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In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13 percents in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.

AbstractmRNA vaccines have tremendous potential to fight against cancer and viral diseases due to superiorities in safety, efficacy and industrial production. In recent decades, we have witnessed the development of different kinds of mRNAs by sequence optimization to overcome the disadvantage of excessive mRNA immunogenicity, instability and inefficiency. Based on the immunological study, mRNA vaccines are coupled with immunologic adjuvant and various delivery strategies. Except for sequence optimization, the assistance of mRNA-delivering strategies is another method to stabilize mRNAs and improve their efficacy. The understanding of increasing the antigen reactiveness gains insight into mRNA-induced innate immunity and adaptive immunity without antibody-dependent enhancement activity. Therefore, to address the problem, scientists further exploited carrier-based mRNA vaccines (lipid-based delivery, polymer-based delivery, peptide-based delivery, virus-like replicon particle and cationic nanoemulsion), naked mRNA vaccines and dendritic cells-based mRNA vaccines. The article will discuss the molecular biology of mRNA vaccines and underlying anti-virus and anti-tumor mechanisms, with an introduction of their immunological phenomena, delivery strategies, their importance on Corona Virus Disease 2019 (COVID-19) and related clinical trials against cancer and viral diseases. Finally, we will discuss the challenge of mRNA vaccines against bacterial and parasitic diseases.

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate v.s. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the 'long tail' in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.

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Abstract. This article introduces TableMiner + , a Semantic Table Interpretation method that annotates Web tables in a both effective and efficient way. Built on our previous work TableMiner, the extended version advances state-of-the-art in several ways. First, it improves annotation accuracy by making innovative use of various types of contextual information both inside and outside tables as features for inference. Second, it reduces computational overheads by adopting an incremental, bootstrapping approach that starts by creating preliminary and partial annotations of a table using 'sample' data in the table, then using the outcome as 'seed' to guide interpretation of remaining contents. This is then followed by a message passing process that iteratively refines results on the entire table to create the final optimal annotations. Third, it is able to handle all annotation tasks of Semantic Table Interpretation (e.g., annotating a column, or entity cells) while state-of-the-art methods are limited in different ways. We also compile the largest dataset known to date and extensively evaluate TableMiner + against four baselines and two reimplemented (near-identical, as adaptations are needed due to the use of different knowledge bases) state-of-the-art methods. TableMiner + consistently outperforms all models under all experimental settings. On the two most diverse datasets covering multiple domains and various table schemata, it achieves improvement in F1 by between 1 and 42 percentage points depending on specific annotation tasks. It also significantly reduces computational overheads in terms of wall-clock time when compared against classic methods that 'exhaustively' process the entire table content to build features for inference. As a concrete example, compared against a method based on joint inference implemented with parallel computation, the non-parallel implementation of TableMiner + achieves significant improvement in learning accuracy and almost orders of magnitude of savings in wall-clock time.

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