An Analysis of the Application of Cognitive Linguistics in English Vocabulary Teaching

This paper aims to analyze the application strategies of Cognitive Linguistics in English vocabulary teaching. In the 1970s and 1980s, Cognitive Linguistics emerged as a prominent theoretical framework, providing new theoretical support for English teaching. This paper first introduces the basic concepts and principles of Cognitive Linguistics. Then, it discusses how Cognitive Linguistics can be applied to promote vocabulary acquisition, including the use of prototype, conceptual metaphor and image schema. Based on the literature review and analysis, this paper further examines the significance of context and collocation in vocabulary learning. Finally, it summarizes the advantages and challenges of the application of Cognitive Linguistics in English vocabulary teaching. This paper highlights the significant role of Cognitive Linguistics in vocabulary teaching and provides new insights and methods for future English teaching

Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to construct the temporarily changed drivable area and occupancy grid map (OGM) based on crowdsourcing trajectories. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.Comment: Presented at TRBAM. Journal version in progres

非貴金属系の多金属電気化学触媒の開発及び水分解反応への応用

九州工業大学博士学位論文（要旨）学位記番号：生工博甲第445号 学位授与年月日：令和4年9月26

Web robot detection using supervised learning algorithms

Web robots or Web crawlers have become the main source of Web traffic. Although some bots perform well, such as search engines, other bots can perform DDoS attacks, posing a huge threat to websites. The project aims to develop an offline system that can effectively detect malicious web robots, which is not only conducive to network traffic cleaning, but also conducive to improving the network security of IoT systems and services. A comprehensive literature review for the years 2010-2019 was conducted to identify the research gap. The key contributions of the research are: 1) it provided a systematic methodology to address the web robot detection problem based on the log file from industrial company; 2) it provided an approach of feature engineering, thus overcoming the challenge of curse of dimensionality; 3) It made a big progress in the accuracy of off-line web robot detection through a holistic study on the three types of machine learning techniques based on real data from industry. Three algorithms based on Keras sequential model, random forest, and SVM, were developed with python to detect web robots from human visitors on the TensorFlow 2.0 platform. Experimental results suggested that random forest obtained the best performance in accuracy and training time...[cont.]Manufacturin

Incremental Neural Implicit Representation with Uncertainty-Filtered Knowledge Distillation

Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. However, they suffer from the catastrophic forgetting problem when continuously learning from streaming data without revisiting the previously seen data. This limitation prohibits the application of existing NIRs to scenarios where images come in sequentially. In view of this, we explore the task of incremental learning for NIRs in this work. We design a student-teacher framework to mitigate the catastrophic forgetting problem. Specifically, we iterate the process of using the student as the teacher at the end of each time step and let the teacher guide the training of the student in the next step. As a result, the student network is able to learn new information from the streaming data and retain old knowledge from the teacher network simultaneously. Although intuitive, naively applying the student-teacher pipeline does not work well in our task. Not all information from the teacher network is helpful since it is only trained with the old data. To alleviate this problem, we further introduce a random inquirer and an uncertainty-based filter to filter useful information. Our proposed method is general and thus can be adapted to different implicit representations such as neural radiance field (NeRF) and neural SDF. Extensive experimental results for both 3D reconstruction and novel view synthesis demonstrate the effectiveness of our approach compared to different baselines