Effects of Cooperative Learning on Motivation, Learning Strategy Utilization, and Grammar Achievement of English Language Learners in Taiwan

To examine the effects of cooperative learning on EFL students in Taiwan, a 12-week quasi-experimental pretest-posttest comparison group research study was designed. Two college classes (42 students each) in Taiwan participated in the study, one receiving grammar instruction through cooperative learning and the other through whole-class teaching. Three specific research questions guided the study. The first looked at effects of cooperative learning on motivation, the second on out-of-class strategy use, and the third on grammar achievement. Additional exploratory questions examined these results across subgroups within each class as well as the relationships between the dependent variables. Data were collected via learners\u27 pretest and posttest scores on the dependent variables. The data were analyzed with MANCOVAs, one- and two-way ANCOVAs, simple effects, and Pearson correlations. Cooperative learning was found to have large positive effects on motivation and strategy use, and medium-to-large positive effects on grammar achievement. Overall, the findings indicated a consistent pattern in favor of cooperative learning over whole-class instruction in teaching the Taiwanese learners English grammar. The results of the exploratory questions indicated that cooperative learning facilitated motivation and strategy use of learners across all subgroups, but more so with those performing at higher and lower levels. Grammar achievement of learners at higher and lower levels was affected positively. Additional analyses also indicated cooperative learning positively affected learning at higher cognitive levels. Implications for future research and for curriculum and instruction are addressed

A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN

Different Proportions of Huangqi ( Radix Astragali Mongolici

Objective. To study the effect of different proportions of Huangqi (Radix Astragali Mongolici) and Honghua (Flos Carthami) injection on α-glucosidase and α-amylase activity simultaneously. Methods. The injections were prepared according to the standards of the China Food and Drug Administration. The assay for potential α-glucosidase inhibitors was based on the hydrolysis of 4-methylumbelliferyl-α-D-glucopyranoside (4-MUG). The α-amylase EnzChek assay kit was used to determine potential α-amylase inhibitors. Acarbose was the positive control. Results. The half maximal (50%) inhibitory concentration (IC50) of acarbose against α-glucosidase and α-amylase was (1.8±0.4) μg/mL and (227±32) μg/mL, respectively. Honghua showed significant inhibition of α-glucosidase activity compared with Huangqi (P<0.01). Honghua inhibited α-amylase activity, but Huangqi did not. IC50s for α-glucosidase inhibition by mixtures at 10 : 1, 5 : 1, and 2 : 1 were significantly lower than those at the 20 : 1 mixture (P<0.01). α-Amylase inhibition by the 2 : 1 mixture was significantly higher than that by the 20 : 1, 10 : 1, and 5 : 1 mixtures at 500 μg/mL and 1000 μg/mL (P<0.01), with 5 : 1 significantly higher than 20 : 1 and 10 : 1 at 1000 μg/mL (P<0.01). Conclusion. Honghua significantly inhibited α-glucosidase activity compared with Huangqi (P<0.01). For simultaneous inhibition of α-glucosidase and α-amylase activities, the mixtures at 2 : 1 and 5 : 1 exhibited significant effects compared with those at 20 : 1 (P<0.01)

Essays on dynamic portfolio management

Over the last three decades, there has been an increasing interest in the problem of the investor's optimal consumption and portfolio rules. Despite the substantial amount of related literature, there remain many areas for further investigation. The thesis, therefore, addresses a number of important issues relating to the theory and practice of dynamic portfolio strategies. The thesis consists of five essays. The first two essays, Chapters 3 and 4, are concerned with efficient dynamic asset allocation programs under alternative market assumptions. Chapter 3 studies a situation where the simple time-invariant portfolio strategies are efficient and provides a complete characterisation of the strategies using the efficiency arguments. The popularised constant proportion portfolio insurance (CPPI) is embedded as a special case. Chapter 4 relaxes the assumption of a constant interest rate to allow the interest rate to follow a one factor stochastic process. The factor risk premium is then determined in a way that is consistent with the underlying equilibrium. These results are then applied to solve explicitly for an investor's optimal portfolio choice problem under the special case of a Vacisek short rate model and alternative utility functions. The third essay, Chapter 5, relaxes the assumption of a constant equity risk premium to allow the risk premium to vary through time. The evolution of the market risk premium in a representative agent equilibrium (consistent with the Black-Scholes option pricing) is investigated using a unified approach. The presence of dividends and intermediate consumption proves to be the key element that enables us to obtain a stationary economy with decreasing relative risk aversion, a theoretical result that has not be established in the existing literature. The last two essays. Chapters 6 and 7. are concerned with issues of portfolio efficiency and performance measurement. Chapter 6 uses the result from Chapter 5 that, without dividends and intermediate consumption, the market risk premium must satisfy the Burgers' equation, and applies Dybvig's payoff distribution pricing model to measure the inefficiency costs incurred when this condition is violated. The numerical results show that the degree of inefficiency is not very significant, at least for the cases which we postulate, but the findings also reassure negative result predicted from the model. Finally, Chapter 7 proposes a new utility based performance measure that can be applied in the ex-post evaluation of dynamic portfolio strategies. We construct a contingent claim estimation approach to estimate the nearest efficient strategy from a single realisation and then quantify the opportunity cost resulting from the departure of the observed strategy from the nearest efficient one. The numerical examples show that the technique is remarkably robust

Identification-method research for open-source software ecosystems

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework

Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis

Aiming at solving large-scale learning problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the learning problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. This is particularly true in a heterogeneous network where the computing nodes experience different computation and communication delays. In this paper, we propose an asynchronous distributed ADMM (AD-AMM) which can effectively improve the time efficiency of distributed optimization. Our main interest lies in analyzing the convergence conditions of the AD-ADMM, under the popular partially asynchronous model, which is defined based on a maximum tolerable delay of the network. Specifically, by considering general and possibly non-convex cost functions, we show that the AD-ADMM is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points as long as the algorithm parameters are chosen appropriately according to the network delay. We further illustrate that the asynchrony of the ADMM has to be handled with care, as slightly modifying the implementation of the AD-ADMM can jeopardize the algorithm convergence, even under a standard convex setting.Comment: 37 page