Multi-Image Semantic Matching by Mining Consistent Features

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.Comment: CVPR 201

NATURAL LANGUAGE PROCESSING BASED GENERATOR OF TESTING INSTRUMENTS

Natural Language Processing (NLP) is the field of study that focuses on the interactions between human language and computers. By “natural language” we mean a language that is used for everyday communication by humans. Different from programming languages, natural languages are hard to be defined with accurate rules. NLP is developing rapidly and it has been widely used in different industries. Technologies based on NLP are becoming increasingly widespread, for example, Siri or Alexa are intelligent personal assistants using NLP build in an algorithm to communicate with people. “Natural Language Processing Based Generator of Testing Instruments” is a stand-alone program that generates “plausible” multiple-choice selections by analyzing word sense disambiguation and calculating semantic similarity between two natural language entities. The core is Word Sense Disambiguation (WSD), WSD is identifying which sense of a word is used in a sentence when the word has multiple meanings. WSD is considered as an AI-hard problem. The project presents several algorithms to resolve WSD problem and compute semantic similarity, along with experimental results demonstrating their effectiveness

CHINESE LANGUAGE LEARNER'S MOTIVATION, INTENDED EFFORT, AND CONTINUATION OF STUDY

Motivation has been widely recognized as one of the key factors in second language (L2) learning and teaching. Yet very few motivational studies have examined adolescents’ motivation to learn a specific L2 within the framework of the contemporary expectancy-value theory, even less empirical research has been done in the Chinese as a Second Language (CSL) setting. It is unclear whether there are differences between boys’ and girls’ perceptions of expectancies for success, task values, and task difficulty in CSL learning. Furthermore, while most research associates motivation with language proficiency, a limited number of CSL studies have addressed the relations between motivation and motivational behaviors such as intended effort and continuation of study. One important purpose of the present study is to apply expectancy-value theory to develop a reliable and valid CSL Learning Motivation Scale which assesses adolescents’ motivation. Based on the literature review, the results of item examination, and expert feedback, a 34-item CSL Learning Motivation Scale was constructed. I conducted a Principal Component Analysis (PCA) to examine the factor structures of the final 34 items based on responses from the 219 students in Grade 6-12 at secondary schools in Southwestern United States. The results yielded five factors: ability/expectancy-related beliefs, intrinsic value-linguistic interests, intrinsic value-cultural interests, utility/attainment value, and perceived task difficulty. The final 34-item CSL Learning Motivation Scale displayed high internal consistency (α=.92). The reliabilities of the above five factors were .87, .80, .84, .92, and .86, respectively. Furthermore, this study examined if adolescents’ expectancy-value motivation in CSL learning significantly predicted their motivational behaviors. The results of regression analysis demonstrated that expectancy-value constructs explained 64% of the variance in intended effort and 74% of the variance in continuation of study. Specifically, expectancy/ability beliefs, intrinsic value-linguistic interests, utility/attainment value, and task difficulty perceptions significantly predicted students’ intended efforts. Expectancy/ability beliefs, intrinsic value-linguistic interests, and utility/attainment value significantly predicted continuation of study. In addition, this study attempted to explore gender differences in expectancy-value motivation in the CSL setting. MANOVA analyses revealed that gender differences in these motivational constructs were not significant.Psychological, Health, and Learning Sciences, Department o

Eeg experimental study on the infl uence of learning activity design on learning effect

With the rapid development of new media, online learning has become an indispensable form of educational practice all over the world. A large number of studies and practices have shown that the gamifi cation of online learning has improved students’ engagement and attention to a certain extent, but there are still some problems in some aspects. This study intends to use EEG interaction technology to monitor students’ learning situation in real time, and study the infl uence of diff erent learning activity designs on students’ learning effi ciency and continuous learning willingness through the design of diff erent elements of learning activities, such as learning knowledge density design and knowledge quantity design. To further understand the learning effi ciency and continuous learning willingness of students when they participate in diff erent learning activities, propose and verify the relationships and principles between diff erent parameters, and provide scientifi c methods and theoretical basis for the design of gamifi ed education system

5,8-Dibromo-14,15,17,18-tetra­methyl-2,11-dithia­[3.3]paracyclo­phane

In the title mol­ecule [systematic name: 12,15-dibromo-52,53,55,56-tetramethyl-3,7-dithia-1,5(1,4)-dibenzenacyclooctaphane], C20H22Br2S2, the distance between the centroids of the two benzene rings is 3.326 (4) Å, and their mean planes are almost parallel, forming a dihedral angle of 1.05 (7)°. The crystal packing exhibits no inter­molecular contacts shorter than the sum of van der Waals radii