Reviewerly/exHarmony huggingface dataset:
Deep Regression Network-Assisted Efficient Streamline Generation Method

Streamlining is one of the most frequently utilized visualization methods to analyze the flow structure of computational fluid dynamics (CFD) data.However, it is challenging to find a set of streamlines showing the most prominent flow across the entire flow field due to the heavy computation time required to generate bundles of streamlines.In this paper, we propose an efficient streamline generation method that removes several seed candidates that are predicted as less important using a 3D U-net based regression model.We employ 3D line integral convolution (LIC) volumes that depict the entire flow field for training data of the proposed learning model and evaluate our method using a real-world CFD data set.We find using our model that we can obtain quality of visualization results comparable to that of the ground truth even when more than 90% of the seed candidates are truncated while operating 6.6∼17.1 times faster than the competing method.
Reinforced Abstractive Text Summarization With Semantic Added Reward

Text summarization is an important task in natural language processing (NLP). Neural summary models summarize information by understanding and rewriting documents through the encoder-decoder structure. Recent studies have sought to overcome the bias that cross-entropy-based learning methods can have through reinforcement learning (RL)-based learning methods or the problem of failing to learn optimized for metrics. However, the ROUGE metric with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -gram matching is not a perfect solution. The purpose of this study is to improve the quality of the summary statement by proposing a reward function used in text summarization based on RL. We propose ROUGE-SIM and ROUGE-WMD, modified functions of the ROUGE function. ROUGE-SIM enables meaningfully similar words, in contrast to ROUGE-L. ROUGE-WMD is a function adding semantic similarity to ROUGE-L. The semantic similarity between articles and summary text was computed using Word Mover’s Distance (WMD) methodology. Our model with two proposed reward functions demonstrated superior performance on ROUGE-1, ROUGE-2, and ROUGE_L than on ROUGE-L as a reward function. Our two models, ROUGE-SIM and ROUGE-WMD, scored 0.418 and 0.406 for ROUGE-L, respectively, for the Gigaword dataset. The two reward functions outperformed ROUGE-L even in the abstractiveness and grammatical aspects.
Edit Distance Based Curriculum Learning for Paraphrase Generation

Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation.In this study, we apply curriculum learning to paraphrase generation for the first time.Different from machine translation, paraphrase generation allows a certain level of discrepancy in semantics between source and target, which results in diverse transformations from lexical substitution to reordering of clauses.Hence, the difficulty of transformations requires considering both source and target contexts.We propose an edit distance between a paraphrased sentence pair as a difficulty metric in curriculum learning.Experiments on formality transfer using GYAFC showed that our curriculum learning with edit distance improves the quality of paraphrase generation.Additionally, the proposed method improves the quality of difficult samples, which was not possible for previous methods.
Tuple Interpretations for Higher-Order Complexity.

We develop a class of algebraic interpretations for many-sorted and higher-order term rewriting systems that takes type information into account. Specifically, base-type terms are mapped to tuples of natural numbers and higher-order terms to functions between those tuples. Tuples may carry information relevant to the type; for instance, a term of type nat may be associated to a pair ⟨ cost, size ⟩ representing its evaluation cost and size. This class of interpretations results in a more fine-grained notion of complexity than runtime or derivational complexity, which makes it particularly useful to obtain complexity bounds for higher-order rewriting systems.We show that rewriting systems compatible with tuple interpretations admit finite bounds on derivation height. Furthermore, we demonstrate how to mechanically construct tuple interpretations and how to orient β and η reductions within our technique. Finally, we relate our method to runtime complexity and prove that specific interpretation shapes imply certain runtime complexity bounds.
Croatian Pupils' Perspectives on Remote Teaching and Learning during the COVID-19 Pandemic

On March 16th 2020, the Croatian government mandated a nationwide closure of schools and introduced remote teaching and learning in response to the COVID-19 pandemic. The present study explores the perspectives of pupils in the 7th grade of elementary education regarding this period of remote teaching and learning. The study aims to examine the level and determinants of pupils' satisfaction with newly implemented practices and to explore their perception of remote teaching and learning in comparison to classroom practices. An online survey was conducted with 920 pupils from 23 elementary schools in Zagreb between May 25th and June 6th 2020. The results indicate average satisfaction levels and a weak ability of the model to predict pupils' satisfaction based on a set of individual pupil characteristics. Pupils' interest in using digital devices and fathers' educational status were the only significant predictors of satisfaction. On average, pupils perceived remote teaching and learning as unfavourable in terms of quality, task load and effort in comparison to classroom practices. There was, however, a small cluster of pupils (15%) who associated remote teaching and learning with higher-quality practices.
A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems

Most existing neural network based taskoriented dialogue systems follow encoderdecoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for the knowledgebased task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes.Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response.We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets.The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics 1 .
Evaluating Gender Bias in Hindi-English Machine Translation

With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs.The word embedding representations of these language models often implicitly draw unwanted associations that form a social bias within the model.The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject.Additionally, there is sparse work done in the realm of measuring and debiasing systems for Indic languages.In our work, we attempt to evaluate and quantify the gender bias within a Hindi-English machine translation system.We implement a modified version of the existing TGBI metric based on the grammatical considerations for Hindi.We also compare and contrast the resulting bias measurements across multiple metrics for pre-trained embeddings and the ones learned by our machine translation model.
Using Zigbee Sensors for Ambient Measurement of Human Gait – Analytical Considerations

With the aging of the population in Canada and elsewhere, applications of Smart Homes for well-being sensing are increasingly being considered in health care. Many of these smart home networks rely on the Zigbee wireless protocol to connect sensors used to measure various health outcomes. This paper provides preliminary results of gait estimation performed on 3 different residences over 11 months using Zigbee connected motion sensors, with a focus on understanding accuracy limitations induced by the Zigbee communication protocol. The accuracy limitations were also observed in the results from a controlled experiment done with 2 different sets of Zigbee motion sensors. This paper provides an in-depth analysis on root cause of variance in gait estimation at the same time laying out conservative variance estimations caused by different scenarios. The accuracy considerations highlighted by the paper are also applicable for all other time sensitive measures. Results of this paper necessitate further analysis of the use of Zigbee operated sensor networks in the evaluation of time sensitive measures.
Adversarial Explanations for Knowledge Graph Embeddings

We propose a novel black-box approach for performing adversarial attacks against knowledge graph embedding models. An adversarial attack is a small perturbation of the data at training time to cause model failure at test time. We make use of an efficient rule learning approach and use abductive reasoning to identify triples which are logical explanations for a particular prediction. The proposed attack is then based on the simple idea to suppress or modify one of the triples in the most confident explanation. Although our attack scheme is model independent and only needs access to the training data, we report results on par with state-of-the-art white-box attack methods that additionally require full access to the model architecture, the learned embeddings, and the loss functions. This is a surprising result which indicates that knowledge graph embedding models can partly be explained post hoc with the help of symbolic methods.
Interfacing participation in citizen science projects with conversational agents

This paper assesses the use of conversational agents (chatbots) as an interface to enhance communication with participants in citizen science projects. After developing a study of the engagement and motivations to interact with chatbots, we explored our results. We based our analysis on the current needs exposed in citizen science literature to assess the opportunities. We found that chatbots are great communication platforms that can help to engage participants as an all-in-one interface. Chatbots can benefit projects in reducing the need for developing an exclusive app while it can be deployed on several platforms. Finally, we establish design suggestions to help citizen science practitioners to incorporate such platforms to new projects. We encourage the development of more advanced interfaces through the incorporation of Machine Learning to several processes.
Biaffine Dependency and Semantic Graph Parsing for EnhancedUniversal Dependencies

This paper presents the system used in our submission to the IWPT 2021 Shared Task.This year the official evaluation metrics was ELAS, therefore dependency parsing might have been avoided as well as other pipeline stages like POS tagging and lemmatization.We nevertheless chose to deploy a combination of a dependency parser and a graph parser.The dependency parser is a biaffine parser, that uses transformers for representing input sentences, with no other feature.The graph parser is a semantic parser that exploits a similar architecture except for using a sigmoid crossentropy loss function to return multiple values for the predicted arcs.The final output is obtained by merging the output of the two parsers.The dependency parser achieves top or close to top LAS performance with respect to other systems that report results on such metrics, except on low resource languages (Tamil, Estonian, Latvian).
COVID-19 Isolation and Contact Tracing with Country Samples: A Systematic Review

COVID-19 was first declared as an international public health emergency and then a pandemic by the WHO. In this systematic review, the importance of isolation and contact tracing has been explained, and what kinds of practices exist in different countries are mentioned.A systematic review was conducted in accordance with the PRISMA and Cochrane guidelines by searching articles from major medical databases such as Google Scholar, PubMed/MEDLINE, Cochrane Library, EMBASE, CINAHL between Jan 1, 2020 and Apr 1, 2021. Observational and modeling studies written on contact tracing, screening, quarantine and isolation were included.27 observational and modeling studies were included. It was seen that rapid contact tracing to reduce the basic reproduction number (R0) from 3.11 to 0.21. Additionally, each new case requires an average of 36 people to be monitored. Since screening programs missed 75% of cases, high-level contact tracing should also be done simultaneously. Wide quarantine would prevent 79.27% deaths and 87.08% infections.Effective, rapid contact tracing is the cornerstone of an effective public health response in outbreaks. Its success depends on quickly identifying cases, gathering information from them about their last contacts, and tracing and isolating those contacts.
A novel prediction algorithm for multivariate data sets

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.
Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.
Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data

This paper presents the system for SemEval 2021 Task 8 (MeasEval).MeasEval is a novel span extraction, classification, and relation extraction task focused on finding quantities, attributes of these quantities, and additional information, including the related measured entities, properties, and measurement contexts.Our submitted system, which placed fifth (team rank) on the leaderboard, consisted of SciBERT with [CLS] token embedding and CRF layer on top.We were also placed first in Quantity (tied) and Unit subtasks, second in MeasuredEntity, Modifier and Qualifies subtasks, and third in Qualifier subtask.
Stage-wise Fine-tuning for Graph-to-Text Generation

Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.However, they fail to fully utilize the structure information of the input graph.In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes the model on Wikipedia before adapting to the graph-to-text generation.In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel treelevel embedding method to capture the interdependency structures of the input graph.This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.
Analysis of Classroom Learning Behaviors Based on Internet of Things and Image Processing

The quick and accurate identification of classroom emotions helps teachers perceive the learning state of their students.This paper designs a bimodal identification system for classroom emotions based on electroencephalogram (EEG) signals and countenances.The system relies on the Internet of things (IoT) technology to collect EEG signals, and extracts the signal features with fractal dimension and multiscale entropy algorithm.After that, the support vector machine (SVM) was adopted to classify the classroom emotions.Then, the features of countenances were extracted by local binary pattern (LBP).Experimental results show that our system accurately identified 85.7% of classroom emotions.Compared with the traditional countenance-based emotion identification method, the bimodal approach could extract rich information on classroom emotions, and achieve a good effect on emotion identification.
The transition towards an environmental sustainability for Cryptocurrency mining

As Cryptocurrency becomes more and more popular so does its demand for mining rigs. At the end of 2020 there were approximately 5,392 different cryptocurrencies available with a total market capitalization of more than $201bn [1]. Cryptocurrencies are using decentralized, distributed systems in order to operate. The mining process involves solving cryptographic equations, which are ultimately used for ensuring encryption of the blockchain transactions, through the use of IT equipment - the most efficient way of doing it being by building mining farms which use Graphics Processing Units (GPUs). The Crypto farmers are rewarded with a share of the transaction they facilitate. As the Cryptocurrency market grows exponentially every year, so does its hunger for energy. For example, the Bitcoin Energy Consumption Index is evaluated to reach 77.782 TWh/year in 2021 [2], which, for comparison, is approximately 1.5 times larger than the entire electricity consumption of Romania in 2020 [3]. In this paper, the transition of Cryptocurrency mining processes towards environmental sustainability will be analysed. A Crypto-farm’s Energy Performance Indicators (EPI) and Power Quality Indices (PQI) will be evaluated and, with the use of dedicated software solutions, the authors will propose an action plan to minimize the environmental impact of the energy boundary and to maximize the EPI, thus maximizing the profitability of this new type of business.
Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection

An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events.In this paper, we present our submission 1 to the Shared Tasks on Socio-Political and Crisis Events Detection, Task 1, Multilingual Protest News Detection, Subtask 2, Event Sentence Classification, of CASE @ ACL-IJCNLP 2021.In our submission, we utilize the RoBERTa model with additional pretraining, and achieve the best F1 score of 0.8532 in event sentence classification in English and the second-best F1 score of 0.8700 in Portuguese via simple translation.We analyze the failure cases of our model.We also conduct an ablation study to show the effect of choosing the right pretrained language model, adding additional training data and data augmentation.
gENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena

Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked.These cross-linguistic differences can lead to ambiguities that are difficult to resolve, especially for sentence-level MT systems.The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren't any specific resources or challenge sets available.In this paper, we introduce gENder-IT, an English-Italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the English source side and multiple gender alternative translations, where needed, on the Italian target side.
Time-Aware Ancient Chinese Text Translation and Inference

In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text.To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era.We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information.We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs.We release both the code and the data 1 for future research.
Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation

Answering a programming question using only its title is difficult as salient contextual information is omitted.Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their corresponding intents from the CoNaLa dataset (Yin et al., 2018).Using both the intent and question body, we use BART to establish a baseline BLEU score of 34.35 for this new task.We find further improvements of 2.8% by combining the mined CoNaLa data with the labeled data to achieve a 35.32 BLEU score.We evaluate prior stateof-the-art CoNaLa models with this additional data and find that our proposed method of using the body and mined data beats the BLEU score of the prior state-of-the-art by 71.96%.Finally, we perform ablations to demonstrate that BART is an unsupervised multimodal learner and examine its extractive behavior. 1