A Study on Chinese EFL Learning of English Pronunciation from the Perspective of Aesthetic Linguistics

English phonetic learning, as the beginning of learning a foreign language, is of great importance in EFL learning. However, the present Chinese EFL learners’ phonetic learning is not satisfactory. Based on theories of aesthetic linguistics, this paper analyzes the aesthetic attributes of English pronunciation and intonation, including the beauty of sonority, rhyme, rhythm, intonation, and succession. And then pedagogical implications are proposed about how to raise EFL learners’ aesthetic consciousness and creation of English pronunciation

A Study on Chinese EFL Learners’ Phonetic Obstacles to Listening Comprehension

Perceiving and identifying speech sounds are greatly important in listening comprehension. This paper aims to analyze Chinese EFL learners’ Phonetic Obstacles to listening comprehension in terms of individual segments, sound change in fluent speech, stress, rhythm, intonation and English varieties. Based on the analysis, suggestions are proposed about how to overcome phonetic obstacles to improve Chinese EFL learners’ ability of listening, including increasing phonetic teaching in listening, memorizing the sound form of words and designing tasks of dictation

A backbone-based communication scheduling scheme for wireless sensor networks

Prolonging network lifetime and retaining maximum communication fidelity are important to many applications of ad-hoc wireless sensor networks. Many energy-efficient communication protocols have been proposed to allow as many sensors as possible to be in idling. Typically, these techniques reduce energy consumption by minimizing the number of transmission packets and the size of each packet. However, recent researches have shown that energy consumed by the sensors in idling state is not negligible. In this research, we address this problem with a novel Backbone-based Communication Scheduling (BCS) technique. This scheme reduces the idling energy dissipation by keeping only a small set of sensors active at any time and leaving the rest of them in sleeping. The active sensors form a communication backbone that maintains the communication fidelity of the entire network. The backbone nodes are rotated with a highly efficient backbone election algorithm to balance the energy consumption of the sensors in the whole network. Our simulations results show that the proposed scheme can significantly extend the network lifetime without compromising the communication fidelity

Regularized Regression Problem in hyper-RKHS for Learning Kernels

This paper generalizes the two-stage kernel learning framework, illustrates its utility for kernel learning and out-of-sample extensions, and proves {asymptotic} convergence results for the introduced kernel learning model. Algorithmically, we extend target alignment by hyper-kernels in the two-stage kernel learning framework. The associated kernel learning task is formulated as a regression problem in a hyper-reproducing kernel Hilbert space (hyper-RKHS), i.e., learning on the space of kernels itself. To solve this problem, we present two regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) in the hyper-RKHS. By doing so, it provides significant model flexibility for kernel learning with outstanding performance in real-world applications. Specifically, our kernel learning framework is general, that is, the learned underlying kernel can be positive definite or indefinite, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of these learning algorithms in the hyper-RKHS and derive the learning rates. Different from the traditional approximation analysis in RKHS, our analyses need to consider the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. To the best of our knowledge, this is the first work in learning theory to study the approximation performance of regularized regression problem in hyper-RKHS.Comment: 25 pages, 3 figure

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.Comment: has been published on IEEE TI

Data Practices in Digital History

This paper presents an exploratory research project that investigates data practices in digital history research. Emerging from the 1950s and ‘60s in the United States, digital history remains a charged topic among historians, requiring a new research paradigm that includes new concepts and methodologies, an intensive degree of interdisciplinary, inter-institutional, and international collaboration, and experimental forms of research sharing, publishing, and evaluation. Using mixed methods of interviews and questionnaire, we identified data challenges in digital history research practices from three perspectives: ontology (e.g., the notion of data in historical research); workflow (e.g., data collection, processing, preservation, presentation and sharing); and challenges. Extending from the results, we also provide a critical discussion of the state-of-art in digital history research, particularly in respect of metadata, data sharing, digital history training, collaboration, as well as the transformation of librarians’ roles in digital history projects. We conclude with provisional recommendations of better data practices for participants in digital history, from the perspective of library and information science