Aprendizagem da cultura chinesa e aquisição de caracteres

Dissertação de mestrado em Estudos Interculturais Português / Chinês: Tradução, Formação e Comunicação EmpresarialA língua e a cultura complementam-se mutuamente. São dois elementos inseparáveis; a língua reflete a cultura de um país e a cultura encarna em si mesma a evolução linguística. Os caracteres, elemento base do chinês com características únicas, refletem diretamente na sua estrutura não só a evolução cultural e estética chinesa, mas também o pensamento, crenças filosóficas e as várias vertentes culturais da sociedade chinesa. Numa análise global do ensino de chinês como língua estrangeira, numa fase inicial o docente deve cativar o aluno com alguns elementos culturais de modo a estimular o seu interesse pela aprendizagem da língua; num nível intermédio a cultura e a língua já devem estar interligadas, e serem utilizadas em conjunto para uma evolução contínua; num nível avançado a cultura torna-se um alicerce para o aluno compreender fenómenos linguísticos, expressar um estilo de linguagem apropriada ou, numa meta mais elevada, interpretar corretamente conotações culturais.Language and culture complement each other. They are two inseparable elements; language reflects the culture of a country and culture embodies within itself the linguistic evolution. Chinese characters, perceived as a key component in Chinese language, reflect within themselves not only the cultural and aesthetics evolution of China, but also, its cultural mentality philosophic convictions and the multifaceted cultural characteristics of Chinese people. Making a global analysis of the Teaching of Chinese as a Foreign Language, one may conclude that, in its initial stages, teachers should use cultural elements as a way of motivating their students into a more comprehensive study of the language. At an intermediate level, culture and language teaching should already be used mutually in order to facilitate the learning evolution of the students. At an advanced level, culture becomes a background knowledge for the students to understand linguistic phenomena, to express in an appropriate language style, or even to interpret correctly cultural connotations.语⾔言与⽂文化两者相辅相成，互为⼀一体，不可分割，语⾔言反映⽂文化现象，⽂文化 包含语⾔言自身的发展。⽽而汉字，作为汉语⾔言的基础部件及特有元素，其结构的演化 与发展直接反映了中华⽂文明及美学的时代特征，⽽而它的运用也从侧面体现了中国⼈人 的精神面貌、哲学信仰等多重⽂文化内涵。因此，纵观对外汉语教学的全程，初级阶 段的⽂文化教学是语⾔言本体学习的辅助⼿手段，旨在帮助学⽣生加深理解，提⾼高学习兴趣； 中级阶段⽂文化与语⾔言学习互为依靠，相互作用；⾼高级阶段⽂文化自觉、不自觉地成为 学习的主体内容，语⾔言则成为理解、表达⽂文化现象，阐释⽂文化内涵的⼯工具

Mean change-point model for aero-engine component faults

Recognizing aero-engine component faults is important in prognostics and health management research, particularly in engine monitoring systems and condition-based maintenance. The former primarily concentrates on recognizing engine working parameters and component performance, and neglects quantitative changes in component faults. Taking lubricating oil consumption as an example, quantitative changes in component faults are analyzed using a mean change-point model based on change-point theory. The change-point stage is presented through the minimum variance algorithm. The change point corresponding to the failure mode is tested using exhaust electrostatic data from a turbojet engine life span experiment to verify the validity and feasibility of the theory

Deformable Object Tracking with Gated Fusion

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods

Elastomeric cellular structure enhanced by compressible liquid filler

Elastomeric cellular structures provide a promising solution for energy absorption. Their flexible and resilient nature is particularly relevant to protection of human bodies. Herein we develop an elastomeric cellular structure filled with nanoporous material functionalized (NMF) liquid. Due to the nanoscale infiltration in NMF liquid and its interaction with cell walls, the cellular structure has a much enhanced mechanical performance, in terms of loading capacity and energy absorption density. Moreover, it is validated that the structure is highly compressible and self-restoring. Its hyper-viscoelastic characteristics are elucidated

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current node drop pooling methods usually keep the top-k nodes according to their significance scores, which ignore the graph diversity in terms of the node features and the graph structures, thus resulting in suboptimal graph-level representations. To address the aforementioned issue, we propose a novel plug-and-play score scheme and refer to it as MID, which consists of a \textbf{M}ultidimensional score space with two operations, \textit{i.e.}, fl\textbf{I}pscore and \textbf{D}ropscore. Specifically, the multidimensional score space depicts the significance of nodes through multiple criteria; the flipscore encourages the maintenance of dissimilar node features; and the dropscore forces the model to notice diverse graph structures instead of being stuck in significant local structures. To evaluate the effectiveness of our proposed MID, we perform extensive experiments by applying it to a wide variety of recent node drop pooling methods, including TopKPool, SAGPool, GSAPool, and ASAP. Specifically, the proposed MID can efficiently and consistently achieve about 2.8\% average improvements over the above four methods on seventeen real-world graph classification datasets, including four social datasets (IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, and COLLAB), and thirteen biochemical datasets (D\&D, PROTEINS, NCI1, MUTAG, PTC-MR, NCI109, ENZYMES, MUTAGENICITY, FRANKENSTEIN, HIV, BBBP, TOXCAST, and TOX21). Code is available at~\url{https://github.com/whuchuang/mid}.Comment: 14 pages, 14 figure

Free-Form Composition Networks for Egocentric Action Recognition

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognition from a compositional generalization perspective. To tackle this problem, we propose a free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos. First, we use a graph to capture the spatial-temporal relations among different hand/object instances in each action video. We thus decompose each action into a set of verb and preposition spatial-temporal representations using the edge features in the graph. The temporal decomposition extracts verb and preposition representations from different video frames, while the spatial decomposition adaptively learns verb and preposition representations from action-related instances in each frame. With these spatial-temporal representations of verbs and prepositions, we can compose new samples for those rare classes in a free-form manner, which is not restricted to a rigid form of a verb and a noun. The proposed FFCN can directly generate new training data samples for rare classes, hence significantly improve action recognition performance. We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack

We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers. Despite phenomenal progress on general adversarial attacks, few works have investigated the vulnerability and attack specifically for QA models. In this work, we first explore the biases in the existing models and discover that they mainly rely on keyword matching between the question and context, and ignore the relevant contextual relations for answer prediction. Based on two biases above, TASA attacks the target model in two folds: (1) lowering the model's confidence on the gold answer with a perturbed answer sentence; (2) misguiding the model towards a wrong answer with a distracting answer sentence. Equipped with designed beam search and filtering methods, TASA can generate more effective attacks than existing textual attack methods while sustaining the quality of contexts, in extensive experiments on five QA datasets and human evaluations.Comment: Accepted by EMNLP 2022 (long), 9 pages main + 2 pages references + 7 pages appendi