America and China: Cultural differences in online learning motivation

There are learning motivation differences among students from different countries. And differences between American and Korean online learners were identified. This study was to identify whether American and Chinese online learners\u27 motivation differed and what were the learner characteristics that affected the motivation of online learners. This study was conducted in May-June, 2005, West Virginia University, the United States of America, and Fudan University, China. Findings revealed that there was a significant difference in learning motivation between the U.S. and Chinese online learners. And there were some learner characteristics affected the learning motivation of online learners as well, such as gender and employment status. Future research could explore the scope of cultural contexts on a more multi-leveled area

Connecting WV fee-fishing businesses with the larger tourism market through the development of tourism package

There is substantial demand for fishing packages in West Virginia. Fee-fishing businesses in West Virginia are often characterized as small businesses, and they could benefit from connecting with larger travel packages that are more likely to attract out-of-state anglers. The objectives are: (1) identify mini-market segments based on fee-fishing experiences; (2) examine how fee-fishing mini-markets can better connect with the larger outdoor recreation markets; and (3) to use this information to identify gaps in recreational offerings and develop tourism packages in a West Virginia test market. Six fee-fishing mini-markets were identified. Regression analysis results indicate that it is possible to develop fishing packages that include other tourism activities through partnerships with West Virginia State Parks. A gap analysis was conducted. The development of additional tourism offerings and public/private partnerships can help address the gaps identified in the weaker markets

Bus allocation to short-turning and interlining lines.

We propose injecting flexibility into public transport service planning by introducing a demand driven method for generating and assigning buses to short-turning and interlining services. This study formulates, solves and applies the problem of assigning vehicles to the lines of a bus network subject to the dual objective of (a) improving the passenger waiting times at stops and (b) reducing the operational costs. At first, the vehicle allocation problem is expanded with the explicit consideration of interlining and short-turning lines that provide greater operational flexibility. The paper introduces a rule-based approach for generating interlining and short-turning lines that are considered as "virtual lines" because some of them might remain inactive if their operation does not improve the vehicle allocation solution. The bus allocation problem to existing and virtual lines is modeled as a combinatorial, multi-objective optimization problem and is solved with a Genetic Algorithm (GA) meta-heuristic that can return improved solutions by avoiding the exhaustive exploration of a combinatorial solution space. The vehicle allocation to existing and virtual lines is applied to the bus network of The Hague with the use of Automated Fare Collection (AFC) data from 24 weekdays and General Transit Feed Specification (GTFS) data. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs without adding a large number of short-turning and interlining line options that could impede the practicality of the bus services

Using graphical representation of user interfaces as visual references

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-133).My thesis investigates using a graphical representation of user interfaces - screenshots - as a direct visual reference to support various kinds of applications. We have built several systems to demonstrate and validate this idea in domains like searching documentation, GUI automation and testing, and cross-device information migration. In particular, Sikuli Search enables users to search documentation using screenshots of GUI elements instead of keywords. Sikuli Script enables users to programmatically control GUIs without support from the underlying applications. Sikuli Test lets GUI developers and testers create test scripts without coding. Deep Shot introduces a framework and interaction techniques to migrate work states across heterogeneous devices in one action, taking a picture. We also discuss challenges inherent in screenshot-based interactions and propose potential solutions and directions of future research.by Tsung-Hsiang Chang.Ph.D

Range-only Target Localisation using Geometrically Constrained Optimisation

The problem of optimal range-only localisation of a single target is of considerable interest in two-dimensional search radar networking. For coping with this problem, a range-only target localisation method using synchronous measurements from radars is presented in the real ellipsoidal earth model. In the relevant radar localisation scenario, geometric relationships between the target and three radars were formed. A set of localisation equations was derived on range error in such a scenario. Using these equations, the localisation task has been formulated as a nonlinear weighted least squares problem that can be performed using the Levenberg- Marquardt (LM) algorithm to provide the optimal estimate of the target’s position. To avoid the double value solutions and to accelerate the convergence speed for the LM algorithm, the initial value was approximately given according to observations from two radars. In addition, the relative validity has been defined to evaluate the performance of the proposed method. The performance of the proposed approach is evaluated using two simulation experiments and a real-test experiment, and it has been found to possess higher localisation accuracy than the other conventional method.Defence Science Journal, Vol. 65, No. 1, January 2015, pp.70-76, DOI:http://dx.doi.org/10.14429/dsj.65.547

A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction

Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications