la théorie mimétique est proposée pour concevoir des neurones et connexions flexibles et physiquement cohérents, aboutissant au Réseau Neuronal Mimétique des Éléments Finis du Rotor (RFEMNN). Le RFEMNN reconnaît efficacement les défauts à travers diverses structures de rotor. Pour améliorer la capacité de diagnostic du RFEMNN avec peu de données, une stratégie d'apprentissage par renforcement alignée avec la physique est proposée. Une architecture générique PIML avec des branches PI et basées sur les données est développée, impliquant un processus en trois étapes : pré-formation de la branche basée sur les données, formation de la branche PI, et formation conjointe. Cette méthode assure des performances supérieures aux modèles basés sur les données dans un contexte de données éparses. De plus, le modèle CNN dilaté utilisant cette approche prédit efficacement la RUL des batteries lithium-ion avec des données de petits cycles. La deuxième contribution est une stratégie SSL pour l'apprentissage à partir de données non étiquetées, introduisant un modèle Siamese CNN-LSTM avec une fonction de perte contrastive personnalisée. Ce modèle extrait des représentations robustes en maximisant les différences dans les mêmes échantillons présentés dans des ordres séquentiels variés. Des tâches en aval sont proposées comme objectifs intermédiaires pour aligner les représentations avec les exigences en aval. Le modèle Siamese CNN-LSTM excelle à prédire la RUL sur le dataset PRONOSTIA et reste stable même avec une augmentation de la rareté des données d'apprentissage.La contribution finale étend les concepts de PIML pour la découverte active des connaissances sur des données non étiquetées et intègre le SSL dans la formation PIML en trois étapes. Une nouvelle structure PI liquide et un modèle PI-CNN-Selective state space model (CNN-SSM) sont développés. Liquid PI introduit des neurones à portes et des connexions liquides qui s'adaptent dynamiquement, acquérant des connaissances physiques grâce à une recherche optimisée. Appliquée dans le suivi du couple des manipulateurs robotisés, cette approche découvre des connaissances en utilisant des opérateurs physiques de base et des poids dynamiques. Le Liquid PI CNN-SSM traite des séquences d'entrée de longueur variable sans prétraitement du signal, optimisant les ressources en nécessitant seulement 600 KB pour gérer 23,9 GB de données. Il atteint des performances de pointe dans des tâches de pronostic mixtes, y compris la dégradation des roulements, l'usure des outils de coupe, le vieillissement des batteries et la fatigue des tubes CFRP. Les travaux futurs appliqueront des lois d'échelle spécifiques au PHM et utiliseront de vastes ensembles de données synthétiques et industrielles pour construire un macro-modèle. Ce modèle pourrait intégrer des capacités de diagnostic et de pronostic avec un traitement de séquence infinie, transformant les méthodologies et les solutions de PHMThis thesis addresses the critical challenge of “sparse data and scarce knowledge” in developing a generic Prognostics and Health Management (PHM) model. A comprehensive literature review highlights the efficacy of hybrid models combining physics-based modeling with machine learning, focusing on Physics-Informed Machine Learning (PIML) and Self-Supervised Learning (SSL) for enhanced learning from unlabeled data. Thereby, this thesis contributes to advancing both PIML and SSL theories and their practical applications in PHM.The first contribution is developing a generic architectural and learning strategy solution for PIML. Various informed approaches are analyzed, and the mimetic theory is proposed to design flexible, physically consistent neurons and interlayer connections. This novel approach leads to the development of the Rotor Finite Elements Mimetic Neural Network (RFEMNN), which mimics rotor finite element-based dynamics to adjust weight distribution and data flow within the neural network. RFEMNN effectively localizes and recognizes compound faults across multiple rotor structures and conditions. To enhance RFEMNN's few-shot diagnostic capability, constraint projection theory and a reinforcement learning strategy are proposed, aligning the learning process with physics. A generic PIML architecture with parallel, independent PI and data-driven branches is proposed, involving a three-stage training process: pre-training the data-driven branch, freezing it to train the PI branch, and joint training of both branches. This method combines optimized local branches into a comprehensive global model, ensuring the PIML model's performance exceeds original data-driven models under spare data context. Moreover, the solid electrolyte interphase growth-informed Dilated CNN model using this approach showcases its superiority, surpassing leading models in predicting lithium-ion battery RUL with small-cycle data.The second contribution is developing an innovative SSL strategy for unlabeled data learning, introducing a Siamese CNN-LSTM model with a custom contrastive loss function. This model extracts robust feature representations by maximizing differences in the same samples presented in varied sequential orders. Variants of downstream tasks are proposed as intermediate objectives in SSL pretext learning, integrating downstream structures into the pre-training model to align representations with downstream requirements. Under this strategy, the proposed Siamese CNN-LSTM excels at predicting RUL on PRONOSTIA-bearing dataset and remains stable even as training data sparsity increases.The final contribution extends PIML concepts for active knowledge discovery on unlabeled data and integrates SSL into the second phase of PIML's three-step training, utilizing both labeled and unlabeled data. A novel Liquid PI structure and an end-to-end Liquid PI-CNN-Selective state space model (CNN-SSM) are developed. The Liquid PI design introduces gated neurons and liquid interlayer connections that adapt dynamically, acquiring physics knowledge through an optimized search within a predefined operator pool. Demonstrated in torque monitoring of robot manipulators, this approach efficiently discovers knowledge using basic physical operators and dynamic weights from unlabeled data. The Liquid PI CNN-SSM processes variable-length input sequences without signal preprocessing, optimizing resources by requiring only 600 KB to handle 23.9 GB of data. It achieves state-of-the-art performance in mixed prognostic tasks, including bearing degradation, tool wear, battery aging, and CFRP tube fatigue, showcasing the originality and versatility of the proposed approach.Future work will apply PHM-specific scaling laws and train on extensive synthetic and industry datasets to build a cross-modal macro-model. It could integrate diagnostic-prognostic capabilities with infinite sequence length processing, continuing to transform PHM methodologies and solutions
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Gallie, Karen Ann. "Development of a knowledge about aging scale." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25395.
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The purpose of this study was to develop a reliable and valid knowledge about aging scale. Two hundred and ninety-eight subjects (128 males, 170 females) from the University of British Columbia, Simon Fraser University, and members of the general population, ranging from 17 to 65 years of age, and having 0 to 12 years of post secondary education, participated in this study. Subjects were chosen on the basis of having gerontological, versus no gerontological training.
Subjects responded to computer randomized Likert scale questionnaires consisting of the initial 60 item Proto Knowledge About Aging Scale, Palmore's Facts on Aging Quiz (FAQ), and Kogan's Old People Scale (OP).
Responses to the initial Proto scale were used to construct a psychometrically appropriate 40 item scale that consisted of three factor dimensions interpreted as Psychological, Biological Change, and Social Lifestyle/Histological Change. This 40 item scale had a Chronbach's alpha of 0.839 and a construct validity value of 0.701. Analysis of Covariance results indicated that the independent variables of age, gender, and years of post secondary education, had no significant extraneous confounding influence (p≤ 0.05) on Proto scale results. However, type of training did influence Proto scale results, with those subjects having gerontological training scoring significantly higher (Duncan's Multiple Range Test p≤ 0.05) than those with no gerontological training.
Investigation into Proto's scale characteristics were further analyzed in relation to the subjects in this investigation, Palmore's FAQ, and Kogan's OP scale, with discussion focussing on Proto's psychometric rigor as compared to Palmore's FAQ.Education, Faculty of
Educational Studies (EDST), Department of
Graduate
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Noori, Sheak Rashed Haider. "A Large Scale Distributed Knowledge Organization System." Doctoral thesis, Università degli studi di Trento, 2011. https://hdl.handle.net/11572/368691.
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The revolution of Internet and the Web takes the computer and information technology into a new age. The information on the web is growing very fast. The progress of information and communication technologies has made accessible a large amount of information, which have provided each of us with access to far more information than we can comprehend or manage. This emphasizes the difficulty with the resulting semantic heterogeneity of the diverse sources. Human knowledge is a living organism and as such evolves in time where different people having different viewpoints and using different terminology among people of different cultures and languages, intensify the heterogeneity of the sources even more. These introduce some concrete problems like natural language disambiguation, information retrieval and information integration. Nevertheless, the problem is quite well known in almost every branch of knowledge and has been independently approached by several communities for several decades. To make this huge amount of existing information accessible and manageable while also solving the semantic heterogeneity problem, namely the problem of diversity in knowledge, and therefore support interoperability, it is essential to have a large scale high quality collaborative knowledge base along with a suitable structure as a common ground on which interoperability among people and different systems should be possible. It will play the role of a reference point for communication, assigning clear meaning by accurate disambiguation to exchanged information, communication and automating complex tasks. However, successfully building large scale knowledge bases with maximum coverage is not possible by a single person or a small group of people without collaborative support. It extremely depends on expert community based support. Therefore, it is necessary for experts to work together on knowledge base building. Furthermore, it is very natural that these expert users will be geographically distributed. Web 2.0 has the potential to support information sharing, interoperability and collaboration on the Web. Simplicity, flexibility and easy to use services make it an interactive and collaborative platform which allows them to create or edit their content. The exponential expansion of the Web users and the potentials of Web 2.0 make it the natural platform of choice for developing knowledge bases collaboratively. We propose a highly flexible knowledge base system, which takes into account diversity of knowledge and its evolution in time. The work presented in this thesis is part of a larger project. More specifically the goal of this thesis is to create a powerful and easy to use knowledge base management system to help people in building, organizing a high quality knowledge base and making accessible their knowledge and to support interoperability in real world scenarios.
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Noori, Sheak Rashed Haider. "A Large Scale Distributed Knowledge Organization System." Doctoral thesis, University of Trento, 2011. http://eprints-phd.biblio.unitn.it/569/1/PhD_Thesis_Noori.pdf.
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The revolution of Internet and the Web takes the computer and information technology into a new age. The information on the web is growing very fast. The progress of information and communication technologies has made accessible a large amount of information, which have provided each of us with access to far more information than we can comprehend or manage. This emphasizes the difficulty with the resulting semantic heterogeneity of the diverse sources. Human knowledge is a living organism and as such evolves in time where different people having different viewpoints and using different terminology among people of different cultures and languages, intensify the heterogeneity of the sources even more. These introduce some concrete problems like natural language disambiguation, information retrieval and information integration. Nevertheless, the problem is quite well known in almost every branch of knowledge and has been independently approached by several communities for several decades. To make this huge amount of existing information accessible and manageable while also solving the semantic heterogeneity problem, namely the problem of diversity in knowledge, and therefore support interoperability, it is essential to have a large scale high quality collaborative knowledge base along with a suitable structure as a common ground on which interoperability among people and different systems should be possible. It will play the role of a reference point for communication, assigning clear meaning by accurate disambiguation to exchanged information, communication and automating complex tasks. However, successfully building large scale knowledge bases with maximum coverage is not possible by a single person or a small group of people without collaborative support. It extremely depends on expert community based support. Therefore, it is necessary for experts to work together on knowledge base building. Furthermore, it is very natural that these expert users will be geographically distributed. Web 2.0 has the potential to support information sharing, interoperability and collaboration on the Web. Simplicity, flexibility and easy to use services make it an interactive and collaborative platform which allows them to create or edit their content. The exponential expansion of the Web users and the potentials of Web 2.0 make it the natural platform of choice for developing knowledge bases collaboratively. We propose a highly flexible knowledge base system, which takes into account diversity of knowledge and its evolution in time. The work presented in this thesis is part of a larger project. More specifically the goal of this thesis is to create a powerful and easy to use knowledge base management system to help people in building, organizing a high quality knowledge base and making accessible their knowledge and to support interoperability in real world scenarios.
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Shoop, Jessica A. "SENIOR INFORMATION TECHNOLOGY (IT) LEADER CREDIBILITY: KNOWLEDGE SCALE, MEDIATING KNOWLEDGE MECHANISMS, AND EFFECTIVENESS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491489274525242.
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Andersson, Martin. "Studies of Knowledge, Location and Growth." Licentiate thesis, Jönköping University, JIBS, Economics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-986.
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Johnson, Michelle E., and Amy Malkus. "Design and Validation of a Nutrition Knowledge Scale for Preschoolers." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/4584.
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Yi, Jialin. "A measure of knowledge sharing behavior scale development and validation /." [Bloomington, Ind.] : Indiana University, 2005. http://wwwlib.umi.com/dissertations/fullcit/3204302.
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Thesis (Ph.D.)--Indiana University, Dept. of Instructional Systems Technology, School of Education, 2005.Source: Dissertation Abstracts International, Volume: 67-01, Section: A, page: 0067. Adviser: Thomas Schwen. "Title from dissertation home page (viewed Jan. 22, 2007)."
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Zhang, Xi. "Knowledge discovery from large-scale biological networks and their relationships." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/23353.
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The ultimate aim of postgenomic biomedical research is to understand mechanisms of cellular systems in a systematical way. It is therefore necessary to examine various biomolecular networks and to investigate how the interactions between biomolecules determine biological functions within cellular systems. Rapid advancement in high-throughput techniques provides us with increasing amounts of large-scale datasets that could be transformed into biomolecular networks. Analyzing and integrating these biomolecular networks have become major challenges. I approached these challenges by developing novel methods to extract new knowledge from various types of biomolecular networks.
Protein-protein interactions and domain-domain interactions are extremely important in a wide range of biological functions. However, the interaction data are incomplete and inaccurate due to experimental limitations. Therefore, I developed a novel algorithm to predict interactions between membrane proteins in yeast based on the protein interaction network and the domain interaction network. In addition, I also developed a novel algorithm, a gram-based interaction analysis tool (GAIA), to identify interacting domains by integrating the protein primary sequences, the domain annotations and interactions and the structural annotations of proteins. Biological assessment against several metrics indicated that both algorithms were capable of satisfactory performance, facilitating the elucidation of cell interactome. Predicting biological pathways is one of major challenges in systems biology. I proposed a novel integrated approach, called Pandora, which used network topology to predict biological pathways by integrating four types of biological evidence (protein-protein interactions, genetic interactions, domain-domain interactions, and semantic similarity of GO terms). I demonstrated that Pandora achieved better performance compared to other predictive approaches, allowing the reconstruction of biological pathways and the delineation of cellular machinery in a systematic view. Finally, I focused on investigating biological network perturbations in diseases. I developed a novel algorithm to capture highly disturbed sub-networks in the human interactome as the signatures linked to cancer outcomes. This method was applied to breast cancer and yielded improved predictive performance, providing the possibility to predict the outcome of cancers based on “network-based gene signatures”. These methods and tools contributed to the analysis and understanding of a wide variety of biological networks and the relationships between them.
Books on the topic "Scarce knowledge"
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Holden, Tony. Knowledge based CAD and microelectronics. Amsterdam: North-Holland, 1987.
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Hameurlain, Abdelkader. Transactions on Large-Scale Data- and Knowledge-Centered Systems VII. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
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de Sá Caetano, Elsa. Cable Vibrations in Cable-Stayed Bridges. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2007. http://dx.doi.org/10.2749/sed009.
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<p>The fifty years of experience of construction of cable-stayed bridges since their establishment as a new category among the classical types have brought an immense progress, ranging from design and conception to materials, analysis, construction, observation and retrofitting. The growing construction of cable-stayed bridges has also triggered researchers’ and designers’ attention to the problem of cable vibrations. Intensive research has been developed all over the world during the last two decades as a consequence of the numerous cases of cable vibrations exhibited by all types of cable-stayed bridges.<p>Despite the increased knowledge of the various vibration phenomena, most of the outcomes and research results have been published in journals and conference proceedings and scarce information is currently provided by the existing recommendations and codes. <p>The present book provides a comprehensive survey on the governing phenomena of cable vibration, both associated with direct action of wind and rain: buffeting, vortex-shedding, wake effects, rain-wind vibration; and resulting from the indirect excitation through anchorage oscillation: external and parametric excitation. Methodologies for assessment of the effects of those phenomena are presented and illustrated by practical examples. Control of cable vibrations is then discussed and state-of-art results on the design of passive control devices are presented. <p>The book is complemented with a series of case reports reflecting the practical approach shared by experienced designers and consultants: Yves Bournand (VSL International), Chris Geurts (TNO), Carl Hansvold (Johs. Holt), Allan Larsen (Cowi) and Randall Poston (WDP & Associates).
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Bulian, Giovanni, and Yasushi Nakano. Small-scale Fisheries in Japan. Venice: Edizioni Ca' Foscari, 2018. http://dx.doi.org/10.30687/978-88-6969-226-0.
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This collection of essays brings together a range of critical approaches, from varying disciplinary backgrounds, to provide an in-depth overview of the past and current status of small-scale fisheries in Japan. The book attempts to map out some of the major themes relating to community-based fisheries-management systems, environmental sustainability, lottery systems for allocating fishing spots, fishing livelihoods, local knowledge, social vulnerability to environmental hazards, socioeconomic factors affecting small-scale fisheries development, history of destructive fishing practices, women’s entrepreneurship in the seafood sector, traditional leadership systems, religious festivals, and power relationship between local communities and government agencies. The aim of this book is then to provide a comprehensive and multifaceted analysis of the cultural richness of this fishing sector, which still plays a key role in the broad academic debates focused on the potential small-scale fishery trajectories within the context of global scenarios.
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Burris, Scott, Micah L. Berman, Matthew Penn, and, and Tara Ramanathan Holiday. Using Evidence and Knowledge Critically in Policy Development. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190681050.003.0007.
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This chapter starts with the recognition that policymaking usually precedes evidence of what laws are effective in solving the problem at hand. That does not mean that policymaking cannot be guided by evidence. Policymakers can usually draw on extensive evidence defining the problem and evidence of analogous policy cases. This chapter identifies sources of policy recommendations and direct evidence of policy impact, including systematic reviews, narrative reviews, models, cost-benefit analysis, and individual studies. It reviews strategies for identifying bias and source credibility and tools for “educated guessing” about policy options in matters where evidence is scarce or incomplete, including causal mapping and the Haddon matrix. Finally, it introduces the Health in All Policies approach and the use of health impact assessments as tools to consider broader impact and cross-sectoral cooperation.
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Smiraglia, Richard P., and Andrea Scharnhorst, eds. Linking Knowledge. Ergon – ein Verlag in der Nomos Verlagsgesellschaft, 2021. http://dx.doi.org/10.5771/9783956506611.
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The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.
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Wagner, Roland, Abdelkader Hameurlain, and Josef Küng. Transactions on Large-Scale Data- and Knowledge-Centered Systems IX. Springer London, Limited, 2013.
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Festing, Marion, Katharina Harsch, Lynn Schäfer, and Hugh Scullion. Talent Management in Small- and Medium-Sized Enterprises. Edited by David G. Collings, Kamel Mellahi, and Wayne F. Cascio. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198758273.013.13.
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Despite the economic importance of small- and medium-sized enterprises (SMEs), talent management in this context is under-researched. The liability of smallness and scarce resources as typical features of SMEs require a specific definition and approach to talent management in this sector. The limited knowledge about talent management in SMEs indicates major challenges in attracting and retaining talent. We draw on the literature on human resource management (HRM) in SMEs, to put talent-management issues in a wider context. Furthermore, we outline HRM and talent-management networks and cooperation in industry clusters as a means for SMEs to join forces to compete with larger, multinational companies. However, this chapter also indicates that more research is needed in the field of talent management in SMEs.
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Penrose, Jago. The Theory of the Growth of the Firm. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198753940.003.0011.
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This chapter comprises a description of Edith’s influential book, The Theory of the Growth of the Firm, how it came to be written, and its main arguments. It is often called revolutionary. She showed that the human resources required for the management of change are tied to the individual firm and so are internally scarce. As management tries to make the best use of the resources available, a ‘dynamic’ interacting process occurs which encourages growth but limits the rate of growth. The book was an important step towards modern, liberally minded management concepts, developing the resource-based and knowledge-based perspective, and ultimately including the theory of stakeholding, in which the interests of employees, customers, and the community count alongside those of shareholder owners.
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Wagner, Roland, Abdelkader Hameurlain, and Josef Küng. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXV. Springer London, Limited, 2016.
Find full textBook chapters on the topic "Scarce knowledge"
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van Duyne, Petrus C., Jackie H. Harvey, and Liliya Y. Gelemerova. "Money-laundering: a global issue and scarce knowledge." In The Critical Handbook of Money Laundering, 1–11. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-52398-3_1.
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Ghasemi, Negin. "Knowledge Transfer from Resource-Rich to Resource-Scarce Environments." In Lecture Notes in Computer Science, 341–44. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56069-9_44.
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Ng, Yen Kaow, and Takeshi Shinohara. "Finding Consensus Patterns in Very Scarce Biosequence Samples from Their Minimal Multiple Generalizations." In Advances in Knowledge Discovery and Data Mining, 540–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_63.
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Mellaoui, Wahiba, Richard Posso, Yodit Gebrealif, Erik Bock, Jörn Altmann, and Hyenyoung Yoon. "Knowledge Management Framework for Cloud Federation." In Economics of Grids, Clouds, Systems, and Services, 123–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92916-9_10.
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AbstractA cloud federation (CF) is an alliance of cloud service providers (CSPs) working together to overcome scalability and portability barriers. However, there are some business challenges (e.g., lack of trust, lack of schemes for revenue sharing, and lack of schemes for resource sharing) and technological challenges (e.g., missing schemes for resource provisioning, lack of coordinated resource management, and little load balancing), causing instability in CFs. As CF alliances pursue strategic goals, they require intensive knowledge sharing. In fact, practitioners have confirmed a positive impact of knowledge management on stability and success of strategic alliances (SA). According to the literature, SAs may also face learning issues such as non–controlled information revelation or unbalanced dissemination of core competencies. These findings pose challenges about the nature of the knowledge and how to share it within a CF. Nonetheless, there is only scarce literature on KM in CF. Thus, the purpose of the paper is to propose a KM framework for CFs with the aim of strengthening stability and potential CF commercialization.
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Lepik, Katri-Liis, and Audronė Urmanavičienė. "The Role of Higher Education Institutions in Development of Social Entrepreneurship: The Case of Tallinn University Social Entrepreneurship Study Program, Estonia." In Innovation, Technology, and Knowledge Management, 129–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84044-0_7.
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AbstractThe purpose of this chapter is to introduce a higher education social enterprise program and explore how it is shaping the field of social entrepreneurship. Social enterprise related university programs are an emerging trend. Entrepreneurial university theory and ecosystem framework are used to illustrate how the university social enterprise program, in turn, develops the field of social entrepreneurship. An example of an existing social enterprise program is discussed to highlight how it can be designed. Cases of social enterprises emerged as the result of the program are used to outline the different impacts that such support to social entrepreneurship might have. The research chapter reveals the multi-dimensional nature of the social enterprise program and its impact on students establishing their own social enterprises. It suggests that the incubation and other support activities should expand beyond the university program including a variety of network partners. The chapter provides empirical evidence of social enterprise development in a higher education institution and contributes to the global body of knowledge about fostering social enterprise development. As the provision of social entrepreneurship education is new in Estonia and the discussions on social enterprises are premature, the number of social entrepreneurship development partners is limited and hence the empirical data is currently scarce. The journey towards an entrepreneurial university is limited due to the lack of legal support and suitable infrastructure which would enhance project-based learning, support ‘spin-offs’ and patenting and rather engenders a more traditional academic learning environment.
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Jian, Yiren, Chongyang Gao, Chen Zeng, Yunjie Zhao, and Soroush Vosoughi. "Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task." In Lecture Notes in Computer Science, 407–23. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78192-6_27.
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Smite, Darja, and Nils Brede Moe. "The Role of Responsiveness to Change in Large Onboarding Campaigns." In Lecture Notes in Business Information Processing, 132–48. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33976-9_9.
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AbstractOnboarding is a process of organizational socialization of the new hires, that includes recruitment, orientation, training, coaching and support. While onboarding individuals into an organization is a rather straightforward task, little is known about 1) onboarding hundreds of developers and 2) doing it on a distance in outsourcing situations. Furthermore, the subject of sustainable growth with respect to organizational capabilities and culture is often overlooked. This paper reports findings from an exploratory multi-case study of two large onboarding campaigns. We collected empirical data from interviews, retrospectives, onboarding documentation and onsite visits. Based on the empirical study, onboarding hundreds of software engineers in a complex agile product development environment which lacks documentation and puts high demands on engineers’ knowledge and skills is a challenging and costly endeavor. To save the costs and for practical reasons, large-scale onboarding is organized in batches with the first batch trained onsite, and the later batches trained internally. We report challenges faced in the two cases and discuss possible solutions. One core finding is that a good plan combined with the organizational agility, i.e., the responsiveness to change, together with organizational maturity determined the success of organizational scaling. The presented cases contribute to the scarce research on knowledge transfer and onboarding in a large-scale agile context.
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Huisman, Mike, Jan N. van Rijn, and Aske Plaat. "Metalearning for Deep Neural Networks." In Metalearning, 237–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_13.
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AbstractDeep neural networks have enabled large breakthroughs in various domains ranging from image and speech recognition to automated medical diagnosis. However, these networks are notorious for requiring large amounts of data to learn from, limiting their applicability in domains where data is scarce. Through metalearning, the networks can learn how to learn, allowing them to learn from fewer data. In this chapter, we provide a detailed overview of metalearning for knowledge transfer in deep neural networks. We categorize the techniques into (i) metric-based, (ii) model-based, and (iii) optimization-based techniques, cover the key techniques per category, discuss open challenges, and provide directions for future research such as performance evaluation on heterogeneous benchmarks.
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Stütz, Sebastian, Andreas Gade, and Daniela Kirsch. "Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 91–102. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_8.
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AbstractCritical success factors for the efficient use of electric trucks are the operational range and the total costs of ownership. For both range and efficient use, power consumption is the key factor. Increasing precision in forecasting power consumption and, hence, maximum range will pave the way for efficient vehicle deployment. However, not only electric trucks are scarce, but also is knowledge with respect to what these vehicles are actually technically capable of. Therefore, this article focuses on power consumption and range of electric vehicles. Following a discussion on how current research handles the mileage of electric vehicles, the article illustrates how to find simple yet robust and precise models to predict power consumption and range by using basic parameters from transport planning only. In the paper, we argue that the precision of range and consumption estimates can be substantially improved compared to common approaches which usually posit a proportional relationship between energy consumption and travel distance and require substantial safety buffers.
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Charalambidou, Georgia, Stella Antoniou, Gregory Papagregoriou, Maria Kyratzi, Apostolos Malatras, Charalambos Stefanou, Mariel Voutounou, and Constantinos Deltas. "Health Inequalities and Availability: Needs and Applications." In Sustainable Development Goals Series, 69–76. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62332-5_6.
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AbstractThe limited access to high quality healthcare in Low- and Middle-Income Countries (LMICs) creates disparities and challenges. In such nations, health outcomes are inevitably influenced by the scarce geographic distribution of health providers and the often unbearable cost of quality services. Regardless of improvements in global life expectancy and mortality rates due to scientific and medical breakthroughs in the modern world, LMICs do not experience similar progress. To bridge the healthcare gap, a coordinated global effort to transfer medical knowledge to developing countries through the digitalization of medicine, in the form of adopting and implementing electronic health records (EHRs) or telemedicine is imperative. This chapter initially explores how the concepts of healthcare inequality and inequity are exerted and provides examples of how medical digitalization is implemented in LMICs. International and national responses to health inequalities that are impacting digitalization efforts and the role of human rights towards achieving the effective and widespread provision of high-quality healthcare services are also addressed.
Conference papers on the topic "Scarce knowledge"
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Lu, Wei, Fu-lai Chung, and Kunfeng Lai. "Scarce Feature Topic Mining for Video Recommendation." In CIKM'16: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2983323.2983892.
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Zeng, Dan, Shanchuan Hong, Shuiwang Li, Qiaomu Shen, and Bo Tang. "Data-Scarce Animal Face Alignment via Bi-Directional Cross-Species Knowledge Transfer." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3612558.
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Hao, Qianyue, Fengli Xu, Lin Chen, Pan Hui, and Yong Li. "Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467181.
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Choosri, N., Hongnian Yu, and A. S. Atkins. "Using constraint programming for split delivery scheduling in scarce resource environment." In 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA 2011). IEEE, 2011. http://dx.doi.org/10.1109/skima.2011.6089984.
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Ding, Ruiqing, Fangjie Rong, Xiao Han, and Leye Wang. "Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals." In WWW '23: The ACM Web Conference 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3543507.3583989.
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Cao, Lele, Sonja Horn, Vilhelm von Ehrenheim, Richard Anselmo Stahl, and Henrik Landgren. "Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557110.
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Li, Pan, Yanwei Fu, and Shaogang Gong. "Regularising Knowledge Transfer by Meta Functional Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/370.
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Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.
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Zhang, Jing, Deqing Zhang, Mingyue Yang, Xiaobin Xu, Weifeng Liu, and Chenglin Wen. "Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples: A Deep CNN Method Based on Knowledge-Transferring from Shallow Models." In 2018 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2018. http://dx.doi.org/10.1109/iccais.2018.8570515.
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Shi, Yuan. "Using Domain Knowledge for Low Resource Named Entity Recognition." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120625.
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In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.
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Zhang, Yu, Hua Lu, Ning Liu, Yonghui Xu, Qingzhong Li, and Lizhen Cui. "Personalized Federated Learning for Cross-City Traffic Prediction." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/611.
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Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.
Reports on the topic "Scarce knowledge"
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Roelen, Keetie, Sukanta Paul, Neil Howard, and Vibhor Mathur. Children’s Engagement with Exploitative Work in Dhaka, Bangladesh. Institute of Development Studies, November 2020. http://dx.doi.org/10.19088/clarissa.2020.001.
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Despite decades of interventions aiming to reduce child labour, children’s engagement with exploitative work remains widespread, particularly in South Asia. Emerging evidence about cash transfer programmes point towards their potential for reducing children’s engagement with work, but knowledge is scarce in terms of their impact on exploitative work and in urban settings. One component of the CLARISSA programme is to trial an innovative ‘cash plus’ intervention and to learn about its potential for reducing children’s harmful and hazardous work in two slum areas in Dhaka, Bangladesh. This Working Paper presents findings from a small-scale qualitative study that was undertaken in late 2019, aiming to inform the design of the cash plus intervention. Findings point towards the potential for cash transfers to reduce the need for children to engage in exploitative work and highlight key considerations for design and delivery, including mode and frequency of delivery and engagement with local leaders and community representatives. URI
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Rosas-Shady, David, Laura Ripani, and Carolina González-Velosa. How Can Job Opportunities for Young People in Latin America be Improved? Inter-American Development Bank, February 2012. http://dx.doi.org/10.18235/0010435.
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Job training programs for vulnerable youth are the main response of Latin American governments to address the problem of inadequate employment opportunities for young people. Despite its importance, knowledge about these programs is scarce. This study contributes to filling this gap in the literature by presenting new evidence on the effectiveness of six of these programs operating or that were implemented in Colombia, Honduras, Mexico, Panama, Peru and Dominican Republic. This analysis uses the results of impact evaluations of these programs and the results of qualitative surveys of young participants and employers, and in-depth interviews to training centers, employers and policy makers. The main results confirm the limited evidence available, namely, that these programs have little impact on the probability of getting a job (although there is a high heterogeneity in these impacts), but a significant impact on job quality. From this analysis, we propose a research agenda to improve knowledge on the functioning and impact of these programs, and provide a series of recommendations to improve the design and increase the effectiveness of youth training programs.
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Russell, Nathaniel, and Jose Claudio Linhares Pires. Assessing Firm-Support Programs in Brazil. Inter-American Development Bank, December 2017. http://dx.doi.org/10.18235/0010693.
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Increasing productivity is generally considered to be the only sustainable way of improving living standards in the long term. The Brazilian economy has had periods of strong growth, particularly until 2010, but the country has performed poorly in terms of aggregate productivity. The federal government has implemented many programs aimed at boosting firm growth and fostering competitiveness in Brazilian industries, though knowledge about their results to date is scarce. This study provides an overview of various Brazilian programs of firm support — including productive finance, business consulting, value chain, export promotion, and innovation support — as well as an assessment of the effects of a subset of these programs on productivity, employment, and real wages. Access to a unique dataset on Brazilian firms and beneficiaries allowed the Office of Evaluation and Oversight to analyze these programs over an 11-year period, 2002 to 2012.
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THOTO, Fréjus, Alban MAS APARISI, and Rodrigue Castro GBEDOMON. Evidence-informed policymaking in Benin’s agriculture, food security and nutrition ecosystem. ACED, September 2024. http://dx.doi.org/10.61647/aa63047.
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In Benin, more than 40% of the workforce depends on the agricultural sector, which is vital for the economy and livelihoods. Food security and nutrition (FSN) policies are therefore crucial for driving the country’s development. The study highlights the diversity of evidence types within this ecosystem, with relatively dynamic research production, but data that is often scarce and of poor quality. These weaknesses make research less usable for decision-makers, who tend to favor information from trusted sources, such as citizen and expert knowledge, rather than research data. Intermediaries play a key role in transforming research data into accessible formats and building trusted relationships with decision-makers to promote better use of evidence. Moreover, institutional frameworks can establish formal mechanisms to enhance the production and use of reliable data in policy processes. The growing involvement of civil society organizations (CSOs) in the agricultural ecosystem has also led to increased accountability in policies, encouraging greater production and use of evidence to legitimize policy choices.
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.
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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.
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Eickhout, Bas, Hans van Meijl, Andrzej Tabeau, and Elke Stehfest. The Impact of Environmental and Climate Constraints on Global Food Supply. GTAP Working Paper, April 2008. http://dx.doi.org/10.21642/gtap.wp47.
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*Chapter 9 of the forthcoming book "Economic Analysis of Land Use in Global Climate Change Policy," edited by Thomas W. Hertel, Steven Rose, and Richard S.J. Tol The goal of this Chapter is to study the complex interaction between agriculture, economic growth and the environment, given future uncertainties. We combine economic concepts and biophysical constraints in one consistent modeling framework to be able to quantify and analyze the long-term socio-economic and environmental consequences of different scenarios. Here, we present the innovative methodology of coupling an economic and a biophysical model to combine state of the art knowledge from economic and biophysical sources. First, a comprehensive representation of the agricultural and land markets is required in the economic model. Therefore we included a land demand structure to reflect the degree of substitutability of types of land-use types and we included a land supply curve to include the process of land conversion and land abandonment. Secondly, the adapted economic model (LEITAP) is linked to the biophysical-based integrated assessment model IMAGE allowing to feed back spatially and temporarily varying land productivity to the economic framework. Thirdly, the land supply curves in the economic model are parameterized by using the heterogeneous information of land productivity from IMAGE. This link between an economic and biophysical model benefits from the strengths of both models. The economic model captures features of the global food market, including relations between world regions, whereas the bio-physical model adds geographical explicit information on crop growth within each world region. An illustrative baseline analyses shows the environmental consequences of the default baseline and a sensitivity analyses is performed with regard to the land supply curve. Results indicate that economic and environmental consequences are very dependent on whether a country is land scarce or land abundant.
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Wirth, Brian D. Bridging the PSI Knowledge Gap: A Multi-Scale Approach. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1167092.
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Karp, Peter D. Supporting Multiuser Access to Large-Scale Persistent Knowledge Bases. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada329281.
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Baldwin, C., and G. Abdulla. Efficient Data Management for Knowledge Discovery in Large-Scale Geospatial Imagery Collections. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/889968.
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Bethel, Wes. Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1421430.
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