Reviewerly/exHarmony huggingface dataset:
Technologies of distance learning for programming basics on the principles of integrated development of key competences

In the era of the fourth industrial revolution – Industry 4.0 – developing key competences (digital, multilingual and mathematical competences in particular) is of paramount importance. The purpose of this work is to investigate the content of key competences of a secondary school student and to develop a method of teaching for the integrated development of multilingual and mathematical competences in the process of teaching Programming Basics with the help of distant technologies. The objectives of the research include generalizing and systematizing theoretical data on the structure and the content of key competences and the potential of informatics lessons for the development of separate components of multilingual and mathematical competences; generalizing and systematizing theoretical data on the ways of arranging distant support for informatics learning, Programming Basics in particular; to investigate the content and the methods of teaching Programming Basics in 7th-11th grades; to develop the e-learning Moodle course using Python for Programming Basics on the principles of integrated approach to developing separate components of multilingual and mathematical competence with determining some methodical special features while using it. The object of the study is to teach informatics to junior high school and high school students. The subject of the study is the means and the methods of realizing distant support in the process of teaching Programming Basics using Python on the principles of an integrated approach to developing multilingual and mathematical competences.
Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.
BLEWS: Using Blogs to Provide Context for News Articles

An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article's social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc.
Improved Heuristics for Optimal Pathfinding on Game Maps

As computer game worlds get more elaborate the more visible pathfinding performance bottlenecks become. The heuristic functions typically used for guiding A*-based path inding are too simplistic to provide the search with the necessary guidance in such large and complex game worlds. This may result in A*-search exploring the entire game map in order to find a path between two distant locations. This article presents two effective heuristics for estimating distances between locations in large and complex game maps. The former, the dead-end heuristic, eliminates from the search map areas that are provably irrele- vant for the current query, whereas the second heuristic uses so-called gateways to improve its estimates. Empirical evaluation on actual game maps shows that both heuristics reduce the exploration and time complexity of A* search significantly over a standard octile distance metric.
Region-Based Segmentation and Object Detection

Object identification and multi-object picture separation are two firmly related processes and it can be enhanced when understood jointly by supporting data from one assignment to the next. Be that as it may, current best in object models are different portrayal for each space creation joint objects and leaving the categorization of numerous part of the scene uncertain. Picture element appearance highlights enable us to do well on classifying formless foundation classes, while the express portrayal of districts encourage the calculation of increasingly complex highlights essential for object detection. Vitally, our model gives a solitary bound together portrayal of the scene we clarify each picture elements of image and authorize it contains in the web between every random variable in our model.
Anomaly detection in wide area network meshes using two machine learning algorithms

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior in network performance data, gathered by the Open Science Grid using perfSONAR servers. Two machine learning algorithms were studied: a Boosted Decision Tree (BDT) and a simple feedforward neural network. The effectiveness of each algorithm was evaluated and compared. Both have shown sufficient performance and sensitivity.
Soccer Video Summarization Using Deep Learning

This paper presents a deep learning approach to summarizing long soccer videos by leveraging the spatiotemporal learning capability of three-dimensional Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) – Recurrent Neural Network (RNN). Our proposed approach involves, 1) a step-by-step development of a Residual Network (ResNet) based 3D-CNN that recognizes soccer actions, 2) manually annotating 744 soccer clips from five soccer action classes for training, and 3) training an LSTM network on soccer features extracted by the proposed ResNet based 3D-CNN. We combine the 3D-CNN and LSTM models to detect soccer highlights. To summarize a soccer match video, we model the video input as a sequential concatenation of video segments whose inclusion in a summary video production is based on its validated relevance. To evaluate the proposed summarization system, 10 soccer videos were summarized and subsequently evaluated by 48 participants polled from 8 countries using the Mean Opinion Score (MOS) scale. Collectively, the summarized videos received a 4 of 5 MOS.
Explain Yourself! Leveraging Language Models for Commonsense Reasoning

Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning.
Syntactically Supervised Transformers for Faster Neural Machine Translation

Standard decoders for neural machine translation autoregressively generate a single target token per time step, which slows inference especially for long outputs.While architectural advances such as the Transformer fully parallelize the decoder computations at training time, inference still proceeds sequentially.Recent developments in nonand semiautoregressive decoding produce multiple tokens per time step independently of the others, which improves inference speed but deteriorates translation quality.In this work, we propose the syntactically supervised Transformer (SynST), which first autoregressively predicts a chunked parse tree before generating all of the target tokens in one shot conditioned on the predicted parse.A series of controlled experiments demonstrates that SynST decodes sentences ∼ 5× faster than the baseline autoregressive Transformer while achieving higher BLEU scores than most competing methods on En-De and En-Fr datasets.
A Multiscale Visualization of Attention in the Transformer Model

The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach. Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements. However, the multi-layer, multi-head attention mechanism in the Transformer model can be difficult to decipher. To make the model more accessible, we introduce an open-source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism. We demonstrate the tool on BERT and OpenAI GPT-2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions

Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition.Although criticized sometimes, the existing peer review system still stands as the benchmark of research validation.The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI.However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration.Here in this work, we investigate the role of reviewers sentiments embedded within peer review texts to predict the peer review outcome.Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and the review polarity to predict the overall recommendation score as well as the final decision.We achieve significant performance improvement over the baselines (∼ 29% error reduction) proposed in a recently released dataset of peer reviews.An AI of this kind could assist the editors/program chairs as an additional layer of confidence in the final decision making, especially when non-responding/missing reviewers are frequent in present day peer review.
Batch Bayesian optimization using multi-scale search

Hyperparameters are crucial to many machine learning algorithms. Conventional methods like grid search can be expensive when tuning hyperparameters of a complex model, for example, deep neural networks. Recently, Bayesian optimization has become popular in the machine learning community as an efficient tool for tuning hyperparameters. Bayesian optimization is a global optimization technique that is well suited for optimizing expensive black-box functions. Traditionally Bayesian optimization operates sequentially with one recommendation per trial. However, often a batch of recommendations can be simultaneously evaluated. Current batch Bayesian methods are mostly heuristic based and none have considered heteroscedastic nature of the unknown objective functions. This paper proposes a new batch Bayesian optimization method using a multi-scale search strategy. A batch of recommendations is constructed by searching for optimal recommendations across objective functions using alternate smoothness assumptions. Theoretical analysis shows that the proposed batch improves the regret bound of the sequential method by an order of K, where K is the batch size. We show the effectiveness of our techniques by minimizing three benchmark global optimization test functions and tuning the hyperparameters of two machine learning algorithms.
Verifying Binarized Neural Networks by Angluin-Style Learning

We consider the problem of verifying the behavior of binarized neural networks on some input region. We propose an Angluin-style learning algorithm to compile a neural network on a given region into an Ordered Binary Decision Diagram (OBDD), using a SAT solver as an equivalence oracle. The OBDD allows us to efficiently answer a range of verification queries, including counting, computing the probability of counterexamples, and identifying common characteristics of counterexamples. We also present experimental results on verifying binarized neural networks that recognize images of handwritten digits.
Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification

Aspect level sentiment classification aims at identifying the sentiment of each aspect term in a sentence. Deep memory networks often use location information between context word and aspect to generate the memory. Although improved results are achieved, the relation information among aspects in the same sentence is ignored and the word location can't bring enough and accurate information for the analysis on the aspect sentiment. In this paper, we propose a novel framework for aspect level sentiment classification, deep mask memory network with semantic dependency and context moment (DMMN-SDCM), which integrates semantic parsing information of the aspect and the inter-aspect relation information into deep memory network. With the designed attention mechanism based on semantic dependency information, different parts of the context memory in different computational layers are selected and useful inter-aspect information in the same sentence is exploited for the desired aspect. To make full use of the inter-aspect relation information, we also jointly learn a context moment learning task, which aims to learn the sentiment distribution of the entire sentence for providing a background for the desired aspect. We examined the merit of our model on SemEval 2014 Datasets, and the experimental results show that our model achieves a state-of-the-art performance.
CaRB: A Crowdsourced Benchmark for Open IE

Open Information Extraction (Open IE) systems have been traditionally evaluated via manual annotation.Recently, an automated evaluator with a benchmark dataset (OIE2016) was released -it scores Open IE systems automatically by matching system predictions with predictions in the benchmark dataset (Stanovsky and Dagan, 2016).Unfortunately, our analysis reveals that its data is rather noisy, and the tuple matching in the evaluator has issues, making the results of automated comparisons less trustworthy.We contribute CaRB, an improved dataset and framework for testing Open IE systems.To the best of our knowledge, CaRB is the first crowdsourced Open IE dataset and it also makes substantive changes in the matching code and metrics.NLP experts annotate CaRB's dataset to be more accurate than OIE2016.Moreover, we find that on one pair of Open IE systems, CaRB framework provides contradictory results to OIE2016.Human assessment verifies that CaRB's ranking of the two systems is the accurate ranking.We release the CaRB framework along with its crowdsourced dataset.
Cross-Domain Sentiment Classification With Bidirectional Contextualized Transformer Language Models

Cross-domain sentiment classification is an important Natural Language Processing (NLP) task that aims at leveraging knowledge obtained from a source domain to train a high-performance learner for sentiment classification on a target domain. Existing transfer learning methods applied on cross-domain sentiment classification mostly focus on inducing a low-dimensional feature representation shared across domains based on pivots and non-pivots, which is still a low-level representation of sequence data. Recently, there have been great progress in the NLP literature in developing high-level representation language models based on Transformer architecture, which are pre-trained on large text corpus and fine-tuned for specific task with an additional layer on top. Among such language models, the bidirectional contextualized Transformer language models of BERT and XLNet have greatly impacted NLP research field. In this paper, we fine-tune BERT and XLNet for the cross-domain sentiment classification. We then explore their transferability in the context of cross-domain sentiment classification through in-depth analysis of two models' performances and update the state-of-the-arts with a significant margin of improvement. Our results show that such bidirectional contextualized language models outperform the previous state-of-the-arts methods for cross-domain sentiment classification while using up to 120 times less data.
FoR: A Dataset for Synthetic Speech Detection

With the advancements in deep learning and other techniques, synthetic speech is getting closer to a natural sounding voice. Some of the state-of-art technologies achieve such a high level of naturalness that even humans have difficulties distinguishing real speech from computer generated speech. Moreover, these technologies allow a person to train a speech synthesizer with a target voice, creating a model that is able to reproduce someone’s voice with high fidelity.In this paper, we introduce the FoR Dataset, which contains more than 198,000 utterances from the latest deep-learning speech synthesizers as well as real speech. This dataset can be used as base for several studies in speech synthesis and synthetic speech detection. Due to its large amount of utterances, it is pertinent for machine learning studies, since it is able to train even complex deep learning models without overfitting. We present several experiments using this dataset, including a deep learning classifier that reached up to 99.96% accuracy in synthetic speech detection.
Challenges Faced by Students and Teachers on Writing Skills in ESL Contexts: A Literature Review

English is an international language and in demand today.English is by far the most widely used language around the world.However, English language writing has always been a challenge for second language students to master.Developing students' ability in writing is one of the major challenges faced by the ESL teachers in most schools nowadays.Nevertheless, writing has always been a major difficulty faced by students in English language learning, especially in elementary schools.Not only that, teachers are also facing some challenges in teaching writing skills for students in elementary schools.Thus, this study aims to explore the challenges faced by both the students and teachers in learning as well as teaching writing skills in elementary schools.
DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.
Partial Multi-Label Learning with Label Distribution

Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identifying the ground-truth label via utilizing the confidence of each candidate label or estimating the noisy labels in the candidate label sets. Nonetheless, these strategies ignore considering the essential label distribution corresponding to each instance since the label distribution is not explicitly available in the training set. In this paper, a new partial multi-label learning strategy named Pml-ld is proposed to learn from partial multi-label examples via label enhancement. Specifically, label distributions are recovered by leveraging the topological information of the feature space and the correlations among the labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered label distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.