Academic literature on the topic 'Big data training'
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Journal articles on the topic "Big data training"
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Минязова, Е. Р. ""Big Data" and personalized training." Higher education today, no. 5-6 (July 18, 2022): 41–45. http://dx.doi.org/10.18137/rnu.het.22.05-06.p.041.
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Рассмотрена значимость анализа «больших данных» для современного образования, возможности работы с ними на примере функционирования образовательной платформы. Показаны перспективы применения технологий big data в персонализированном онлайн-обучении, а также риски применения анализа «больших данных». The article describes the importance of big data analysis for modern education. The possibilities of working with big data on the example of educational platform have been considered. Prospects of big data technologies in personalized online learning, as well as risks of big data analysis are considered.
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Scaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (August 2018): 569. http://dx.doi.org/10.1017/s174392131900543x.
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AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides training courses in machine learning, big data techniques and data intensive methodologies as part of the Big Data Africa initiative.
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Abdullateef Omitogun, Abdullateef Omitogun, and Khalid Al-Adeem Abdullateef Omitogun. "Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation." International Journal of Computer Auditing 1, no. 1 (December 2019): 092–113. http://dx.doi.org/10.53106/256299802019120101005.
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<p>This study presents evidence on practicing auditors’ perceptions of and competencies in applying big data and data analytics to audit engagements. An electronic questionnaire distributed to accountants shows that auditors have good information technology skills and are well-acquainted with big data and data analytics. However, they lack relevant technical skills and are unfamiliar with related data analysis tools, excluding Excel. The results reveal 64.71% of accountants have not attended any training on big data and data analytics, while 31.37% plan to enhance related knowledge. Auditors need to obtain training on substantive audit risk assessments using big data and data analytics.</p> <p> </p>
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Wang, Yiting, and Le Yu. "Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions." Mobile Information Systems 2022 (August 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/5115184.
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In the information age, “mobile Internet,” “cloud computing,” “Internet of Things,” and “data mining” concepts are emerging at the same time, as well as other fields of related data-based applications. The mobile application will be born as a result. Therefore, in the information age, big data, which involves information in a specific key or specialized field, has gradually begun to receive a lot of attention in recent years. In 2011, the US consulting firm McKinsey and Company first proposed the arrival of the “era of big data” and in August 2015 in China’s State Council issued a notice of action outline “to promote the development of big data.” Meanwhile, big data has gradually become an important factor in driving national reform and innovation, promoting scientific and technological progress, improving the way society is managed, and guiding changes in education and research. Big data is driving a very influential shift in thinking in an era where big data is changing the way we live, becoming the way we understand the world, and gradually becoming the source of new inventions and services. At the same time, the rapid development of big data technology for physical education teachers needs big data for management and training and other institutional managers to provide more effective ways and means of education management, but up to now, the status of big data for management is still another serious challenge, sports and training and other institutions of big data and processing process of data nonintelligent, nonclosed-loop processing, data nonlinked processing, etc. Many problems are also still very obvious. According to the new characteristics of sports big data refinement management, the current situation of sports professional training institutions teacher management, combined with sports training institutions to find some more practical sports training institutions teachers big data management methods can effectively improve the efficiency of management, teacher team building, strengthen sports training institutions to improve the quality of teaching teachers, and promote the overall quality of students have a positive impact. In this paper, we combine the characteristics of “big data” and the construction of teachers in sports training institutions, and put forward some suggestions on how to improve the level of teachers in sports training institutions in the era of big data and conclude that the construction of teachers in sports training institutions should seize the key era now and enter the “|big data era.” We conclude that the construction of teachers in sports training institutions should seize the critical era and enter the “big data era,” so as to rely on science and technology to improve the construction system of teachers in sports training institutions.
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Wang, Huiqin. "College Physical Education and Training in Big Data: A Big Data Mining and Analysis System." Journal of Healthcare Engineering 2021 (November 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/3585630.
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Recently, big data has been broadly used as a research method in all aspects of analysis, prediction, and evaluation. The application of big data to college students’ physical education plays a significant role in encouraging the completion of physical education at various levels. The application of the Internet and the advent of smartphones impact the way college students participate in physical exercise. At present, more and more students begin to participate in sports, and students’ demand for physical training is increasing. During physical education training, a lot of data is generated every moment because of various actions and behaviors. Due to technical limitations, these data were not effectively collected and applied. In this environment, the development and management of sports data mining systems have become more and more important. This paper designs an intelligent big data system for college physical education training. The study mainly focuses on data decentralization, lack of data talents, insufficient technical support, and low utilization of venues in physical education. While designing a big data system, the data is collected based on ease of data collection, and a response framework with excellent performance in storing analytical data is selected. The design and management of this system have a certain significance for the improvement and optimization of current college physical education training.
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Jinhui, Zheng, Wang Sheng, Zheng Jinhong, Cai Guoliang, Cai Zhiqiang, and Du Yuntao. "Analysis on Survey Data of Special Physical Training for Skiers in Summer Training Based on Big Data." Mobile Information Systems 2021 (December 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/3024089.
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Due to the geographical and natural conditions, the development of skiing events is more resistant in China, and the training venues, methods, and concepts are insufficient, making it difficult for Chinese skiers to make some progress and aspire to the highest peak in this field. The purpose of this study is to explore and analyze the survey data of the professional physical training of skiers in summer training based on big data. Big data is employed to investigate and analyze the special physical training of skiers in summer training. Based on the data of professional physical training of skiers in summer training under big data, the current situation of skiers in summer training is examined, and the limitations are compared to improve the traditional physical training of skiers. Results show that the special physical training of skiers based on big data is more feasible in summer training, and the improvement of training effect is more obvious than traditional physical training. The training effect of the proposed method can more effectively solve the difficulties in summer training for skiers and understand the essentials of the action.
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Serik, M., G. Nurbekova, and J. Kultan. "Big data technology in education." Bulletin of the Karaganda University. Pedagogy series 100, no. 4 (December 28, 2020): 8–15. http://dx.doi.org/10.31489/2020ped4/8-15.
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The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.
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Qu, Qingling, Meiling An, Jinqian Zhang, Ming Li, Kai Li, and Sukwon Kim. "Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data." Contrast Media & Molecular Imaging 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3725295.
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Thinking of big data as a collection of huge and sophisticated data sets, it is hard to process it effectively with current data management tools and processing methods. Big data is reflected in that the scale of data exceeds the scope of traditional volume measurement, and it is difficult to collect, store, manage, and analyze through traditional methods. Analyzing the biomechanics of table tennis training through big data is conducive to improving the training effect of table tennis, so as to formulate corresponding neuromuscular control training. This paper mainly analyzes various indicators in biomechanics and kinematics in table tennis training under big data. Under these metrics, an improved decision tree method was then used to analyze the differences between athletes trained for neuromuscular control and those who did not. It analyzed the effect of neuromuscular control training on the human body through different experimental control groups. Experiments showed that after nonathletes undergo neuromuscular control training, the standard rate of table tennis hitting action increases by 10% to 20%, reaching 80%. The improvement of athletes is not very obvious.
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Lane, Julia. "BIG DATA: THE ROLE OF EDUCATION AND TRAINING." Journal of Policy Analysis and Management 35, no. 3 (May 10, 2016): 722–24. http://dx.doi.org/10.1002/pam.21922.
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Wei, Jingwei. "Study and application of computer information big data in basketball vision system using high-definition camera motion data capture." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042003. http://dx.doi.org/10.1088/1742-6596/2083/4/042003.
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Abstract Contemporary computer big data technology is developing rapidly, and it has played a role in promoting the intelligent development of sports. Based on this research background, the thesis revolves around the application of computer big data artificial intelligence in the field of basketball training. The study found that computerized big data in basketball training and teaching is mainly reflected in the following aspects: high-definition camera motion data capture, player analysis to obtain assisted training system, etc. The final paper uses a basketball vision system as a case study to analyze specific applications of big data for computer information.
Dissertations / Theses on the topic "Big data training"
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Guo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.
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Big data is an increasingly attractive concept in many fields both in academia and in industry. The increasing amount of information actually builds an illusion that we are going to have enough data to solve all the data driven problems. Unfortunately it is not true, especially for areas where machine learning methods are heavily employed, since sufficient high-quality training data doesn't necessarily come with the big data, and it is not easy or sometimes impossible to collect sufficient training samples, which most computational algorithms depend on. This thesis mainly focuses on dealing situations with limited training data in visual object recognition, by developing novel machine learning algorithms to overcome the limited training data difficulty.
We investigate three issues in object recognition involving limited training data: 1. one-shot object recognition, 2. cross-domain object recognition, and 3. object recognition for images with different picture styles. For Issue 1, we propose an unsupervised feature learning algorithm by constructing a deep structure of the stacked Hierarchical Dirichlet Process (HDP) auto-encoder, in order to extract "semantic" information from unlabeled source images. For Issue 2, we propose a Domain Adaptive Input-Output Kernel Learning algorithm to reduce the domain shifts in both input and output spaces. For Issue 3, we introduce a new problem involving images with different picture styles, successfully formulate the relationship between pixel mapping functions with gradient based image descriptors, and also propose a multiple kernel based algorithm to learn an optimal combination of basis pixel mapping functions to improve the recognition accuracy. For all the proposed algorithms, experimental results on publicly available data sets demonstrate the performance improvements over previous state-of-arts.
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Hmida, Hmida. "Extension des Programmes Génétiques pour l’apprentissage supervisé à partir de très larges Bases de Données (Big data)." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED047.
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Dans cette thèse, nous étudions l'adaptation des Programmes Génétiques (GP) pour surmonter l'obstacle du volume de données dans les problèmes Big Data. GP est une méta‐heuristique qui a fait ses preuves pour les problèmes de classification. Néanmoins, son coût de calcul est un frein à son utilisation avec les larges bases d’apprentissage. Tout d'abord, nous effectuons une revue approfondie enrichie par une étude comparative expérimentale des algorithmes d'échantillonnage utilisés avec GP. Puis, à partir des résultats de l'étude précédente, nous proposons quelques extensions basées sur l'échantillonnage hiérarchique. Ce dernier combine des algorithmes d'échantillonnage actif à plusieurs niveaux et s’est prouvé une solution appropriée pour mettre à l’échelle certaines techniques comme TBS et pour appliquer GP à un problème Big Data (cas de la classification des bosons de Higgs). Par ailleurs, nous formulons une nouvelle approche d'échantillonnage appelée échantillonnage adaptatif, basée sur le contrôle de la fréquence d'échantillonnage en fonction du processus d'apprentissage, selon les schémas fixe, déterministe et adaptatif. Enfin, nous présentons comment transformer une implémentation GP existante (DEAP) en distribuant les évaluations sur un cluster Spark. Nous démontrons comment cette implémentation peut être exécutée sur des clusters à nombre de nœuds réduit grâce à l’échantillonnage. Les expériences montrent les grands avantages de l'utilisation de Spark pour la parallélisation de GPIn this thesis, we investigate the adaptation of GP to overcome the data Volume hurdle in Big Data problems. GP is a well-established meta-heuristic for classification problems but is impaired with its computing cost. First, we conduct an extensive review enriched with an experimental comparative study of training set sampling algorithms used for GP. Then, based on the previous study results, we propose some extensions based on hierarchical sampling. The latter combines active sampling algorithms on several levels and has proven to be an appropriate solution for sampling techniques that can’t deal with large datatsets (like TBS) and for applying GP to a Big Data problem as Higgs Boson classification.Moreover, we formulate a new sampling approach called “adaptive sampling”, based on controlling sampling frequency depending on learning process and through fixed, determinist and adaptive control schemes. Finally, we present how an existing GP implementation (DEAP) can be adapted by distributing evaluations on a Spark cluster. Then, we demonstrate how this implementation can be run on tiny clusters by sampling.Experiments show the great benefits of using Spark as parallelization technology for GP
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張金慶 and Kam-hing Cheung. "Quality training: an expert system application." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267038.
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Varga, Tamás. "Off-line cursive handwriting recognition using synthetic training data." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2838183&prov=M&dok_var=1&dok_ext=htm.
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Lyttkens, Peter. "Electromagnetic field and neurological disorders Alzheimer´s disease, why the problem is difficult and how to solve it." Thesis, Uppsala universitet, Logopedi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-380074.
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Grard, Matthieu. "Generic instance segmentation for object-oriented bin-picking." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC015.
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Le dévracage robotisé est une tâche industrielle en forte croissance visant à automatiser le déchargement par unité d’une pile d’instances d'objet en vrac pour faciliter des traitements ultérieurs tels que la formation de kits ou l’assemblage de composants. Cependant, le modèle explicite des objets est souvent indisponible dans de nombreux secteurs industriels, notamment alimentaire et automobile, et les instances d'objet peuvent présenter des variations intra-classe, par exemple en raison de déformations élastiques.Les techniques d’estimation de pose, qui nécessitent un modèle explicite et supposent des transformations rigides, ne sont donc pas applicables dans de tels contextes. L'approche alternative consiste à détecter des prises sans notion explicite d’objet, ce qui pénalise fortement le dévracage lorsque l’enchevêtrement des instances est important. Ces approches s’appuient aussi sur une reconstruction multi-vues de la scène, difficile par exemple avec des emballages alimentaires brillants ou transparents, ou réduisant de manière critique le temps de cycle restant dans le cadre d’applications à haute cadence.En collaboration avec Siléane, une entreprise française de robotique industrielle, l’objectif de ce travail est donc de développer une solution par apprentissage pour la localisation des instances les plus prenables d’un vrac à partir d’une seule image, en boucle ouverte, sans modèles d'objet explicites. Dans le contexte du dévracage industriel, notre contribution est double.Premièrement, nous proposons un nouveau réseau pleinement convolutionnel (FCN) pour délinéer les instances et inférer un ordre spatial à leurs frontières. En effet, les méthodes état de l'art pour cette tâche reposent sur deux flux indépendants, respectivement pour les frontières et les occultations, alors que les occultations sont souvent sources de frontières. Plus précisément, l'approche courante, qui consiste à isoler les instances dans des boîtes avant de détecter les frontières et les occultations, se montre inadaptée aux scénarios de dévracage dans la mesure où une région rectangulaire inclut souvent plusieurs instances. A contrario, notre architecture sans détection préalable de régions détecte finement les frontières entre instances, ainsi que le bord occultant correspondant, à partir d'une représentation unifiée de la scène.Deuxièmement, comme les FCNs nécessitent de grands ensembles d'apprentissage qui ne sont pas disponibles dans les applications de dévracage, nous proposons une procédure par simulation pour générer des images d'apprentissage à partir de moteurs physique et de rendu. Plus précisément, des vracs d'instances sont simulés et rendus avec les annotations correspondantes à partir d'ensembles d'images de texture et de maillages auxquels sont appliquées de multiples déformations aléatoires. Nous montrons que les données synthétiques proposées sont vraisemblables pour des applications réelles au sens où elles permettent l'apprentissage de représentations profondes transférables à des données réelles. A travers de nombreuses expériences sur une maquette réelle avec robot, notre réseau entraîné sur données synthétiques surpasse la méthode industrielle de référence, tout en obtenant des performances temps réel. L'approche proposée établit ainsi une nouvelle référence pour le dévracage orienté-objet sans modèle d'objet expliciteReferred to as robotic random bin-picking, a fast-expanding industrial task consists in robotizing the unloading of many object instances piled up in bulk, one at a time, for further processing such as kitting or part assembling. However, explicit object models are not always available in many bin-picking applications, especially in the food and automotive industries. Furthermore, object instances are often subject to intra-class variations, for example due to elastic deformations.Object pose estimation techniques, which require an explicit model and assume rigid transformations, are therefore not suitable in such contexts. The alternative approach, which consists in detecting grasps without an explicit notion of object, proves hardly efficient when the object geometry makes bulk instances prone to occlusion and entanglement. These approaches also typically rely on a multi-view scene reconstruction that may be unfeasible due to transparent and shiny textures, or that reduces critically the time frame for image processing in high-throughput robotic applications.In collaboration with Siléane, a French company in industrial robotics, we thus aim at developing a learning-based solution for localizing the most affordable instance of a pile from a single image, in open loop, without explicit object models. In the context of industrial bin-picking, our contribution is two-fold.First, we propose a novel fully convolutional network (FCN) for jointly delineating instances and inferring the spatial layout at their boundaries. Indeed, the state-of-the-art methods for such a task rely on two independent streams for boundaries and occlusions respectively, whereas occlusions often cause boundaries. Specifically, the mainstream approach, which consists in isolating instances in boxes before detecting boundaries and occlusions, fails in bin-picking scenarios as a rectangle region often includes several instances. By contrast, our box proposal-free architecture recovers fine instance boundaries, augmented with their occluding side, from a unified scene representation. As a result, the proposed network outperforms the two-stream baselines on synthetic data and public real-world datasets.Second, as FCNs require large training datasets that are not available in bin-picking applications, we propose a simulation-based pipeline for generating training images using physics and rendering engines. Specifically, piles of instances are simulated and rendered with their ground-truth annotations from sets of texture images and meshes to which multiple random deformations are applied. We show that the proposed synthetic data is plausible for real-world applications in the sense that it enables the learning of deep representations transferable to real data. Through extensive experiments on a real-world robotic setup, our synthetically trained network outperforms the industrial baseline while achieving real-time performances. The proposed approach thus establishes a new baseline for model-free object-oriented bin-picking
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Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.
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The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated.Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
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TSAI, YU-SHUN, and 蔡有順. "A Study on the Training Situation and Gap of Courses in Taiwan's Big Data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/52913800576229564520.
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碩士中華大學
科技管理學系
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In this era, the new technology applications has emerge in the advancement of technology. For example: Internet of Things(IOT), Autonomous Cars, Smart City, Artificial Intelligence(AI), Robot, Stem cells cultured in vitro, and Big Data. Application the new technologies was inevitable when the emerging technologies is growth, and the emerging technologies talents demand. However, the talents demand depends on the training programs. Big data courses as an example, many university and personnel training of institutions to established the big data courses, but this courses is diversified development. Therefore, this study is expected to compile a many of institutions to established the big data courses, and interviews with related experts, from the current understanding of the big data courses training situation and gap in Taiwan. This study collection of the 15 universities are courses planning of the big data courses, and big data information. In Taiwan, the big data courses to established the college of management, and set up the courses is data analysis and application. This courses set up in the department of statistics, mathematics, information management, and other fields. And the future the big data courses can be opened to more information processing courses in Taiwan. So that the amount of talented people is more valuable in Taiwan.
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(5929568), Tommy Y. Chang. "Reducing Wide-Area Satellite Data to Concise Sets for More Efficient Training and Testing of Land-Cover Classifiers." Thesis, 2019.
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Obtaining an accurate estimate of a land-cover classifier's performance over a wide geographic area is a challenging problem due to the need to generate the ground truth that covers the entire area that may be thousands of square kilometers in size. The current best approach constructs a testing dataset by drawing samples randomly from the entire area --- with a human supplying the true label for each such sample --- with the hope that the selections thus made statistically capture all of the data diversity in the area. A major shortcoming of this approach is that it is difficult for a human to ensure that the information provided by the next data element chosen by the random sampler is non-redundant with respect to the data already collected. In order to reduce the annotation burden, it makes sense to remove any redundancies from the entire dataset before presenting its samples to a human for annotation. This dissertation presents a framework that uses a combination of clustering and compression to create a concise-set representation of the land-cover data for a large geographic area. Whereas clustering is achieved by applying Locality Sensitive Hashing (LSH) to the data elements, compression is achieved through choosing a single data element to represent a given cluster. This framework reduces the annotation burden on the human and makes it more likely that the human would persevere during the annotation stage. We validate our framework experimentally by comparing it with the traditional random sampling approach using WorldView2 satellite imagery.
Books on the topic "Big data training"
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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
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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.
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Nimatulaev, Magomedhan. Information technology in professional activities. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1031122.
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The textbook is intended for studying of discipline "Information technologies in professional activity". Discusses key issues of forming of information society, basic notions and definitions of Informatization of various types and levels of professional activity, the analysis of information systems and technologies to solve economic and management problems.
Meets the requirements of Federal state educational standards of higher education of the last generation.
It is recommended that students enrolled in the bachelor in the direction of training "Management", as well as postgraduate and graduate students to update knowledge and skills in the application of information systems and technologies in the context of big data Analytics and managerial decision-making.
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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
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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.
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Masie, Elliott. Big Learning Data. American Society for Training & Development, 2013.
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Training Students to Extract Value from Big Data. Washington, D.C.: National Academies Press, 2014. http://dx.doi.org/10.17226/18981.
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Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
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Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
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Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
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Committee on Applied and Theoretical Statistics, National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Maureen Mellody. Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press, 2015.
Find full textBook chapters on the topic "Big data training"
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Schroth, Stephen T. "Education and Training." In Encyclopedia of Big Data, 430–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_81.
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Schroth, Stephen T. "Education and Training." In Encyclopedia of Big Data, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_81-1.
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Su, Man-Na, Zhi-Jian Fang, Shao-Zhen Ye, Ying-Jie Wu, and Yang-Geng Fu. "An Optimized Artificial Bee Colony Based Parameter Training Method for Belief Rule-Base." In Big Data, 77–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_5.
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Wani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. "Training Supervised Deep Learning Networks." In Studies in Big Data, 31–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6794-6_3.
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Kumar, Aditya, and Satish Narayana Srirama. "Fog Enabled Distributed Training Architecture for Federated Learning." In Big Data Analytics, 78–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_7.
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Qu, Zhaowei, Chunye Wu, Xiaoru Wang, and Yanjiao Zhao. "Identification of Sentiment Labels Based on Self-training." In Data Mining and Big Data, 404–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_38.
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Liu, Ruyi, Yi Zhang, Damon M. Chandler, Qiguang Miao, and Tiange Liu. "LaG-DESIQUE: A Local-and-Global Blind Image Quality Evaluator Without Training on Human Opinion Scores." In Big Data, 268–77. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_18.
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Jia, Xue-peng, and Xiao-feng Rong. "A Self-training Method for Detection of Phishing Websites." In Data Mining and Big Data, 414–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_39.
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Zhao, Jiaqi, Ting Bai, Yuting Wei, and Bin Wu. "PoetryBERT: Pre-training with Sememe Knowledge for Classical Chinese Poetry." In Data Mining and Big Data, 369–84. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8991-9_26.
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Wang, Youyun, Chuzhe Tang, and Xujia Yao. "A Distribution-Aware Training Scheme for Learned Indexes." In Web and Big Data, 143–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85899-5_11.
Full textConference papers on the topic "Big data training"
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Giobergia, Flavio, and Elena Baralis. "Fast Self-Organizing Maps Training." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006055.
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Wang, Fei, Guoyang Chen, Weifeng Zhang, and Tiark Rompf. "Parallel Training via Computation Graph Transformation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006180.
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Khan, Rituparna, and Michael Gubanov. "Towards Tabular Embeddings, Training the Relational Models." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377769.
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Hu, Ziqing, Yihao Fang, and Lizhen Lin. "Training Graph Neural Networks by Graphon Estimation." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671996.
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Van, Minh-Hao, Wei Du, Xintao Wu, Feng Chen, and Aidong Lu. "Defending Evasion Attacks via Adversarially Adaptive Training." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020474.
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Peng, Zhanglin, Jiamin Ren, Ruimao Zhang, Lingyun Wu, Xinjiang Wang, and Ping Luo. "Scheduling Large-scale Distributed Training via Reinforcement Learning." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622264.
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Shah, Ruchi, Shaoshuai Zhang, Ying Lin, and Panruo Wu. "xSVM: Scalable Distributed Kernel Support Vector Machine Training." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006315.
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Jeong, Jueun, Hanseok Jeong, and Han-Joon Kim. "An AutoEncoder-based Numerical Training Data Augmentation Technique." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020487.
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Khan, Rituparna, and Michael Gubanov. "WebLens: Towards Web-scale Data Integration, Training the Models." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377742.
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Tripathi, Samarth, Jiayi Liu, Sauptik Dhar, Unmesh Kurup, and Mohak Shah. "Improving Model Training by Periodic Sampling over Weight Distributions." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378212.
Full textReports on the topic "Big data training"
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Adebayo, Oliver, Joanna Aldoori, William Allum, Noel Aruparayil, Abdul Badran, Jasmine Winter Beatty, Sanchita Bhatia, et al. Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission. The Royal College of Surgeons of England, August 2022. http://dx.doi.org/10.1308/fos2.2022.
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Over the past 50 years the capability of technology to improve surgical care has been realised and while surgical trainees and trainers strive to deliver care and train; the technological ‘solutions’ market continues to expand. However, there remains no coordinated process to assess these technologies. The FOS:TEST Report aimed to (1) define the current, unmet needs in surgical training, (2) assess the current evidence-base of technologies that may be beneficial to training and map these onto both the patient and trainee pathway and (3) make recommendations on the development, assessment, and adoption of novel surgical technologies. The FOS:TEST Commission was formed by the Association of Surgeons in Training (ASiT), The Royal College of Surgeons of England (RCS England) Robotics and Digital Surgery Group and representatives from all trainee specialty associations. Two national datasets provided by Health Education England were used to identify unmet surgical training needs through qualitative analysis against pre-defined coding frameworks. These unmet needs were prioritised at two virtual consensus hackathons and mapped to the patient and trainee pathway and the capabilities in practice (CiPs) framework. The commission received more than 120 evidence submissions from surgeons in training, consultant surgeons and training leaders. Following peer review, 32 were selected that covered a range of innovations. Contributors also highlighted several important key considerations, including the changing pedagogy of surgical training, the ethics and challenges of big data and machine learning, sustainability, and health economics. This summates to 7 Key Recommendations and 51 concluding statements. The FOS:TEST Commission was borne out of what is a pivotal point in the digital transformation of surgical training. Academic expertise and collaboration will be required to evaluate efficacy of any novel training solution. However, this must be coupled with pragmatic assessments of feasibility and cost to ensure that any intervention is scalable for national implementation. Currently, there is no replacement for hands-on operating. However, for future UK and ROI surgeons to stay relevant in a global market, our training methods must adapt. The Future of Surgery: Technology Enhanced Surgical Training Report provides a blueprint for how this can be achieved.
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Guérin, Laurence, Patrick Sins, Lida Klaver, and Juliette Walma van der Molen. Onderzoeksrapport Samen werken aan Bèta Burgerschap. Saxion, 2021. http://dx.doi.org/10.14261/ff0c6282-93e2-41a7-b60ab9bceb2a4328.
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In het TechYourfuture project ‘Samen werken aan Bèta Burgerschap’, dat plaats vond in de periode maart 2015 - maart 2020, gaven de onderzoekers samen met scholen en bedrijven concreet invulling aan burgerschapsonderwijs. De maatschappij en maatschappelijke vraagstukken worden steeds complexer. Politieke, technologische, economische, sociaal-culturele of ecologische aspecten van een vraagstuk zijn met elkaar verweven. Daarnaast spelen ook globale en lokale dimensies een rol. Er zijn alleen hierdoor al meerdere antwoorden mogelijk op een vraagstuk. Gedurende het project hebben basisschoolleerlingen (wereldwijde) maatschappelijk-technologische vraagstukken geanalyseerd, bediscussieerd en daar oplossingen voor bedacht. Leraren hebben in het project geleerd bèta burgerschap activiteiten te ontwikkelen, uit te voeren en te evalueren. In de kern gaat het er in Bèta Burgerschap om dat leerlingen door groepsgewijs vraagstukken op te lossen burgerschapscompetenties ontwikkelen. Het gaat hier om drie hoofdcompetenties: (1.) Collectieve argumentatievaardigheden, (2.) Attituden ten opzichte van maatschappelijk technologische vraagstukken en, (3.) Bèta- en techniekkennis. In het onderzoek ‘Samen werken aan Bèta Burgerschap’ is gekeken naar de ontwikkeling van deze drie hoofdcompetenties bij leerlingen die deelnamen aan Bèta Burgerschap activiteiten, alsook naar de effecten van de training en video-coaching die de leerkrachten in het project gevolgd hebben. De resultaten hiervan zijn in het onderzoeksrapport te lezen. Het onderzoek laat zien dat Bèta Burgerschap een aanpak is die leerlingen mogelijkheden biedt om te oefenen met groepsgewijs probleem oplossen als burgerschapscompetentie. Door op school met maatschappelijk-technologische vraagstukken aan de slag te gaan, doen leerlingen meer kennis op over deze vraagstukken en worden zij zich meer bewust van wat er in de wereld speelt en van hoe zij zich verhouden tot deze vraagstukken. Om met Bèta Burgerschap aan de slag te gaan en het netwerk denken en de discussie doeltreffend te begeleiden, blijkt het professionaliseringstraject van toegevoegde waarde te zijn.
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LI, Zhendong, Hangjian Qiu, xiaoqian Wang, chengcheng Zhang, and Yuejuan Zhang. Comparative Efficacy of 5 non-pharmaceutical Therapies For Adults With Post-stroke Cognitive Impairment: Protocol For A Bayesian Network Analysis Based on 55 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0036.
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Review question / Objective: This study will provide evidence-based references for the efficacy of 5 different non-pharmaceutical therapies in the treatment of post-stroke cognitive impairment(PSCI). 1. Types of studies. Only randomized controlled trials (RCTs) of Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) and Computer-assisted cognitive rehabilitation(CA) for PSCI will be recruited. Additionally, Studies should be available in full papers as well as peer reviewed and the original data should be clear and adequate. 2. Types of participants. All adults with a recent or previous history of ischaemic or hemorrhagic stroke and diagnosed according to clearly defined or internationally recognized diagnostic criteria, regardless of nationality, race, sex, age, or educational background. 3.Types of interventions and controls. The control group takes non-acupuncture treatment, including conventional rehabilitation or in combination with symptomatic support therapy. The experimental group should be treated with acupuncture on basis of the control group. 4.The interventions of the experimental groups were Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) or Computer-assisted cognitive rehabilitation(CA), and the interventions of the control group takes routine rehabilitation and cognition training or other therapies mentioned above that were different from the intervention group. 5.Types of outcomes. The primary outcomes are measured with The Mini-Mental State Examination (MMSE) and/or The Montreal Cognitive Assessment Scale (MoCA), which have been widely used to evaluate the cognitive abilities. The secondary outcome indicator was the Barthel Index (BI) to assess independence in activities of daily living (ADLs).
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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.
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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.