Naturalness Of Human-computer Interaction Research Articles

Researching Student Interaction with GIS Software While Learning Spatial Concepts: Toward a Standard Measure of GIS Interaction

A better understanding of differences in the nature of human computer interaction (HCI) relating to GIS software can help with learning spatial concepts and spatial thinking skills. This motivates our study, which aims to create a standard measure of interaction with GIS software that can be used individually or along with existing measurement instruments for spatial thinking to generalize results across studies for evaluating GIS interaction and usability. The study developed a composite taxonomy identifying types of user interactions and strategies with GIS in relation to different spatial concepts and processes of reasoning. The taxonomy was used as a framework to create tasks which users can complete with GIS software as a measure interaction with GIS. Study data were collected from 35 students enrolled in an online undergraduate course covering fundamentals of geographic information science and technology. Following three course assignments using GIS software to demonstrate a range of spatial concepts for problem solving, students completed various tasks constituting the standard measure of GIS interaction. The results indicate that the standard measure succeeds in measuring how individuals interact with GIS and strategize to complete different spatial tasks. The standard measure of GIS interaction will be useful for the development of usability guidelines for GIS and further contributes to research in the acquisition and application of spatial concepts and reasoning in learning environments.

Forming a method for the integral estimation of interface quality in automated systems based on the quantitative and qualitative indicators

Informatization is considered an integral part of the functioning of the present-day society, which changes the nature of human-computer interaction and interface. It was substantiated that the interface quality determines the success of software marketing. It was shown that quality is estimated by both quantitative and qualitative indicators. Their calculation is complicated because of the lack of an analytical model of usability. Two Euclidean norms in the form of the root mean square value and the largest value of the modulus of the set of attribute values were proposed for continuous factor attributes. The problem was reduced to a geometric inequality by applying development into the Maclaurin series and a three-level comparator. Expressions of the upper and lower boundaries of the generalized index of interface quality represented through a set of attribute estimates were obtained. Expressions of the maximum possible absolute and relative errors represented by the method of recurrent approximation through the first and second derivative of the trend were substantiated. Representation of data in a plane with one common axis turned by a certain angle was applied which enabled fast viewing of all planes. It was shown that when all attributes have a quantitative dimension, it is sufficient to apply one norm to estimate the generalized index. However, in the case of quantitative and qualitative estimations, consecutive application of norms for the root mean square and the maximum possible value of the module solves the problem of comprehensive generalized estimation of interface quality. Experimental study of estimation of the generalized quality of the user interfaces for the MedInfoService medical information system which covers automation of curative treatment processes in outpatient clinics and hospitals was presented.

Trends and challenges of the human-machine interfaces naturality development

The paper contains the overview of various types of user interfaces, problems that arise during their development and usage, as well as possible solutions to manage these problems. In addition, the authors analyzed the increase in the naturalness of human-computer interaction in new user interfaces. Possible prospects for the development of such interfaces in order to increase the naturalness of human-computer interaction are also investigated.

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?

The Impact of In-Group Membership on e-Loyalty of Women Online Shoppers

The objective of this study is to propose and test the Social Identity Approach to Website Design research model, which considers the role of Social Identity in the development of e-Loyalty. Using an online survey instrument comprised of existing Information Systems and Social Identity measures, data were collected from 322 women online shoppers who were members of the salient ingroup. The results of this study indicate that, in women online shoppers, the perception of social presence in an online retail store positively influences their enjoyment of the online shopping experience. The results also suggest that women online shoppers’ enjoyment of an online shopping experience positively influences their intention to revisit the website or recommend the website to other online shoppers, which are e-Loyal behaviors. In addition, this study extends related studies by proposing and testing the psychographic nature of human-computer interaction as a possible catalyst for e-Commerce Success.

Learning automata-based approach to learn dialogue policies in large state space

This paper addresses the problem of scalable optimisation of dialogue policies in speech-based conversational systems using reinforcement learning. More specifically, for large state spaces several difficulties like large tables, an account of prior knowledge and data sparsity are faced. Hence, we present an online policy learning algorithm based on hierarchical structure learning automata using eligibility trace method to find optimal dialogue strategies that cover large state spaces. The proposed algorithm is capable of deriving an optimal policy that prescribes what action should be taken in various states of conversation so as to maximise the expected total reward to attain the goal and incorporates good exploration and exploitation in its updates to improve the naturalness of human-computer interaction. The proposed model is tested using the most sophisticated evaluation framework PARADISE for accessing the travel information system.

Role-Based Human-Computer Interactions

With increased understanding of cognitive informatics and the advance of computer technologies, it is becoming clear that human-computer interaction (HCI) is an interaction between two kinds of intelligences, i.e., natural intelligence and artificial intelligence. This paper attempts to clarify interaction-related terminologies through step-by-step definitions, and discusses the nature of HCI, arguing that shared models are the most important aspect of HCI. This paper also proposes that a role-based interaction can be taken as an appropriate shared model for HCI, i.e., Role-Based HCI.

Hybrid Feature and Decision Fusion Based Audio-Visual Speaker Identification in Challenging Environment

The contribution of this paper is to propose a novel approach of evaluating the performance of a noise robust audio-visual speaker identification system in challenging environment. Though the traditional HMM based audio-visual speaker identification system is very sensitive to the speech parameter variation, the proposed hybrid feature and decision fusion based audio-visual speaker identification is found to be stance and performs well for improving the robustness and naturalness of human-computerinteraction. Linear Prediction Cepstral Coefficients and Mel Frequency Cepstral Coefficients are used to extract the audio features and Active Appearance Model and Active Shape Model have been used to extract the appearance and shape based features for the facial image. Principal Component Analysis method has been used to reduce the dimensionality of large feature vector and to normalize, the vector normalization algorithm has been used. Features and decision both are fused in two different levels and finally four different classifier outputs are combined in parallel fashion to achieve the identification result. The performances of all these uni-modal and multi-modal system performance have been evaluated and compared with each other on VALID audiovisual multi-modal database, containing both vocal and visual biometric modalities.

Likelihood Ratio Based Score Fusion for Audio-Visual Speaker Identification in Challenging Environment

It is well known to enhance the performance of noise robust speaker identification using visual speech information with audio utterances. This paper presents an approach to evaluate the performance of a noise robust audio-visual speaker identification system using likelihood ratio based score fusion in challenging environment. Though the traditional HMM based audio-visual speaker identification system is very sensitive to the speech parameter variation, the proposed likelihood ratio based score fusion method is found to be stance and performs well for improving the robustness and naturalness of human-computerinteraction. In this paper, we investigate the proposed audiovisual speaker identification system in typical office environments conditions. To do this, we investigated two approaches that utilize speech utterance with visual features to improve speaker identification performance in acoustically and visually challenging environment: one seeks to eliminate the noise from the acoustic and visual features by using speech and facial image pre-processing techniques. The other task combines speech and facial features that have been used by the multiple Discrete Hidden Markov Model classifiers with likelihood ratio based score fusion. It is shown that the proposed system can improve a significant amount of performance for audio-visual speaker identification in challenging official environment conditions.

Design issues in a semiotic description of user responses to three interfaces

Semiotics has been used as a theoretical basis for systematizing the increasingly interdisciplinary nature of human-computer interaction. This paper focuses on the descriptive qualities of semiotics which are useful for understanding the user perspective of computer systems. The end goal is to assess the application of such semiotic description in interface design. An experiment is presented in which volunteers were asked to use and describe three interfaces created with the Apple Macintosh product Hypercard. A semiotic interpretation is given to their responses based on six principles taken from the relevant literature. The evaluation is then used to lay down a set of general guidelines for interface design.