Trialing project-based learning in a new EAP ESP course: A collaborative reflective practice of three college English teachers

Currently in many Chinese universities, the traditional College English course is facing the risk of being ‘marginalized’, replaced or even removed, and many hours previously allocated to the course are now being taken by EAP or ESP. At X University in northern China, a curriculum reform as such is taking place, as a result of which a new course has been created called ‘xue ke’ English. Despite the fact that ‘xue ke’ means subject literally, the course designer has made it clear that subject content is not the target, nor is the course the same as EAP or ESP. This curriculum initiative, while possibly having been justified with a rationale of some kind (e.g. to meet with changing social and/or academic needs of students and/or institutions), this is posing a great challenge for, as well as considerable pressure on, a number of College English teachers who have taught this single course for almost their entire teaching career. In such a context, three teachers formed a peer support group in Semester One this year, to work collaboratively co-tackling the challenge, and they chose Project-Based Learning (PBL) for the new course. This presentation will report on the implementation of this project, including the overall designing, operational procedure, and the teachers’ reflections. Based on discussion, pre-agreement was reached on the purpose and manner of collaboration as offering peer support for more effective teaching and learning and fulfilling and pleasant professional development. A WeChat group was set up as the chief platform for messaging, idea-sharing, and resource-exchanging. Physical meetings were supplementary, with sound agenda but flexible time, and venues. Mosoteach cloud class (lan mo yun ban ke) was established as a tool for virtual learning, employed both in and after class. Discussions were held at the beginning of the semester which determined only brief outlines for PBL implementation and allowed space for everyone to autonomously explore in their own way. Constant further discussions followed, which generated a great deal of opportunities for peer learning and lesson plan modifications. A reflective journal, in a greater or lesser detailed manner, was also kept by each teacher to record the journey of the collaboration. At the end of the semester, it was commonly recognized that, although challenges existed, the collaboration was overall a success and they were all willing to continue with it and endeavor to refine it to be a more professional and productive approach

Research on the Application of Intelligent Evaluation and Teacher-student Cooperation Assessment System in Teaching English Writing

Low efficiency in teaching and time-consuming in writing evaluation are two big problems for college English teachers. Therefore, it is necessary to create a new teaching model to solve these problems existing in traditional classroom-based teaching. This research adopts the research methods of test comparison before and after the students’ composition experiment, questionnaire and semi-open interviews. Empirical research on a new teaching model that integrates the intelligent composition review and reform system represented by Piangai.com and the collaborative evaluation of teachers and students is conducted. The research results show that the new writing teaching model improves the quality of students’ writing, promotes students’ learning initiative, and enhances students’ writing self-efficacy. This writing teaching model provides ideas for solving the problem of time-consuming and inefficient English writing teaching in large classes

Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

Evaluating Fuel Consumption for Continuous Descent Approach Based on QAR Data

Fuel savings are a significant aspect for evaluating the current and future technologies of civil aviation. Continuous-Descent Approach (CDA), as a representative of new concepts, requires a method for evaluating its fuel benefits. However, because of unavailability of the practical operational data, it is difficult to validate whether the previous fuel consumption mechanisms are suitable. This paper presents a unique method for quantifying potential fuel benefits. This permits an easy evaluation for the new procedures without modelling before implementing field tests. The proposed method is detailed in this paper. It derives from the inherent mechanical characteristic of aircraft engine, and utilizes historical flight data, rather than modelling, to predict fuel flow rates by matching flight conditions from Quick Access Recorder (QAR) data. The result has been shown to predict fuel consumption for conventional descent with the deviation of ±0.73%. To validate such method, a case study for our designed CDA procedure is presented. Fuel consumptions in baseline scenarios are estimated to analyse the variable impacts on fuel consumption. The estimated fuel benefits are consistent with the results in the previous field tests. This analysis helps support Air Traffic Management decisions on eventual field test by reducing the validation time and cost.</p

Chemostratigraphy of the uppermost Cambrian at the Ordovician GSSP

Chemostratigraphy is an important tool for correlating layered sedimentary rock successions. Preserved/near primary carbon isotope signatures in marine carbonates can provide high-resolution profiles for sedimentary sequences supplementing the need for distinguishing fossils from different depositional environments and those lacking fossil materials. The Global Boundary Stratotype Section and Point (GSSP) of the Cambrian‒Ordovician boundary is located at Green Point in the Green Point Formation of the Cow Head Group in western Newfoundland, Canada. To reconstruct a continuous and high-resolution chemostratigraphy from the Cambrian‒Ordovician boundary to the Furongian Series Stage 10, we included the δ13C results of the Green Point Formation covering the Ordovician GSSP interval (Azmy et al., 2014). The Green Point Formation through the base of Ordovician GSSP consists of alternating dark gray to black shale and thin ribbon limestone rhythmites, with few fossils. The samples are micritic limestone, dolomitic limestone, and dolostone. They were determined to be in primary to near-primary condition based on multiple screening tests. Cathodoluminescence screening reveals dull to bright luminescence of the samples indicative of good preservation for many of them. The δ13Ccarb and δ18O values of the Green Point carbonates range from -6.44‰ to +0.33‰ (VPDB) and from -8.63‰ to -5.67‰ (VPDB), respectively, with poor correlation. Mn/Sr ratios range from 0.63 to 9.82, with no correlation to δ13Ccarb, but with ratios supporting the near primary nature of the δ13C values. Carbon isotope compositions of the Green Point Formation below the Ordovician GSSP fluctuate but remaine essentially invariantly negative. The δ13C values reveal a negative excursion at and below the Cambrian‒Ordovician boundary, which may correlate with the Top of Cambrian Carbon Isotope Excursion (TOCE) and its significant negative excursion. A nadir of -6.44 ‰ at the base of the Eoconodontus conodont zone marks the proposed GSSP for the base of the Furongian Series Stage 10. The lower excursion may be correlated with the Hellnmaria-Red Tops Boundary (HERB) carbon isotope excursion found in sequences in the United States of America, Australia, and north China. Without an adequate record of conodonts, high-resolution chemostratigraphic trends of carbon isotope compositions facilitate the correlation of intercontinental and intracontinental sequences.This research was financially supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC 7961-215) to Uwe Brand

Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across different cameras. Current part-level feature learning methods typically detect vehicle parts via uniform division, outside tools, or attention modeling. However, such part features often require expensive additional annotations and cause sub-optimal performance in case of unreliable part mask predictions. In this paper, we propose a weakly-supervised Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel recalibration and cluster-based mask generation without vehicle part supervisory information. Secondly, PMNet leverages teacher-student guided learning to distill vehicle part-specific features from PANet and performs multi-scale global-part feature extraction. During inference, PMNet can adaptively extract discriminative part features without part localization by PANet, preventing unstable part mask predictions. We address this Re-ID issue as a multi-task problem and adopt Homoscedastic Uncertainty to learn the optimal weighing of ID losses. Experiments are conducted on two public benchmarks, showing that our approach outperforms recent methods, which require no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded vehicle Re-ID task and exhibits good generalization ability.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Frequency response of an underwater acoustic focusing composite lens

Acoustic lenses composed of metamaterials are used as highly anisotropic subwavelength media and have broad applications in a wide range of industrial areas. As reported in recent research, an acoustic lens composed of a cross-shaped structure can achieve high-intensity 3D focusing in an underwater system. However, the operating characteristics of this lens at different frequencies have not been studied in detail until now. In this work, we studied the focusing performance of a particular acoustic lens at different working frequencies, and the band structure, wave intensity distribution, reflection and transmission coefficients, and refractive index of a unit cell were investigated, as well as the characteristics of the acoustic lens through a simulation and experiment. Errors were minimized in the experiments through reasonable design, and we found that although the wave intensity of a single unit cell decreased as the frequency increased, in the acoustic lens, the intensity of the sound field at its focal point increased with the frequency. The present research provides an improved method for designing acoustic lenses with different working frequencies and can guide nondestructive testing (NDT) and biomedical treatment