Event-driven displays for manipulator control

The problem of constructing event-related information displays from multidimensional data generated by proximity, force-torque and tactile sensors integrated with the terminal device of a remotely controlled manipulator is considered. Event-driven displays are constructed by using appropriate algorithms acting on sensory data in real time. Event-driven information displays lessen the operator's workload and improve control performance. The paper describes and discusses several event-driven display examples that were implemented in the JPL teleoperator project, including a brief outline of the data handling system which drives the graphics display in real time. The paper concludes with a discussion of future plans to integrate event-driven displays with visual (TV) information

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approache

Influence of Context on Item Parameters in Forced-Choice Personality Assessments

A fundamental assumption in computerized adaptive testing (CAT) is that item parameters are invariant with respect to context – items surrounding the administered item. This assumption, however, may not hold in forced-choice (FC) assessments, where explicit comparisons are made between items included in the same block. We empirically examined the influence of context on item parameters by comparing parameter estimates from two FC instruments. The first instrument was compiled of blocks of three items, whereas in the second, the context was manipulated by adding one item to each block, resulting in blocks of four. The item parameter estimates were highly similar. However, a small number of significant deviations were observed, confirming the importance of context when designing adaptive FC assessments. Two patterns of such deviations were identified, and methods to reduce their occurrences in a FC CAT setting were proposed. It was shown that with a small proportion of violations of the parameter invariance assumption, score estimation remained stable

Measuring the effect of customer relationship management (CRM) components on the non financial performance of commercial banks: Egypt case

This paper presents customer relationship management (CRM) components as applied on the Egyptian Commercial Banks, examined from the bankers' point of view. Then, it intends to measure their effect on the level of customer satisfaction and loyalty from the customers’ point of view as examples of the non financial performance measures. The paper is quantitative in nature and consists of two different structured questionnaires using convenience/quota sampling. The first involved 180 employees in order to measure CRM applicability, and the second involved 270 customers to measure the level of customer satisfaction and loyalty and their effect on the Egyptian Commercial Banks' financial performance The findings show that the selected banks apply CRM components but the level of application differs from one bank to another. The results showed a significant positive relationship between CRM and customer satisfaction in the Egyptian Commercial Banks, when applying them together and not separately. In addition, there is a strong positive effect between customer satisfaction and loyalty which was reflected on the Commercial Banks' financial performance. The findings confirm the importance of studying and implementing CRM to achieve customer loyalty and improve the Egyptian Commercial Banks financial performance. Banks wishing to improve their relationships with customers need to focus on the CRM components to develop relevant and effective marketing strategies and tactics. The paper measures the CRM as a multidimensional construct as applied on the Egyptian Commercial Banks and relate it to the achievement of the ultimate goal of retaining customers to gaining a sustainable competitive advantage and achieve more profits