Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic. This article focuses on a more realistic UDA scenario, i.e., partial domain adaptation (PDA), where the target label space is subsumed to the source label space. In the PDA scenario, the source outliers that are absent in the target domain may be wrongly matched to the target domain (technically named negative transfer), leading to performance degradation of UDA methods. This article proposes a novel target-domain-specific classifier learning-based domain adaptation (TSCDA) method. TSCDA presents a soft-weighed maximum mean discrepancy criterion to partially align feature distributions and alleviate negative transfer. Also, it learns a target-specific classifier for the target domain with pseudolabels and multiple auxiliary classifiers to further address the classifier shift. A module named peers-assisted learning is used to minimize the prediction difference between multiple target-specific classifiers, which makes the classifiers more discriminant for the target domain. Extensive experiments conducted on three PDA benchmark data sets show that TSCDA outperforms other state-of-the-art methods with a large margin, e.g., 4% and 5.6% averagely on Office-31 and Office-Home, respectively.

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset's performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system.

We propose a short-term sparse portfolio optimization (SSPO) system based on alternating direction method of multipliers (ADMM). Although some existing strategies have also exploited sparsity, they either constrain the quantity of the portfolio change or aim at the long-term portfolio optimization. Very few of them are dedicated to constructing sparse portfolios for the short-term portfolio optimization, which will be complemented by the proposed SSPO. SSPO concentrates wealth on a small proportion of assets that have good increasing potential according to some empirical financial principles, so as to maximize the cumulative wealth for the whole investment. We also propose a solving algorithm based on ADMM to handle the $\ell^1$-regularization term and the self-financing constraint simultaneously. As a significant improvement in the proposed ADMM, we have proven that its augmented Lagrangian has a saddle point, which is the foundation of the iterative formulae of ADMM but is seldom addressed by other sparsity strategies. Extensive experiments on $5$ benchmark data sets from real-world stock markets show that SSPO outperforms other state-of-the-art systems in thorough evaluations, withstands reasonable transaction costs and runs fast. Thus it is suitable for real-world financial environments.

Carried out during a semester-long drama class, this research aimed to scrutinize the effects of e-concept mapping via LMS on English majors' (N=38) analytical skills presented in their PowerPoint (PPT) slide writing in Taiwan. Students were instructed to follow the concept mapping agenda via university learning management system (LMS), idea webbing, spider mapping, and network tree making to read and analyze the dramatic texts and to present their analysis on PPT slides. Thus, analytical reading input (e-concept mapping via LMS) was expected to lead an EFL (English as a foreign language) writing output (PPT slide writing). Students were pre-tested, summative assessed and post-tested using in-class PPT presentations. Results confirmed that analytical reading with e-concept mapping strategies could solve EFL students' PPT slide writing problem and improve their performance and the attainment of the task shares via LMS. The research offered a different perspective on teaching EFL writing with a specific focus on analytical reading to writing and also implied the important consequence on analytical reading to analytical writing for future research.

In the context of economic level sustaining its ascent curve, living and education are upgrading at every moment. Medical facilities have increased dramatically and competitions among hospitals become more and more intensive. Therefore, people require better medical services. In order to promote the competitiveness, hospital's services must be aimed to patients' needs. Also, hospitals should adjust the type of medical treatment to satisfy patients. However, there are many aspects of medical service qualities. Hospitals have to concentrate on important factors of Medical Service Quality and improve the customer satisfaction. The study surveyed with questionnaire 185 patients who had been consulted the Medical Centers in central Taiwan. Data of Quality Function Deployment were collected. The study then used the GRA (Grey Relational Analysis) method to analyze the QFD data in order to rank the Medical Service Quality Factors. The results indicate that the five most important factors of Medical Service Quality are: nurse's service, hospital construction, hospital personnel's professionalism, hospital's management system, staff service quality. In addition, the study also compared with conventional methods, quality attribute ranking. The rankings of factors are very alike. The findings show that GRA method is not only suitable for the analysis of QFD data, but also able to complement the limitation of conventional method. Keywords: Grey Relational Method, QFD, Medical Service Quality, Quality attribute ranking.