Development of an explicit and implicit knowledge identification tool for the analysis of the decision-making process of traditional Asian medicine doctors

Background and objectives: While pattern identification (PI) is an essential process for diagnosis and treatment in traditional Asian medicine (TAM), it is difficult to objectify since it relies heavily on implicit knowledge. Here, we propose a machine learning-based analysis tool to objectify and evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge. Methods: Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TAM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the explicit and implicit importance scores of the AR questionnaire, which were obtained through doctors′ explicit scoring and feature evaluations of machine learning models, respectively. The analysis tool consists of eight evaluation indicators used to compare, analyze and visualize the explicit and implicit knowledge of TAM doctors. Results: The analysis results for 8 doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. We also conducted a postquestionnaire study with the doctors who participated to evaluate the applicability of our tool. Conclusions: This study proposed a tool to evaluate and compare decision-making processes of TAM doctors in terms of their explicit and implicit knowledge. We identified the differences between doctors′ own explicit and implicit knowledge and the differences among TAM doctors. The proposed tool would be helpful for the clinical standardization of TAM, doctors′ own clinical practice, and intern/resident training.

Modes and Forms of Knowledge of Farming Entrepreneurship: An Ethnographic Analysis among Small Farmers in NW Italy

This article investigates the modes and forms of knowledge underpinning farming entrepreneurship through an ethnographic case study of Alessandria province in NW Italy. It shows that farming entrepreneurs base their decisions on explicit and implicit knowledge encompassing forms of knowledge linked to the environment where they live, their trade, the characteristics of their firms, issues concerning their family and private life, and even the emotions linked with their surroundings. All these forms of knowledge inform their vision of their future and guide them in their choices in terms of investments and crop selection. Accordingly, the article argues that farming entrepreneurship is embedded in the locale.

Human Factors in Interactive Machine Learning: A Cybersecurity Case Study

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.

Implicit and explicit learning of Bayesian priors differently impacts bias during perceptual decision-making

Knowledge without awareness, or implicit knowledge, influences a variety of behaviors. It is unknown however, whether implicit knowledge of statistical structure informs visual perceptual decisions or whether explicit knowledge of statistical probabilities is required. Here, we measured visual decision-making performance using a novel task in which humans reported the orientation of two differently colored translational Glass patterns; each color associated with different orientation probabilities. The task design allowed us to assess participants’ ability to learn and use a general orientation prior as well as a color specific feature prior. Classifying decision-makers based on a questionnaire revealed that both implicit and explicit learners implemented a general orientation bias by adjusting the starting point of evidence accumulation in the drift diffusion model framework. Explicit learners additionally adjusted the drift rate offset. When subjects implemented a stimulus specific bias, they did so by adjusting primarily the drift rate offset. We conclude that humans can learn priors implicitly for perceptual decision-making and depending on awareness implement the priors using different mechanisms.

A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure

This paper describes the creation of a domain ontology to represent knowledge to populate a knowledge base to be used by agents, in the environment of Internet Infrastructure routing domains. Protégé 5 was used, which produces results suitable for both software-developed agents and humans. The knowledge created with Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are presented and the ontology is made available for public use.The content produced with Protégé 5 will be used to populate the knowledge base of the Structure for Knowledge Acquisition, Use, Learning and Collaboration (SKAU), an environment to support intelligent agents over Internet Autonomous Systems domains.

Defamiliarizing Technology, Habituation, and the Need for a Structuralist Approach

In response to my article “Earth, Technology, Language”, Christopher Müller asks whether use-oriented theory and Wittgensteinian language can capture the structural relations of power that shape habituation and argues that digital media do not provide opportunities for empowerment and democracy because there is no co-ownership. In my reply I argue that I have shown that this can be done with the broader conception of use I propose, that the grammar of technology should also be understood in terms of implicit knowledge, and that technology, like language, also has a public dimension: I claim that there is no such thing as a private technology or private power, and that some degree of co-ownership or resistance is possible. In the second part of the paper I reply to Bas de Boer’s questioning of my criticism of postsphenomenology. I insist that postphenomenology does not have the full instrumentarium to carry out an adequate and comprehensive analysis of the social dimension of technology use, and that it is important to attend to the structural dimension of technology, with or without use of the term ‘transcendental’. I clarify my use of the term as referring to conditions of possibility.

THE CONTRIBUTIONS OF IMPLICIT-STATISTICAL LEARNING APTITUDE TO IMPLICIT SECOND-LANGUAGE KNOWLEDGE

This study addresses the role of domain-general mechanisms in second-language learning and knowledge using an individual differences approach. We examine the predictive validity of implicit-statistical learning aptitude for implicit second-language knowledge. Participants (n = 131) completed a battery of four aptitude measures and nine grammar tests. Structural equation modeling revealed that only the alternating serial reaction time task (a measure of implicit-statistical learning aptitude) significantly predicted learners’ performance on timed, accuracy-based language tests, but not their performance on reaction-time measures. These results inform ongoing debates about the nature of implicit knowledge in SLA: they lend support to the validity of timed, accuracy-based language tests as measures of implicit knowledge. Auditory and visual statistical learning were correlated with medium strength, while the remaining implicit-statistical learning aptitude measures were not correlated, highlighting the multicomponential nature of implicit-statistical learning aptitude and the corresponding need for a multitest approach to assess its different facets.

Investigating the Roles of Implicit and Explicit Knowledge in the Production of Grammatical Errors

This research aims to investigate the role of implicit and/or explicit knowledge in the production of grammatical errors in academic texts. Explicit knowledge is defined as conscious and declarative knowledge used to monitor language production while implicit knowledge is defined as an intuitive knowledge which enables the second language learners to use the language spontaneously without any reflection (Zhang, 2015). The reasons why this research is conducted is due to the fact that the students are still producing errors even though they have learnt English since elementary school. The data is gained from the academic text written down by fifteen English department students studying in a university in Jakarta. It is analyzed by using two different measurements: (1) Delayed Grammatical Judgment Tests (GJT), and (2) Interview including Metalingual Comment to investigate the role of explicit knowledge in the production of grammatical errors. The other two measurements are also used; they are (1) Timed Grammaticality Judgment Test (TGJT), and (2) Oral Production Test (OPT) to investigate the role of implicit knowledge. The results show that 19.2% of grammatical errors are produced due to the implicit knowledge and 80.8% is due to explicit knowledge. Since the explicit knowledge plays an important role in producing the grammatical errors, it can be concluded that teaching English grammar for university students is still important. If possible, the English Grammar lessons should be given more rooms in the overall curriculum