Quantum Cognition based on an Ambiguous Representation Derived from a Rough Set Approximation

Over the last years, in a series papers by Arrechi and others, a model for the cognitive processes involved in decision making has been proposed and investigated. The key element of this model is the expression of apprehension and judgement, basic cognitive process of decision making, as an inverse Bayes inference classifying the information content of neuron spike trains. For successive plural stimuli, it has been shown that this inference, equipped with basic non-algorithmic jumps, is affected by quantum-like characteristics. We show here that such a decision making process is related consistently with ambiguous representation by an observer within a universe of discourse. In our work ambiguous representation of an object or a stimuli is defined by a pair of maps from objects of a set to their representations, where these two maps are interrelated in a particular structure. The a priori and a posteriori hypotheses in Bayes inference are replaced by the upper and lower approximation, correspondingly, for the initial data sets each derived with respect to a map. We show further that due to the particular structural relation between the two maps, the logical structure of such combined approximations can only be expressed as an orthomodular lattice and therefore can be represented by a quantum rather than a Boolean logic. To our knowledge, this is the first investigation aiming to reveal the concrete logic structure of inverse Bayes inference in cognitive processes.Comment: 23 pages, 8 figures, original research pape

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

Interactive and life-long learning for identification and categorization tasks

Abstract (engl.) This thesis focuses on life-long and interactive learning for recognition tasks. To achieve these targets the separation into a short-term memory (STM) and a long-term memory (LTM) is proposed. For the incremental build up of the STM a similarity-based one-shot learning method was developed. Furthermore two consolidation algorithms were proposed enabling the incremental learning of LTM representations. Based on the Learning Vector Quantization (LVQ) network architecture an error-based node insertion rule and a node dependent learning rate are proposed to enable life-long learning. For learning of categories additionally a forward-feature selection method was introduced to separate co-occurring categories. In experiments the performance of these learning methods could be shown for difficult visual recognition problems

Qualitative Research and Computer Analysis: New Challenges and Opportunities

The use of computers for Qualitative Data Analysis (QDA) in qualitative research has been growing rapidly in the last decade. QDA programs are software packages developed explicitly for the purpose of analyzing qualitative data. A range of different kinds of program is available for the handling and analysis of qualitative data, such as Atlas/ti, HyperRESEARCH, and NUD*IST. With the development of new technologies, the QDA software has advanced from the efficient code-and-retrieve ability to the development of sophisticated organizing system or conceptual tool for data analysis as well as information management. This presentation is an initial discussion on the impact of database and web technology on QDA. The following issues will be discussed: why or how QDA methods are different from the database approach as means of managing and exploring unstructured data; what data mining and web search tools offer to qualitative research; what QDA software should do to support web-based research; and what methodological and technical problems are posed for web-based QDA by the web itself.published_or_final_versionCentre for Information Technology in Education, University of Hong Kon

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Kansei engineering with online review mining methodology for robust service design

Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers’ emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE’s flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei’s validity and the proposed solution’s robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework