Activity Engagement: Perspectives from Nursing Home Residents with Dementia

Engagement in social and leisure activities is an indicator of quality of life and well-being in nursing homes. There are few studies in which nursing home residents with dementia self-reported their experiences in activity engagement. This qualitative study describes types of current activity involvement and barriers to activities as perceived by nursing home residents with dementia. Thirty-one residents participated in short, open-ended interviews and six in in-depth interviews. Thematic content analysis showed that participants primarily depended on activities organized by their nursing homes. Few participants engaged in self-directed activities such as walking, visiting other residents and family members, and attending church services. Many residents felt they had limited opportunities and motivation for activities. They missed past hobbies greatly but could not continue them due to lack of accommodation and limitation in physical function. Environmental factors, along with a fixed activity schedule, further prevented them from engaging in activities. Residents with dementia should be invited to participate in activity planning and have necessary assistance and accommodation in order to engage in activities that matter to them. Based on the findings, a checklist for individualizing and evaluating activities for persons with dementia is detailed.     

The change of planned happenstance skills and its association with career-related variables during school-to-work transition

The current study examined how planned happenstance skills (i.e., curiosity, flexibility, persistence, optimism, and risk-taking) changed during school-to-work transition and how career-related variables were associated with the initial levels and change rates of planned happenstance skills. In a sample of 307 South Korean college students, all planned happenstance skills except for risk-taking decreased over time. Furthermore, some of the career-related variables were associated with the initial level and the change rates of planned happenstance skills. In sum, the current study suggests that utilizing planned happenstance skills may be a worthwhile pursuit during school-to-work transition.     

WSVR-based fuzzy neural network with annealing robust algorithm for system identification

This paper proposes a fuzzy neural network (FNN) based on wavelet support vector regression (WSVR) approach for system identification, in which an annealing robust learning algorithm (ARLA) is adopted to adjust the parameters of the WSVR-based FNN (WSVR-FNN). In the WSVR-FNN, first, the WSVR method with a wavelet kernel function is used to determine the number of fuzzy rules and the initial parameters of FNN. After initialization, the adjustment for the parameters of FNNs is performed by the ARLA. Combining the self-learning ability of neural networks, the compact support of wavelet functions, the adaptive ability of fuzzy logic, and the robust learning capability of ARLA, the proposed FNN has the superiority among the several existed FNNs. To demonstrate the performance of the WSVR-FNN, two nonlinear dynamic plants and a chaotic system taken from the extant literature are considered to illustrate the system identification. From the simulation results, it shows that the proposed WSVR-FNN has the superiority over several presented FNNs even the number of training parameters is considerably small.     

Designing a business model for financial products for cultural heritage in the Korean market

There are many contingent valuation (CV) studies to estimate the economic benefits of cultural heritage, but few provided advice on the design of financial products for cultural heritage assets. This paper conducted conjoint and willingness-to-pay (WTP) analyses for providing the design implication of these financial products. We calculated WTP for each attribute. The result of conjoint analysis shows that people prefer a short period of investment, high-expected rate of return and small amount of money invested. Only the amount invested and the rate of return on investment are significant to estimate WTP for these financial products.     

Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.     

A group recommendation system for online communities

Online communities are virtual spaces over the Internet in which a group of people with similar interests or purposes interact with others and share information. To support group activities in online communities, a group recommendation procedure is needed. Though there have been attempts to establish group recommendation, they focus on off-line environments. Further, aggregating individuals’ preferences into a group preference or merging individual recommendations into group recommendations—an essential component of group recommendation—often results in dissatisfaction of a small number of group members while satisfying the majority. To support group activities in online communities, this paper proposes an improved group recommendation procedure that improves not only the group recommendation effectiveness but also the satisfaction of individual group members. It consists of two phases. The first phase was to generate a recommendation set for a group using the typical collaborative filtering method that most existing group recommendation systems utilize. The second phase was to remove irrelevant items from the recommendation set in order to improve satisfaction of individual members’ preferences. We built a prototype system and performed experiments. Our experiment results showed that the proposed system has consistently higher precision and individual members are more satisfied.     

Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking

In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples.Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and Rocchio's technique.Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only.