Achieving Efficient Teacher Talk: A Reflective Analysis of Teacher-student Class Communication

In recent years, there has been an increase in the number of studies on “teacher talk” used in TCSL classrooms. Many of these studies are focused on language interactions between the teacher and the students as well as on the individual functions of “the teachers’ classroom talk.” This article examines the current status of, and problems associated with, teachers’ instructional language, i.e., teacher talk. Based on transcripts of audio recordings of teachers’ instructional language, this study analyzes problems ranging from inaccurate to excessive words in the instructional language and the causes of these problems. This article also validates the study findings by analyzing textual data on teachers’ reflections, and also proposes specific solutions such as simplifying vocabulary and grammar of the instructional language, using auxiliary methods, establishing typical linguistic contexts, and implementing graphics and charts to organize thoughts. Finally, fundamental strategies to achieve “efficient instruction” are made that teachers intentionally enhance their Chinese language competence and teaching abilities, and educational institutions place sufficient emphasis on teacher training and instructional guidance and implement best practices

Design, simulation and experiment of particle dampers attached to a precision instrument in spacecraft

Aiming at attenuating the vibration of a precision instrument in spacecraft, multiple particle dampers are designed and their damping performances are evaluated. Firstly, the vibrating table test for the primary system under sin-swept excitation is conducted to acquire the vibration characteristic. Then enclosures attached to the installing bracket are designed and fabricated elaborately. Using discrete element-finite element (DE-FE) coupling algorithm, the effects of some system parameters (such as: mass ratio, particle material, numbers of dampers and cavity depth) are investigated to optimize the damping capacity of particle dampers. Furthermore, a series of experiments are conducted to verify the performance of particle dampers under dynamic load. The results indicate that the transfer functions of acceleration in Y and Z direction decrease at 22.58 % and 77.38 % respectively, while only 3.1 % mass of the primary system is attached

Matching dependence-related queries in the system dependence graph.

In software maintenance and evolution, it is common that develop-ers want to apply a change to a number of similar places. Due to the size and complexity of the code base, it is challenging for develop-ers to locate all the places that need the change. A main challenge in locating the places that need the change is that, these places share certain common dependence conditions but existing code searching techniques can hardly handle dependence relations satisfactorily. In this paper, we propose a technique that enables developers to make queries involving dependence conditions and textual condi-tions on the system dependence graph of the program. We carried out an empirical evaluation on four searching tasks taken from the development history of two real-world projects. The results of our evaluation indicate that, compared with code-clone detection, our technique is able to locate many required code elements that code-clone detection cannot locate, and compared with text search, our technique is able to effectively reduce false positives without losing any required code elements

Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus

Aspect-based sentiment analysis (ABSA) is a fine-grained and diverse task in natural language processing. Existing deep learning models for ABSA face the challenge of balancing the demand for finer granularity in sentiment analysis with the scarcity of training corpora for such granularity. To address this issue, we propose an enhanced BERT-based model for multi-dimensional aspect target semantic learning. Our model leverages BERT's pre-training and fine-tuning mechanisms, enabling it to capture rich semantic feature parameters. In addition, we propose a complex semantic enhancement mechanism for aspect targets to enrich and optimize fine-grained training corpora. Third, we combine the aspect recognition enhancement mechanism with a CRF model to achieve more robust and accurate entity recognition for aspect targets. Furthermore, we propose an adaptive local attention mechanism learning model to focus on sentiment elements around rich aspect target semantics. Finally, to address the varying contributions of each task in the joint training mechanism, we carefully optimize this training approach, allowing for a mutually beneficial training of multiple tasks. Experimental results on four Chinese and five English datasets demonstrate that our proposed mechanisms and methods effectively improve ABSA models, surpassing some of the latest models in multi-task and single-task scenarios

Identifying Bug Signatures Using Discriminative Graph Mining

Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single state-ment or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In this study, we propose to model software executions with graphs at two levels of granularity: methods and basic blocks. An indi-vidual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted sub

Extracting Paraphrases of Technical Terms from Noisy Parallel Software Corpus

In this paper, we study the problem of extracting technical paraphrases from a parallel software corpus, namely, a collection of duplicate bug reports. Paraphrase acquisition is a fundamental task in the emerging area of text mining for software engineering. Existing paraphrase extraction methods are not entirely suitable here due to the noisy nature of bug reports. We propose a number of techniques to address the noisy data problem. The empirical evaluation shows that our method significantly improves an existing method by up to 58%.

Anything in Any Scene: Photorealistic Video Object Insertion

Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results