Although there is abundant, significant and expanding literature on teaching, learning and knowledge generation beliefs and practices, and an equally extensive strong body of work exploring physical and technological environments and systems for learning and teaching, published research intersecting both is uncommon and ‘not well understood’ (Temple, 2007, p. 4). Yet, inspection of this nexus reveals it to be a site in which diverse conversations of disparate parties can, and need to, carry on. Frequently, terms like ‘interdisciplinary’ and ‘blended’ arise in current educational discourse, but more often than not they are used rhetorically whilst intellectual and technological pursuits continue as usual along discrete paths (Brew, 2008; Huutoniemi, Thompson Klein, Bruun, & Hukkinen, 2010; Selwyn, In press).
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The wise consider the entire universe as their preceptor (Caraka Sahitā CS.Vi.14). Knowledge generation in Āyurveda has followed mainly four types of siddhānta (theory) viz. 1. śarvatantra siddhānta (the theory which is accepted by all śāstrā-s), 2. pratitantra siddhānta (the theories not in any other śāstrā-s:), 3. adhikaraa siddhānta (from the related topics and theories) and 4. abhyupagama siddhānta (unproven and not fully tested theories). These siddhānta-s were debated among groups of scientists and students of various schools of thought. The methods of such discourses are guided by 44 rules of logic, ‘vādamārga’ (CS. Vi.8.26) to justify propositions. The Āyurvedic texts are formulated based on the 32 criteria (Suśruta Sahita; SS.Utta.65) and 36 criteria by Caraka (CS.Si.43) known as tantra-yukti in order to ensure rigour. Āyurveda considers knowledge of whole cannot be obtained by knowing its parts.
It is evident that the respondent’s motivation to generate and utilise tacit knowledge was largely influenced by their perception of extent to which they and their knowledge are recognised. Further, the motivation to generate and utilise tacit knowledge was evident from the respondents’ willingness to learn and support others. Some interviewees (DRT1, OLE1) felt that colleagues/ sub-ordinates are frequently approaching them when faced with a problem, as they are keen to help others. Also, most of the interviewees agreed that they rely on colleagues to get advice. DRT1 felt comfortable learning from his young, talented team members. Hence, this willingness to support and learn from other people showed their motivation to utilise tacit knowledge within the company. MGR2 suggested that she wanted to learn anything new, rather than passing it to someone else, whilst OLE4 supported changes within the company, as it will bring him the opportunity to learn. As such, this willingness to learn showed their motivation to generate tacit knowledge. However, as iterated by MGR1 and DRT1 their motivation to share tacit knowledge was subject to the nature of the knowledge. Although MGR1 was willing to share tacit knowledge he had gained from working with the company, he believed that this is not done to the extent of other business parts, due to the nature of the subject. In contrast, DRT2 believed that knowledge relating to his area of work is always shared. As a result, it is concluded that the level of recognition, willingness to learn and support, and nature of knowledge are important determinants of the ‘motivational driver’ to generate and utilise tacit knowledge at individual level.
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To enhance the quality of API reference documentation and the efficiency with which the relevant information it contains can be accessed, it’s necessary to first understand its contents by analyzing it. Therefore, to reason about the quality and value of Java API reference documentation, focus should be about what knowledge it contains. Because Knowledge refers to retrieve useful information from data and then use this knowledge for specific purpose. By analyzing the contents of Java API Reference Documentation Knowledge is generated and this knowledge can be categorized further.
c) alignment of policy modelling actors with researchers. The policy decision making rounds and research- based knowledge generation round needs to be aligned. The alignment could be reached through policy actors’ interaction. Using the terminology of participatory policy decision making, the researcher generating tacit knowledge for policy decision is converted to the policy actor with the stake to recognise the hidden phenomena covering the other actors’ stakes and fundamental consistent pattern. Equal actor distribution and stake representation is the background for participatory policy modelling, which brings creativity, shared knowledge, commitments and responsibility. Networks of actors, acting in the knowledge generation policy rounds, give rational response to emerging social problems via the non-hierarchical and interdependent nature. The non-hierarchical nature of network lets a single policy actor act in a coherent manner, equally sharing own knowledge and responsibility among round participants. Researchers bound with policy actors ensure awareness, confidence and mutual understanding and use rounds as the platform for communication, which is based on self-confidence, they enable fully understandable knowledge generation, based on scientifically relevant, politically evidenced and socially acceptable set of policy decision.
Noun-verb event frame (NVEF) knowledge in conjunction with an NVEF word-pair identifier [Tsai et al. 2002] comprises a system that can be used to support natural language processing (NLP) and natural language understanding (NLU). In [Tsai et al. 2002a], we demonstrated that NVEF knowledge can be used effectively to solve the Chinese word-sense disambiguation (WSD) problem with 93.7% accuracy for nouns and verbs. In [Tsai et al. 2002b], we showed that NVEF knowledge can be applied to the Chinese syllable-to-word (STW) conversion problem to achieve 99.66% accuracy for the NVEF related portions of Chinese sentences. In [Tsai et al. 2002a], we defined a collection of NVEF knowledge as an NVEF word-pair (a meaningful NV word-pair) and its corresponding NVEF sense-pairs. No methods exist that can fully and automatically find collections of NVEF knowledge from Chinese sentences. We propose a method here for automatically acquiring large-scale NVEF knowledge without human intervention in order to identify a large, varied range of NVEF-sentences (sentences containing at least one NVEF word-pair). The auto-generation of NVEF knowledge (AUTO-NVEF) includes four major processes: (1) segmentation checking; (2) Initial Part-of-Speech (IPOS) sequence generation; (3) NV knowledge generation; and (4) NVEF knowledge auto-confirmation.
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ledge is one of the most powerful production engines”. K. Viig determined position of knowledge in a modern company, and D. Stonehouse investigated conditions fa- voring knowledge control system functioning. E. Broo- king and Т. Stewart studied significance ofintellectual capital for a company. P. Druker considered importance of transition to knowledge management as a specific strategic concept. I. Nonaka determined capability of an economic entity to transform nonformalized knowledge into formalized one as a fundamental criterion of as- sessment regarding knowledge generation efficiency. R. Solow suggested a type of relationship specifying the re- sults of scientific and technological advance impact on the results of introduction of innovations, which cause engineering process change. F. Valenta introduced clas- sification in terms of profoundness of changes made in a production process. B. Twiss, carrying out research into new knowledge generation issues, determined that 80- 90% of activity in the context of new knowledge genera- tion are not economically efficient in terms of real mar- ket activity.
This article intends to open space for future research on the development of more effective leaderships in Higher Education Institutions (IES), in the sense that when the teacher perceives that imself from the effects that Such actions being. Private Higher Education Institutions (IES), like all profit organizations, need to survive in the business world, not only by providing on but by ensuring profits for the operation of their activities. These need to guarantee the adequate application of their resources to be able to capture clients / students and survive in a frenetic, no matter how much the means must be applied to conquer it, that is, in words Of Machiavelli: "The End justifies the means". And in this scenario of the great competitions arise the modern questions about the possible styles or types of ip that would be more adequate to make the organizations reach their objectives, that is, it would be necessary to make that the organizations reach their objectives that the directions Or coordinators of higher education apply abusive supervision, because what has been observed, currently, is that in the eagerness to earn profits, or reach their goals, some leaders of IES has been leaning in the lines of Machiavelli and are very inclined to exercise leadership Abusive. Thus, the key ill be to analyze the impacts of an abusive coordination on the performance of teachers in the work with focus on "knowledge generation"; Such a study is of great relevance to the organizational world and, mainly, to Higher Education Institutions (HEIs), since these are the main
Open Information Extraction Open Informa- tion Extraction (OpenIE) aims to extract triple knowledge from raw text. It finds triples that have specific predefined relations by using lexical and syntactic patterns (Mintz et al., 2009; Fader et al., 2011). Several neural-network-based relation ex- traction methods have been proposed (Lin et al., 2016; Zhang et al., 2017). These models construct classifiers to estimate the relation between two ar- bitrary entities. OpenIE models are trained with sentence-level annotation data or distant supervi- sion, while our model is trained with triples in a knowledge base. Since openIE can extract new triples from raw text, it can be used to make aug- mentation data for the CKB completion model. Knowledge generation There are several stud- ies on the knowledge generation task that use neural network models. For example, Hu et al. (2017) proposed an event prediction model that uses a sequence-to-sequence model. Prakash et al. (2016) and Li et al. (2017) proposed a para- phrase generation model. These studies targeted only specific relationships and did not explicitly incorporate relations into the generation model. Our CKB generation model explicitly incorpo- rates relation information into the decoder and can model multiple relationships in one model. 8 Conclusion
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Figure 3 shows our overall approach. We first re- trieve topically related news documents using tags from the video meta-data. Next, we apply entity discovery and linking as well as event extraction methods to the documents, which yields a set of entities and events relevant to the video. We rep- resent this background knowledge in two ways: 1) we encode the entities through entity embeddings and 2) we encode the event and entity typing in- formation into a knowledge gate vector, which is a one-hot vector where each entry represents an entity or event type. Finally, with the video and these representations of the background knowl- edge, we employ our KaVD network, an encoder- decoder (Cho et al., 2014) style model, to generate the description.
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As the events of the last quarter of the XX century demonstrated, Neo-Schumpeterian theories can adequately explain the nature and driving forces of modern post-industrial economic development. In this regard, attention can be paid to the fact that this is paradoxical enough: Schumpeterian conceptual approach is barely studied in university programs, but de-facto it lays at the heart of economic strategies and current policies of developed and dynamic successful countries. The economic strategy of the European Union is a vivid example. Ten-year strategies – the Lisbon strategy (2000-2010) and next the “ Europe 2020 ” strategy – actually represent the Schumpeterian and Neo-Schumpeterian concept, where new knowledge and innovations are recognized as the main driving force of economic development (European Commission, 2010; Bazhal, 2013; Carayannis, 2013). These strategies make an emphasis on the fact that along with implementation of traditional goals of macroeconomic policy – attainment of macroeconomic stability, improving the efficiency of available resources and support of employment – today the leading role is assigned to those challenges associated with facilitating an accelerated transition to an innovative knowledge economy.
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With the cycling of nursing research, theory, practice, education, and policy making, nursing knowledge has been developed and disseminated. A clinical practicum is a good way to deliver nursing knowledge. Also an area of end-of-life (EOL) care is a good field to integrate nursing knowledge with dif- ferent perspectives and multiple ways of knowing. The purposes of this paper were 1) to describe nursing knowledge and EOL with an integrated view, 2) to critique the current phenomenon of the field of EOL, and 3) to propose a re- design of the clinical practicum in EOL care. An integrative review was per- formed as the method of the study. The results showed that the reflection of nursing knowledge from different perspectives regarding EOL may help the nursing discipline develop various nursing knowledge, practice, education, and research. However, this paper shows that recent education is more fo- cused on empirical knowledge, problem-solving approaches with a totality paradigm, and didactic teaching-learning methods. For redesigning the clini- cal practicum, it is necessary to reestablish a caring relationship between nursing instructors and nursing students. In terms of the teaching-learning process, the human-becoming teaching-learning process is recommended over the nurse-becoming process.
Research is a continuously evolving process which makes the generation of new knowledge and validates or rejects the present knowledge. The modern system of medicine has been evolved with the rigorous clinical trials of a particular drug, the de- tailed study of the safety and efficacy of the drug. The pharmacodynamics and pharma- cokinetic properties of the drug has been studied in detail and the evidence has been proved time to time. The most important part is that the modern system of medicine has used advancements in the knowledge of basic life sciences like physiology, bioche- mistry, microbiology, pharmacology and pathology. Conversely, in Ayurveda the ba- sic concepts of Ayurveda has not been yet defined or explained and the clinical trials of the drug has been carried out and said effa- cacious. For example Ekangveer Rasa  is
Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story com- prehension. This task requires not only to understand the con- text clues which play an important role in planning the plot, but also to handle implicit knowledge to make a reasonable, coherent story. In this paper, we devise a novel model for story ending generation. The model adopts an incremental en- coding scheme to represent context clues which are spanning in the story context. In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and rea- sonable endings. Through building context clues and using implicit knowledge, the model is able to produce reasonable story endings. Automatic and manual evaluation shows that our model can generate more reasonable story endings than state-of-the-art baselines 1 .
There are various views among the academicians, researchers and practitioners on the concepts and definitions of knowledge but central theme is still the same for all of them. There is single definition of KM not found till today. It has been defined in a number of ways, but in general the thought relates to unlocking and leveraging the knowledge of individuals so that this knowledge becomes available as an organizational resource. KM makes knowledge independent from the particular individuals. Duffy  defines Knowledge Management as a set of business practices and technologies used to assist an organization to obtain maximum advantage from one of its most important assets — knowledge. Sveiby  defined KM as, The art of creating value from an organisation is intangible assets.´ Davenport and Prusak  defined KM as, ³KM is concerned with the exploitation and development of the knowledge assets of an organisation with a view to furthering the knowledge objectives.´ Knowledge management is the systemic and organizationally specified process for acquiring, organizing and communicating knowledge of employees so that other employees may make use of it to be more effective and productive in their work .
Rural women in Jiangyong County, Hunan Province, developed their own unique script called nüshu ( 女书 ), a phonetic form of writing different from Chinese script. Nobody is quite sure how or how long ago nüshu was invented; we only know that it existed before the twentieth century. Largely barred from learning to read and write Chinese script, the women of Jiangyong County taught nüshu to each other, passing the knowledge from generation to generation (Chiang, 1995, pp. 273-277; S. Liu & Hu, 1994). Chinese, and then western, scholars began studying nüshu in the 1980s, when it was already dying out. By that time only a small group of elderly women could still read and write nüshu. Scholars have examined nüshu from the perspectives of anthropology, linguistics, literature, literacy, and women’s studies (Chiang, 1995; Gong, 1990; Idema, 1999; Idema & Grant, 2004; F. Liu, 2001; S. Liu & Hu, 1994; McLaren, 1996, 1998, 1999; Silber, 1994, 1995; Zhao, 1992). I would like to examine nüshu from the perspective of curriculum theory.
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Knowledge distillation can effectively transfer knowledge from BERT, a deep language repre- sentation model, to traditional, shallow word embedding-based neural networks, helping them approach or exceed the quality of other heavyweight language representation models. As shown in previous work, critical to this dis- tillation procedure is the construction of an unlabeled transfer dataset, which enables ef- fective knowledge transfer. To create transfer set examples, we propose to sample from pre- trained language models fine-tuned on task- specific text. Unlike previous techniques, this directly captures the purpose of the trans- fer set. We hypothesize that this principled, general approach outperforms rule-based tech- niques. On four datasets in sentiment clas- sification, sentence similarity, and linguistic acceptability, we show that our approach im- proves upon previous methods. We outper- form OpenAI GPT, a deep pretrained trans- former, on three of the datasets, while using a single-layer bidirectional LSTM that runs at least ten times faster.
Horezu pottery is a traditional craft, still kept in northern Valcea county, at the foot of the Carpathian mountains. The craft was included in the UNESCO Immaterial Heritage List as it reflects the local identity and knowledge of potters from Horezu, kept from generation to generation, thus preserving techniques and specific motifs that give it a unique character. However, the craft is in danger due to the reduced opportunities in the rural areas of supporting this occupation financially, thereby reducing the number of craftsmen. Like all rural areas in Romania, Horezu faces the migration of the population towards towns and cities.
Still on this subject, another relevant conclusion mentioned by (Gerv´as and Le´on, 2014) is that the same information may be represented through dif- ferent data structures without affecting its essence, or a data structure can be extended for representing additional types of information. For example, Bru- tus (Bringsjord and Ferrucci, 2000) used a specific representation for representing the betrayal. Bru- tus was developed using a logic-programming sys- tem called FLEX, which is based on the program- ming language Prolog. Its knowledge about betrayal was modelled by a set of statements in FLEX, called frames. Every frame formalized the essential char- acteristics of betrayal: the betrayer, the betrayed, the locations, the actions involved, etc. Mexica (Perez y Perez, 1999) used a wider representation of the re- lationships between the characters, not specifically focused on betrayal. Relations in Mexica are of two types: emotional links and tensions. Emotional links represent affective reactions between charac- ters. They are defined in terms of three attributes: type (love or friendship), valence (positive or nega- tive) and intensity. Tensions represent if there is a conflict between two characters. It is defined by a type (of conflict) and a state (on or off).
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The problem of Generating Referring Expressions (GRE) can be summed up as a search for the prop- erties in a knowledge base (KB) whose combination uniquely distinguishes a set of referents from their dis- tractors. The content determination strategy adopted in such algorithms is usually based on the assump- tion (made explicit in Reiter (1990)) that the space of possible descriptions is partially ordered with respect to some principle(s) which determine their adequacy. Traditionally, these principles have been defined via an interpretation of the Gricean maxims (Dale, 1989; Reiter, 1990; Dale and Reiter, 1995; van Deemter, 2002) 1 . However, little attention has been paid to con-