The Design of Computer Interfaces Adaptive to Human Emotion: Current Issues and Research Directions

Despite the rapid advancement of computer technology, computers remain incapable of understanding human emotion. As a result, users have often been unaided for their aversive emotion that may take place during their computer tasks. This may be detrimental to positive and productive interactions between users and computers. This paper reviews some empirical studies regarding the effect of emotion on computer work and conceptualizes what constitutes an emotional computer. It is proposed that the emotional computer be designed to understand human emotion and adapt its interface accordingly. This paper raises a number of research questions in relation to such issues as measurement (e.g., automatic detection of human emotion, time delay), signal processing (e.g., accuracy) and user interfaces (e.g., ways to alleviate the intensity of negative emotion). Considering that there has been very little research on the design and aftermath of emotional computers, further studies are urgently needed

International negotiation support systems

Almost all results for negotiation support systems have been obtained in a United States context, based on the conventions and culture of American group processes. This paper reports on the construction and testing of a prototype group decision support system (GDSS) to support international negotiation. The system was shown to work and to provide gains in both the quality of the interaction and the attitude toward the decision reached. Although the prototype system is narrow in scope focusing on two specific cultures and a single scenario and uses simple and limited technology, the results obtained suggest that computer-based intercultural GDSS can help people in managing the added complexity of multinational negotiation and in interacting effectively with one another

Effect of Cognitive Style and Physiological Phenomena on Judgmental Time Series Forecasting

Managerial intuition is a well-recognized cognitive ability but still poorly understood for the purpose of developing effective decision support systems. This research investigates whether the differences in accuracy of “time series forecasting” are related to the differences in one’s cognitive style, using statistical test The hypotheses established in the research model did not have positive correlation The lack of correlation between “cognitive style and physiological measures” and accuracy in forecasting may be caused by uncontrolled external variable. Thus, further analyses on physiological characteristics and brainwaves are needed. The approaches such as neural network and data mining are proposed

A Data Mining for the Effect of Cognitive Style, Subjective Emotion, and Physiological Phenomena on the Accuracy of Judgmental Time-Series Forecasting

Data mining is finding hidden rules in given dataset using non-traditional methods. The objective is to discover some useful tendency or patterns from the given collection of data. We had mined the rules representing the effect of cognitive style, subjective emotion, and physiological phenomena on the accuracy of subjects\u27 judgmental time-series forecasting. Then we have tried to find out any consistent tendencies in the frequent rules. Subjects in Analytic-style show more accurate forecasting. Subjects in relaxed mode show more accurate forecasting. And Subjects’ left EEG and beta rhythm seem to have a significant effect on their forecasting accuracy. But additional data mining to the other effects should be made

Comparing the Effects of Cognative Style, Subjective Emotion, and Physiological Phenomenon on the Accuracy of Intuitive Time-Series Forecasting Using an SONN: A Proposal

Self-organizing neural network (SONN) is known to be able to extract features in input samples [Kohonen, 1995]. By updating not only the weight vector of the winning neuron in the self-organizing layer but also those of its neighboring neurons, neighboring neurons would eventually become to respond similarly to a specific input vector. Then the distribution of winning neurons for a class may be distinguished from those for other classes. Luttrell proposed a SONN which can inherently use the correlation between input vectors of separate clusters and he called it self-supervised adaptive neural network [Luttrell, 1992]. In this report, we propose the use of the selfsupervised adaptive algorithm in analyzing the correlation between cognitive style and the accuracy of intuitive time-series forecasting, and suggest a way to compare the relative degree of correlation between each of cognitive style, subjective emotion and physiological phenomenon and the accuracy of intuitive time-series forecasting

A comparative study of the use of executive information systems between Korea and the United States

Since their conception, EIS have been developed and adopted in many firms. The importance of EIS is now increasing with the need of accessing data from many sources under the high-velocity environment. Although many of the Korean firms are now in the process of introducing EIS with the faster growing economy, most research has been conducted on the use of EIS in Western firms. This study aims to explore the current state of EIS usage in Korean firms and make a comparison with that of the United States. Based upon Watson et al. (1992), a questionnaire instrument was constructed. The questionnaires were distributed to 136 Korean companies that were reported to use EIS and 50 of the questionnaires returned. We found that there were many differences in the use of EIS between Korea and the United States. This was mainly due to the fact that the use of EIS in Korea was at a very early stage and executives in Korea did not realize and/or understand the benefits from the use of EIS yet

An effective strategy to prevent allopurinol-induced hypersensitivity by HLA typing

Purpose: This study was conducted to evaluate the usefulness of human leukocyte antigen (HLA) typing in preventing allopurinol-induced severe cutaneous adverse reactions (SCARs) through the application of an allopurinol tolerance induction protocol or prescription of other alternative medications in high-risk patients. Methods: HLA typing was performed in patients with chronic renal insufficiency who needed allopurinol. HLA-B*58:01-negative patients were prescribed the usual dose of allopurinol. For HLAB*58:01-positive patients, administration of either allopurinol based on a 28-day tolerance induction protocol or alternative medications was initiated. Hypersensitivity reactions were surveyed for 90 days and compared with the result of a previous retrospective cohort study. Results: Among a total of 401 study subjects, no SCARs were noted in HLA-B*58:01-positive patients with application of the tolerance were any SCARs observed in HLA-B*58:01-negative patients who started allopurinol at the usual dose (n = 355). Compared with the previous retrospective cohort study, a significant reduction in SCARs was observed in HLA-B*58:01-positive patients (0 vs. 18%; P = 0.002). Conclusion: This study shows the usefulness of HLA-B* 58: 01 screening in identifying patients at high risk for the development of allopurinol-induced SCARs and suggests that application of a -tolerance induction protocol or alternative medications could be an effective strategy to prevent allopurinol-induced SCARs in HLAB*58:01-positive patients.N