Reviewerly/exHarmony huggingface dataset:
Natural Language Processing of COVID-19 Reports Involving China in New York Times —a Machine-based Framing Study of Media Language

Natural Language Processing (NLP) is a most promising and powerful method for big data analysis. It is gaining increasing attention from language researchers with its potentiality in information extraction, automatic indexing, textual framing, topic modeling, sensitivity analysis and other machine analytics studies. Through employing the LDA topic modeling and NLTK (Natural Language Toolkit) Vader SentimentAnalyser, this research makes a contrastive study of the overall news coverage in New York Times (NYT) against the backdrop of Covid-19 and its China-specific reports, with the aim of addressing what areas of concern were respectively selected and foregrounded to the public in these two types, what sensitivities were revealed and how linguistic devices were used to frame China's response to Covid-19. Analysis of metaphorical expressions in NYT shows that metaphors tended to be employed as a device to realize the dominant negative polarity latent in the reports and thus establish unfavourable images of China. This study deepens the methodological endeavors in media and linguistic studies through combining content analysis and machine-based analysis.
Responding to customer queries automatically by customer reviews’ based Question Answering

The entire world has been undergoing its own digital transformation over the past few decades as technology has advanced in leaps and bounds. Following this, an increase in the number of people using digital platforms for buying products online likewise increases the number of questions or enquiries posted about a product on an online shopping platform like Amazon on a day to day basis. Though we have gone completely digital in posting these questions, the answering of these questions is still manual. The forums are rarely active. By the time the user gets an answer to his question, either he has bought that product already through offline means or has lost interest in buying that product since it is time consuming. Moreover, the questions which are asked are mostly repetitive. At times the answers are already out there since they have already been given to some other user who had asked the same question. Also, lot of answers are embedded in the user reviews. Therefore, the answers can be extracted from the existing product reviews. This may lead to increase in sale and greater customer satisfaction as his query is resolved in much lower response time. We have review-based question answering systems that aim at answering the questions from the reviews given on the product by other customers. However, the existing systems have certain drawbacks due to the use of RNN, like missing attention mechanism etc. In this work, we enhance the performance of the existing review based QA systems by carrying out some prototypical experiments with the basic models of NLP and then moving towards more advanced Language Models while identifying and rectifying the shortcomings of the existing model. Further, in this work a thorough comparative analysis of the models and approaches that have been worked on is presented. We have enhanced the current state of the art existing review QA systems by using BERT, BART and also applied various heuristics for comparison. We achieved the best BLEU score of 0.58 by using BERT, which is an improvement of 0.19 on the current existing system.
E-VAN : Enhanced Variational AutoEncoder Network for Mitigating Gender Bias in Static Word Embeddings

Recent research has shown that pre-trained context-independent word embeddings display biases such as racial bias, gender bias, etc. Using a novel, tunable algorithm, this study attempts to mitigate the hidden gender bias in static embeddings. In order to train the model, an enhanced variational autoencoder (E-VAN) is used to learn the latent space of the embedding. Then the latent distributions are used while adaptively resampling and re-weighting the rare/under-represented data. While the word embeddings retain semantic information, E-VAN effectively mitigates unwanted biased gendered associations. Our method E-VAN outperforms previous state-of-the-art methods in both quantitative and human evaluation.
Measuring Text-to-SQL Semantic Parsing Model on the Question Generalizability

One of the challenges in NLP tasks, such as text-to-SQL semantic parsing, is generalization. In the text-to-SQL task, having separate training and testing data can measure one aspect of the generalization: how well the model generalizes to unseen databases. Other aspects, however, remain unaccounted for. We propose a new dataset and a more challenging and thorough evaluation process that focuses on the two challenges of generalizing the text-to-SQL model: database content references and question patterns. We create SPIDER-QG, an augmented dataset that employs three techniques, to assess generalizability. First, we replace the set of values in the existing test set with other values from the same column in the same database. Second, we use the synonym of each value as a replacement instead. Third, we generate new questions for the existing SQL query by back-translating the original question. Our evaluation setup demonstrates the generalization challenges and struggles of the current models.
Named Entity Recognition on COVID-19 Scientific Papers

Text mining techniques, especially named entity recognition (NER), play a vital role in supporting researchers for keeping track of hundred thousand of papers on COVID-19 related literature. Although a few research has been performed NER on COVID-19 scientific papers, very little is currently known concerning the behaviors of current entity recognition models in this new domain. Therefore, this ongoing study attempts to analyze current NER models’ performance and limitations on the CORD-19 dataset. By examining three NER models, this study showed that NER performance is improved with the similarity between the testing and pretraining data. When there are little manually annotated resources for COVID-19 NER exist, our analysis suggested that for training purposes, enhancing the dictionary for seed annotation is effective (not necessarily requiring costly human annotation).
Random Division: An Effective Method for Chinese Text Classification

As a fundamental part of natural language processing, text classification is the backbone of tasks and applications such as machine translation and classification. Among the text classification tasks of all languages, the one for Chinese appears to be one of the most challenging due to the complex structures and expressions within the nature of Chinese. Researchers generally require a significant amount of data for model training and tuning, while most of the time, that desired amount of data cannot be fulfilled and satisfied. Given the circumstances, we propose an effective data enhancement technique to lower the demand for data. The central principle is as follows: Randomize the acquired word vectors and tokens from tokenizing the text based on a certain density level (i.e., every group contains five words), then use the randomized results as data input. During the above process, a considerable number of data variations would be generated, easing the demand for data. From the experiments, we tested our theory on multiple Chinese natural language processing datasets and received signs of improvements in model performance across all the datasets used, thus proving the validity of the previously mentioned method.
CWITR: A Corpus for Automatic Complex Word Identification in Turkish Texts

The Complex Word Identification (CWI) task aims to provide support to resolve accessibility barriers for people who experience difficulties with cognitive, language, and learning disabilities. The task is concerned with the detection and identification of complex words that are unusual and difficult to understand by certain target groups. CWI systems have a large impact on the output of Text Simplification (TS) systems. This paper revisits the CWI task by extending available datasets by creating a new CWI corpus. In this study, we collect a new CWI dataset (CWITR) of complex single and multi-token words consisting of different text genres for Turkish and prepare it for investigation of computational methods on discrimination between complex and non-complex words forms.
Classification of advertisement articles using sentiment analysis

We live in a flood of big data and information through computers, communications, social media, and mass media. In other words, we can get the information we want quickly and easily, but we have many questions about the accuracy and reliability of this information. That is, there are many problems in trying to obtain accurate knowledge of such reckless details, and in particular, advertisement articles provided by online newspapers need to be clearer and more manageable when individuals try to find precise information and reports. Such experiences are threatened even to the foundation of existence due to distrust of Internet newspapers and advertisement evasion. To solve this problem, this study used emotion analysis of natural language processing to classify general and advertisement articles. Getting going Existing similar studies have mainly been undertaken to classify such advertisement articles, such as spam mail classification, and most of these studies used general natural language processing. However, this paper is a study that analyzes text data to understand further the meaning of the words, sentences, and phrases and adds steps to explore emotions to provide more accurate information that individuals want.
DIFFSTRACT: distinguishing the content of texts

Nowadays, it is almost a standard issue to generate summaries of texts automatically. In contrast, it is still a problem to identify the differences in the statements of the two publications. For the most part, this still requires a human being to read and evaluate at least excerpts of the relevant passages. Finding a so-called text differentiation with appropriate tools is becoming an increasingly interesting and important task to effectively cope with the daily flood of information on the WWW. For years, co-occurrence graphs have been a proven means of deriving statements of various kinds from texts. So-called text- representing centroids (TRC's) has often been an effective tool for identifying, comparing and categorizing texts or sections. The present article examines how a different form of co-occurrence graphs can take place and be helpful. First, different co-occurrence graphs are built from a larger corpus and various individual texts or text groups. Subsequently, the calculated difference graphs can be used to create summaries that precisely characterize the differences between texts. Experimental results show that this new method works well.
A corpus of drafts of NLP papers from non-native English speakers

We created an English parallel corpus of 3,005 sentence pairs, each containing a well-polished text from ACL Anthology Reference Corpus (ACL-ARC) [1] and corresponding restated drafts collected from 26 non-native writers. The purpose of this paper is to explore the writing features of the drafts from non-native English speakers, so as to benefit research in Academic Writing Aid Systems. We present a feature analysis of the corpus based on handcrafted features. To assess utility, we formulate a draft identification task to automatically recognize drafts from ground truth texts based on hybrid features. We show that the combination of deep semantic features with the optimal handcrafted features improves identification accuracy on the collected data, up to 84.57%.
Explaining Math Word Problem Solvers

Automated math word problem solvers based on neural networks have successfully managed to obtain 70-80% accuracy in solving arithmetic word problems. However, it has been shown that these solvers may rely on superficial patterns to obtain their equations. In order to determine what information math word problem solvers use to generate solutions, we remove parts of the input and measure the model’s performance on the perturbed dataset. Our results show that the model is not sensitive to the removal of many words from the input and can still manage to find a correct answer when given a nonsense question. This indicates that automatic solvers do not follow the semantic logic of math word problems, and may be overfitting to the presence of specific words.
Extracting Source Information From News Articles

One of the factors influencing the credibility of news is source attribution. Ideally, news would be based on a balanced variety of sources. In this work we use spaCy1 and Python2 to identify sources of information cited in news articles and assign the sources to categories, as a first step in building software that assesses the balance and breadth of the sourcing in news articles. The preliminary testing of the software indicates that identification of the sources has a recall of 73% and accuracy of 95%, and the sources are categorized with overall accuracy of 78%.
Dependency Graphs for Summarization and Keyphrase Extraction

We introduce a graph-based summarization and keyphrase extraction system that uses dependency trees as inputs for building a document graph. The document graph is built by connecting nodes containing lemmas and sentence identifiers after redirecting dependency links to emphasize semantically important entities. After applying a ranking algorithm to the document graph, we extract the highest ranked sentences as the summary. At the same time, the highest ranked lemmas are aggregated into keyphrases using their context in the dependency graph. Our algorithm specializes in handling long documents, including scientific, technical, legal, and medical documents.
Research on Innovation Management of Enterprise Supply Chain Digital Platform Based on Blockchain Technology

Purpose: In response to the problems of high production costs, weak comprehensive competitiveness, and incomplete capital chain in the development of enterprises in the international market, this article proposes to apply blockchain technology to the digital platform of the supply chain. By scientifically managing the digital platform, enterprises can reduce production costs and improve their capital chain. Design/methodology/approach: In this long-term economic life, theoretical science and technology are still developing, gradually forming a blockchain-based enterprise supply chain development model. The continuous research on the blockchain theory can provide more convenient services for the supply chain digital platform innovation management. Findings: This article studies the entire supply chain management process through blockchain algorithms and innovative management methods, which can enable enterprises to further develop on the digital platform of supply chain, promote the digital construction of supply chain, and ensure the sustainable development of enterprises. Originality/value: This can reduce the supply chain digital platform innovation management risk of enterprises by more than 60%, as well as reduce the cost of enterprises, which has a more far-reaching impact on the sustainable development of enterprises.
A Neural Span-Based Continual Named Entity Recognition Model

Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e.g., personal assistants). Meanwhile the learning paradigm of NER advances to new patterns such as the span-based methods. However, its potential to CL has not been fully explored. In this paper, we propose SpanKL, a simple yet effective Span-based model with Knowledge distillation (KD) to preserve memories and multi-Label prediction to prevent conflicts in CL-NER. Unlike prior sequence labeling approaches, the inherently independent modeling in span and entity level with the designed coherent optimization on SpanKL promotes its learning at each incremental step and mitigates the forgetting. Experiments on synthetic CL datasets derived from OntoNotes and Few-NERD show that SpanKL significantly outperforms previous SoTA in many aspects, and obtains the smallest gap from CL to the upper bound revealing its high practiced value. The code is available at https://github.com/Qznan/SpanKL.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems

Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for minimax problems in the Federated Learning (FL) paradigm have received considerable interest. Existing federated algorithms for general minimax problems require the full aggregation (i.e., aggregation of local model information from all clients) in each training round. Thus, they are inapplicable to an important setting of FL known as the cross-device setting, which involves numerous unreliable mobile/IoT devices. In this paper, we develop the first practical algorithm named CDMA for general minimax problems in the cross-device FL setting. CDMA is based on a Start-Immediately-With-Enough-Responses mechanism, in which the server first signals a subset of clients to perform local computation and then starts to aggregate the local results reported by clients once it receives responses from enough clients in each round. With this mechanism, CDMA is resilient to the low client availability. In addition, CDMA is incorporated with a lightweight global correction in the local update steps of clients, which mitigates the impact of slow network connections. We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks. Theoretical and experimental results demonstrate the efficiency of CDMA.
Exploration via Epistemic Value Estimation

How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance to compute an exploration bonus or upper confidence bound. Unfortunately the required uncertainty is difficult to estimate in general with function approximation. We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators. It equips agents with a tractable posterior over all their parameters from which epistemic value uncertainty can be computed efficiently. We use the recipe to derive an epistemic Q-Learning agent and observe competitive performance on a series of benchmarks. Experiments confirm that the EVE recipe facilitates efficient exploration in hard exploration tasks.
Open-Set Profile-to-Frontal Face Recognition on a Very Limited Dataset

Open-set face recognition on a small dataset with limited image samples per individual poses a significant challenge and is a topic of active research.Therefore, this study investigated the problems of open-set face verification and face identification on a dataset known as the ITB Frontal Profile Limited Dataset (IFPLD), which included only one frontal and one profile image per individual.Various training procedures were used to obtain a more appropriate network embedding for feature representation on the dataset.Transfer learning was employed to improve the performance of the models by fine-tuning the networks using a dataset with properties similar to those of the IFPLD.The results showed that the SimCLR method generated the optimal network embedding for face verification on the Siamese network.The prototypical network with an N-way-k-shot learning scenario where k-1 came from data augmentation outperformed the Siamese network for face identification by a maximum 17.0% accuracy improvement.The transformation from 1-shot learning to k-shot learning is critical for achieving high performance.
The Electronic Control System of a Trapped-Ion Quantum Processor: A Systematic Literature Review

[Background] Ions in an ion trap are among the most promising technologies to implement a quantum processor. This machine is governed by a classic electronic control system, for which we found no systematic electronic system-level approach for its conception, design, implementation, and/or verification. The trapped-ion quantum processor cannot advance its roadmap without an appropriate and fitting electronic control system. To fully enable further advancements in the field, an understanding of the electronic control system as a system of its own and the conception of its electronic system-level description, are due needed. [Objective] Therefore, we want to address the electronic control system of a trapped-ion quantum processor as a system of its own by first identifying its published literature. [Method] For that purpose, we conducted a systematic literature review abiding by the APISSER methodology for systematic literature reviews in engineering, which describes and delimits the reach of this study. [Results] In this paper, we report the results of the study, classify them, and present possible research directions and considerations to be taken in the conception of this system. [Conclusion] This study lays a introductory foundation to grasp the requirements and environment of the electronic control system of a trapped-ion quantum processor. A foundation much needed to leverage further research on this topic by the engineering community. Furthermore, it serves as a checkpoint in time: listing and synthesizing, the existing published body-of-knowledge of this topic within the defined boundaries of our method.
Multi-Agent Deep Reinforcement Learning With Progressive Negative Reward for Cryptocurrency Trading

Recently, reinforcement learning has been applied to cryptocurrencies to make profitable trades. However, cryptocurrency trading is a very challenging task due to the volatility of the market, especially during bearish periods. In addressing this problem, the existing literature employs single-agent techniques such as deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimization (PPO), or their ensembles. Moreover, in the context of cryptocurrencies, the mechanisms for restricting losses during a bearish market are insufficiently robust. Consequently, the performance of reinforcement learning methods for cryptocurrency trading in the existing literature is constrained. To overcome this limitation, in this paper, we propose a novel cryptocurrency trading method based on multi-agent proximal policy optimization (MAPPO) with a collaborative multi-agent scheme and a local-global reward function to optimize both the individual and collective performance of the agents. Both a multi-objective optimization technique and a multi-scale continuous loss (MSCL) reward are used to train agents using a progressive penalty to avoid consecutive losses of portfolio value. As a result, better cumulative returns are achieved than when baseline methods are used. In addition, the superiority of our method is emphasized by the result of the bearish test set, where only our method can make a profit. Specifically, our method obtains a 2.36% cumulative return, whereas the baseline methods result in negative cumulative returns. In comparison to FinRL-Ensemble, a reinforcement learning-based method, our method achieves a 46.05% greater cumulative return in the bullish test set.