Search results

Research of artificial intelligence (AI), has aimed at making machines intelligent via the simulation of natural intelligence, particularly human intelligence. During the past decades, there have been three major approaches aimed at achieving this goal, namely structuralism, functionalism and behaviorism. Unfortunately, they work separately and contradictorily to a large extent. The purpose of this paper is to present a better and more unified approach.

The paper analyses each of the three major approaches to AI, describing their advantages and disadvantages. There then follows an attempt to explore a new and more reasonable approach to AI. The new approach should be able to solve all the problems that the existing approaches can solve on one hand and can solve the problems that the existing approaches cannot solve on the other hand.

It was found that the more reasonable and more powerful approach is the one that directly touches the common and core mechanism of intelligence formation. This is due to the fact that the mechanism of intelligence formation is much more essential than other windows of an intelligent system, such as structure, function, or behavior. It was also found that the common and core mechanism of intelligence formation can be implemented through the information‐knowledge‐intelligence transformation. The third finding is that the three existing approaches are special cases of the mechanism approach under different conditions and can thus be harmoniously unified within the frame of the mechanism approach.

The three findings in the paper: the mechanism approach, the implementation of the mechanism approach, and the unification of the existed three major approaches, are important laws never found before in the literature. The breakthrough of the mechanism approach to AI will be of great significance to both theoretical and practical research in AI in the years to come.

Knowledge is perceived as very important asset for organizations and knowledge management is critical for organization competitiveness. Because the nature of knowledge is always represented as complex and varied, it is difficult to extend effectiveness of knowledge re‐use in organizations. In this article, an approach based on the Zachman’s Framework to externalize organizational knowledge into semi‐structured knowledge is developed, and eXtensible Markup Language (XML) is applied to transform the knowledge into documents. In addition, latent semantic indexing (LSI), which is capable of solving problems of synonyms and antonyms, as well as improving accuracy of document searches, is incorporated to facilitate search of semi‐structured knowledge (SSK) documents based on user demands. The SSK approach shows great promise for organizations to acquire, store, disseminate, and reuse knowledge.

The purpose of this paper is to consider “at home ethnography” and “abroad ethnography” not as labels standing for different kinds of fieldwork “out there” but rather as the poles of a continuum identifying the ethnographer’s situated, relative and ever changing epistemic status.

Building on data from a recent fieldwork in an intensive care unit, the author identifies the different epistemic circumstances that originate from the entanglement of the multiple territories of knowledge at stake in any ethnography of complex organizations.

The analysis shows how the participants’ relative access to knowledge and rights to claim it vary according to the circumstances and the unfolding of the interaction. The discussion advances that the ethnographer oscillates between “being abroad” and “being at home” as if he was constantly moving between the two classical positions of ethnographic work: making the familiar strange as it is typical of ethnographies focusing on the “very ‘ordinariness’ of normality” (Ybema et al., 2009, p. 2), and making the strange familiar as it is typical of anthropologists studying exotic communities.

The paper contributes to the still ongoing debate on “at home” organizational ethnography, by addressing the limits of the “insider doctrine” (Merton, 1972) that still pervades contemporary ethnography and proposes cognitive oscillation as the challenging mindset of any ethnographer-in-the-field.

This study aims to determine the effect of online social networks on university students’ environmentally responsible behavior (ERB). This research aimed to develop and test a behavioral model in the context of online social networks, where students’ attitudes, knowledge and behavior influence their ERB.

This study used a quasi-experiment with a pretest-posttest design and a random parallelization control group. The research used a questionnaire to assess ERB, environmental attitudes and environmental knowledge. The researcher randomly assigned 120 students to an experimental and a control group of equal size. Both groups initially completed a pretest. The experimental group was trained in environmental issues over four months (an academic semester) via an online social network.

The findings indicated that the social network had a significant effect on motivating ERB. Additionally, it improved environmental attitudes. According to the results, online social networks such as Facebook can significantly aid in teaching and learning environmental issues in formal academic settings.

Online social networks facilitated significant cognitive progress in environmental education. The primary objective is to educate students about ERB.

The purpose of the Ethics Support Office, funded by the Clinical and Translational Science Award (CTSA) and administered by the Institute for Translational Sciences (ITS) at the University of Texas Medical Branch (UTMB), is to provide research ethics support to faculty, fellows, and students. This chapter reports on an ongoing qualitative study to understand scientists' views on ethical issues in team science and their suggestions for advancing ethical policy and activities in order to improve ethics training. We originally conducted face-to-face, semistructured, qualitative interviews with a convenience sample of 20 key ITS researchers, representing the majority of researchers. The scientists' most general approach to ethics – in perceiving them, understanding them, and applying them – is to appreciate ethics in terms of their relevance to particular research situations and problems. They prefer to deal with ethics as a common feature and value incorporated into their work. Respondents suggested that those teaching ethics in multidisciplinary translational research must develop strategies that help scientists see and understand the relevance of ethics education to their work. Strategies include improving communication skills, providing shared opportunities for learning, sensitizing researchers to the demands on others on the team who are expected to contribute data and knowledge to the success of the project, and imbedding ethicists on research teams. In tune with the key finding of the study, ethics instructors and coaches need to become well acquainted with the nuances of their scientists' work. This approach will respond to the scientists' desire to conduct ethical research, but in practical terms of the specialized nature of their work.

The purpose of this paper is to investige the general computing mechanisms of solving the system information problems of interpretation and its fundamental limitations, due to physical basis Turing machine.

For creation of theoretical base of methodology, the authors make an attempt to demonstrate the possibility of a constructive building of Turing machine as meta-ontological basis of computing. In the course of this building the role of the operator of atomic implicative transition if-then as generic operator of recognition/decision-making is shown. In order to substantiate the thesis about the determinative role of implicative transition in the interpretation mechanisms, the authors will carry out the comparative analysis methods of interpretation in systems of pattern recognition and expert systems interpretation type.

The carried-out analysis allows to formulate a common mechanism underlying the classical methods of solving problems of interpretation and to concretize the fundamental limitations of these methods, caused computational basis of their actualization. The cybernetic interpretation of this mechanism is offered.

The fundamental limitations of classical methods of solving problems of interpretation sets the boundaries of the cybernetic approach and allows to outline a way out beyond it. In this context, the authors put forward knowledge-based mechanism of perceptual modeling of dynamics of system visual environment – autonomous agent.

This article draws from ethnomethodology and poststructural discourse analysis to examine commonsense knowledge about whiteness and white racial identities. In order to get at that which most broadly passes as matters of commonsense in the United States, the research design includes analysis of both interview and television data. I make two sets of concurrent arguments, one that regards the production of whiteness as a kind of normalcy against which race and racialization is made meaningful and another concerned with the analytical power derived by combining ethnomethodology and poststructural discourse analysis. I illustrate local practices for interrupting hegemonic reproductions of whiteness and conclude with methodological considerations.

Purpose – This chapter responds to interdisciplinary debates regarding studies of sex, sexuality, and gender. I briefly examine how the sex/gender paradigm of the 1960s shaped feminist theory in the social sciences and explore two feminist frameworks that have contested the sex/gender paradigm: West and Zimmerman's “doing gender” and Butler's performativity. I situate this literature, and related debates about intersectionality, in the context of Margaret Andersen's (2005) Sociologists for Women in Society (SWS) feminist lecture.

Methodology/approach – Using empirical analyses of brief television excerpts, I develop an ethnomethodological study of practice and poststructural analysis of discourse to demonstrate how trenchant forms of cultural knowledge link together gender, sex, and sexuality.

Findings – Sex and gender function as disciplinary forces in the service of heterosexuality; consequently studies of gender that do not account for sexuality reproduce heterosexism and marginalize queer sexualities. These findings, considered in relationship to Andersen's analysis of intersectionality, illustrate both a narrow conceptualization of the field rooted to a 19th century European model and a methodological mandate that must be examined in relationship to the politics of social research.

Practical implications – A more fruitful conceptual starting point in thinking through intersectionality may be citizenship, rather than systematic exploitation of wage labor. In addition, a more full analysis of intersectionality would also require that we rethink our methodological orientations.

Originality/value of paper – The chapter illustrates some of the analytic effects and political consequences that commonsense knowledge about gender, sex, and sexuality holds for feminist scholarship and advances alternative possibilities for future feminist research.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.