Abstract ：

Recent studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users, the captured evidence belongs to the perspective of individual cognition. However, individuals' cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased information. In this paper, we propose a Dual-viewmodel based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. For collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users to generate global evidence. For individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise an inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm the effectiveness of CICD.

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Abstract ：

Since the COVID-19 pandemic, video live-streaming technologies that ensure learning continuity have evolved rapidly. With the epidemic under control in China, a growing number of colleges classroom teaching will be online live simultaneously. Traditional offline teaching will gradually transform to the combination of online and offline mode. With the demand for the normality high-definition online live-streaming, the traditional live broadcast system faces the problems of limited scalability, poor fault tolerant ability and poor user experience. In this paper, a distributed online live-streaming system cross multiple campuses which based on SRS is designed and realized. Through dynamic distributed push, upstream and downstream traffic separation, and multi-level load balancing mechanism, the system can satisfy the need of the automatic and unattended normal video live-streaming teaching of large-scale classrooms. In this loosely coupled system, all components are distributed on each campus and communicate via message queues and HTPP API. The system has been applied to Xi'an Jiaotong University daily online and offline teaching for 700 classrooms spread over three campuses. © 2021 IEEE.

Keyword ：

Disease control E-learning Multimedia systems User experience Video streaming

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Abstract ：

Aims: Ethanol ingestion affects cognition and emotion, which have been attributed to the dysfunction of specific brain structures. Studies of alcoholic patients and animal models consistently identify reduced hippocampal mass as a key ethanol-induced brain adaptation. This study evaluated how neuroadaptation in the hippocampus (Hip) produced by ethanol contributed to related behavioral deficits in male and female rats. Methods: Effects of acute, short-term and long-term ethanol exposure on the anxiety-like behavior and recognition memory on adult male and female Sprague-Dawley rats were assessed using elevated plus maze test and novel object recognition test, respectively. In addition, in order to investigate the direct effect of ethanol on hippocampal neurons, primary culture of hippocampal neurons was exposed to ethanol (10, 30 and 90 mM; 1, 24 and 48 h), and viability (CCK-8) and morphology (immunocytochemistry) were analyzed at structural levels. Western blot assays were used to assess protein levels of NT3-TrkC-ERK. Results: Acute and short-term ethanol exposure exerted anxiolytic effects, whereas long-term ethanol exposure induced anxiogenic responses in both sexes. Short-term ethanol exposure impaired spatial memory only in female rats, whereas long-term ethanol exposure impaired spatial and recognition memory in both sexes. These behavioral impairments and ethanol-induced loss of hippocampal neurons and decreased cell viability were accompanied by downregulated NT3-TrkC-ERK pathway. Conclusion: These results indicate that NT3-TrkC-ERK signaling in the Hip may play an important role in ethanol-induced structural and behavioral impairments.

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The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of contextin-dependent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present CHASE, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are contextin-dependent, and 28% of SQL queries are easy. We experiment on CHASE with three state-of-the-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that CHASE highlights the challenging problems of XDTS. We believe that CHASE can provide fertile soil for addressing the problems.

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Abstract ：

Classroom interaction is an important part of teaching which can greatly enhance the learning performance of students. However, the following problems still exist in conducting interactive teaching in conventional classroom, such as limited interaction format, lack of enthusiasm and unequal interaction frequency. As a widely adopted interactive tool in online video websites, the bullet screen is instant and convenient with high interactivity, which is extremely popular among the young generation such as university undergraduate students. In this paper, a software platform is designed to collect the bullet screen messages generated by students in classroom and display them instantly together with teaching materials such as lecture slides via projector, which can be later collected for teaching and learning performance analysis. This paper aims to propose a possible solution of the mentioned problems to construct a convenient and efficient interactive classroom to improve the teaching quality. © 2021 IEEE.

Keyword ：

Educational technology Students

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Abstract ：

The no-arbitrage condition is a cornerstone concept in financial market research. However, the arbitrage mechanism that is inherent in the trading process for related securities, is not readily observable. We develop a generalized smooth-transition vector error-correction model, or GST-VECM, to estimate the arbitrage mechanism from financial market data. The GST-VECM can (i) back out the implied no-arbitrage band, (ii) estimate arbitrage intensity for upper and lower bound violations, and (iii) accommodate convergence risk for statistical arbitrage. Using the introduction of CSI300 ETF trading in China as a natural experiment, we estimate the GST-VECM to reveal some insight into how a microstructural policy, by altering the index arbitrage mechanism, affects the pricing link between spot and futures markets. © 2020 Elsevier B.V.

Keyword ：

Commerce Error correction Financial markets Risk perception

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Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively.

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Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING(1) for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are incorporated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.

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Abstract ：

In recent years, the positive relationship between economic freedom and rising levels of innovation has encouraged economies to meet various environmental challenges as effectively as possible. This paper provides a moderation analysis to capture the impact of international trade on pollution as economic freedom increases. Data from secondary sources were collected to analyze the relationship. Data on economic freedom index variables were collected from the Heritage Foundation website, data for imports and exports, as well as for greenhouse gas emissions were collected from the Eurostat website. The sample of the study includes all European countries for which data is available (1996-2017 period). The findings suggest that economic freedom measured through two main indexes, market openness and regulatory efficiency, positively moderate the relationship between trade and pollution. Most significant results are revealed in the case of the moderating effect of business freedom used as an indicator of the efficiency of regulations and investments freedom used as an indicator of market openness.

Keyword ：

economic freedom international trade market openness pollution regulatory efficiency

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Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.

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