Reviewerly/exHarmony huggingface dataset:
Design Challenges in Low-resource Cross-lingual Entity Linking

Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques.However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained.This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention.Our analysis indicates that current methods are limited by their reliance on Wikipedia's interlanguage links and thus suffer when the foreign language's Wikipedia is small.We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs.With experiments on 25 languages, QuEL shows an average increase of 25% in gold candidate recall and of 13% in end-to-end linking accuracy over state-of-the-art baselines.
Multi-resolution Annotations for Emoji Prediction

Emojis are able to express various linguistic components, including emotions, sentiments, events, etc. Predicting the proper emojis associated with text provides a way to summarize the text accurately, and it has been proven to be a good auxiliary task to many Natural Language Understanding (NLU) tasks.Labels in existing emoji prediction datasets are all passage-based and are usually under the multi-class classification setting.However, in many cases, one single emoji cannot fully cover the theme of a piece of text.It is thus useful to infer the part of text related to each emoji.The lack of multi-label and aspectlevel emoji prediction datasets is one of the bottlenecks for this task.This paper annotates an emoji prediction dataset with passage-level multi-class/multi-label, and aspect-level multiclass annotations.We also present a novel annotation method with which we generate the aspect-level annotations.The annotations are generated heuristically, taking advantage of the self-attention mechanism in Transformer networks.We validate the annotations both automatically and manually to ensure their quality.We also benchmark the dataset with a pretrained BERT model. 45,
Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

In this age, where cryptocurrencies are slowly creeping into the banking services and making a name for them, it is becoming crucially essential to figure out the security concerns when users make transactions. This paper investigates the untrusted users of cryptocurrency transaction services, which are connected using smartphones and computers. However, as technology is increasing, transaction frauds are growing, and there is a need to detect vulnerabilities in systems. A methodology is proposed to identify suspicious users based on their reputation score by collaborating centrality measures and machine learning techniques. The results are validated on two cryptocurrencies network datasets, Bitcoin-OTC, and Bitcoin-Alpha, which contain information of the system formed by the users and the user's trust score. Results found that the proposed approach provides improved and accurate results. Hence, the fusion of machine learning with centrality measures provides a highly robust system and can be adapted to prevent smart devices' financial services.
Study of students’ cognitive states self-regulation during lessons

The relevance of the study of that problem is associated with an increase in information loads on students in connection with the academic load in a situation of information oversaturation.The modern conditions require more and more adaptation to stressful situations, as well as the many stressful factors that students are exposed to both in ordinary life and in the situation of educational activities.In this regard, this article is aimed to study selfregulation in the process of educational activity, namely during lectures, seminars and passing exams/tests.The leading method of the study of this paper is the questionnaire method, conducted among 81 students of the Kazan Federal University for the Humanities and Natural Sciences, which made it possible to identify the most pronounced cognitive states of students during various types of study process.The questionnaire also made it possible to familiarize themselves with their methods of self-regulation of cognitive mental states and obstacles, difficulties in using self-regulation methods.The article shows that students during lecture and seminar training experience the same state of interest and reflection.The difference is that during the lecture classes for students, a cognitive state of curiosity is added to these states, and at a seminar focus.In the tests, students are mostly only in a state of concentration.The article also shows that students most often use self-regulation techniques in seminar-type training sessions.Students resort to various methods of self-regulation: such as forcing oneself to concentrate on a task, a technique, on the contrary, abstracting oneself, as well as using physical exercises, breathing techniques, etc.The materials presented in that article can be used in the development of work programs of academic disciplines implemented within the framework of higher education, in the preparation of training classes of lecture and seminar types, as well as when working with students to increase their effectiveness during the educational process.The scientific novelty of this research consists in the presentation of a technologies of students' cognitive mental states self-regulation during educational process.
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative

Millions of people around the world are sharing COVID-19 related information on social media platforms.Since not all the information shared on the social media is useful, a machine learning system to identify informative posts can help users in finding relevant information.In this paper, we present a BERT classifier system for W-NUT2020 Shared Task 2: Identification of Informative COVID-19 English Tweets.Further, we show that BERT exploits some easy signals to identify informative tweets, and adding simple patterns to uninformative tweets drastically degrades BERT performance.In particular, simply adding "10 deaths" to tweets in dev set, reduces BERT F1score from 92.63 to 7.28.We also propose a simple data augmentation technique that helps in improving the robustness and generalization ability of the BERT classifier.
Semantically Driven Sentence Fusion: Modeling and Evaluation

Sentence fusion is the task of joining related sentences into coherent text.Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants.We show that this hinders models from robustly capturing the semantic relationship between input sentences.To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases.We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation.To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework.Our experiments highlight the improvements of our approach over state-of-the-art models. 1
Semi-Supervised Density Peaks Clustering Based on Constraint Projection

Clustering by fast searching and finding density peaks (DPC) method can rapidly identify the centers of clusters which have relatively high densities and high distances according to a decision graph.Various methods have been introduced to extend the DPC model over the past five years.DPC was originally presented as an unsupervised learning algorithm, and the thought of adding some prior information to DPC emerges as an alternative approach for improving its performance.It is extravagant to collect labeled data in real applications, and annotation of class labels is a nontrivial work, while pairwise constraint information is easier to get.Furthermore, the class label information can be converted into pairwise constraint information.Thus, we can take full advantage of pairwise constraints (or prior information) as much as possible.So this paper presents a new semi-supervised density peaks clustering algorithm (SSDPC) that uses constraint projection, which is flexible in loosening a few constraints over the learning stage.In the first stage, instances involving instance-level constraints and the remaining instances are concurrently projected to a lower dimensional data space led by the pairwise constraints, where viewing the distribution of data instances more clearly is available.Subsequently, traditional DPC is executed on the new lower dimensional dataset.Lastly, a few datasets from the Microsoft Research Asia Multimedia (MSRA-MM) image and UCI machine learning repository datasets are adopted in the experimental validation.The experimental results demonstrate that the proposed SSDPC achieves better performance than other three semi-supervised clustering algorithms.
Pectoral muscle deletion on a mammogram to aid in the early diagnosis of breast cancer

Mammograms are the images used by radiologists to diagnose breast cancer. In this diagnosis, the pectoral muscle appears in mammograms in oblique mediolateral views (MLO) of the right breast and another in the left breast appears in cranio-caudal views which are marked with (CC). Considering that the pectoral muscle has the same density as the small, suspicious masses in the image, its presence in the image being processed could also require detection procedures. In this paper, we present a new general framework for pectoral muscle suppression which is the first work in the analysis of a mammography image. As a result, we proceed to four stages of image processing. The first step is to orient the image if necessary, then use a pre-processing which is to enhance the contrast of the image, and remove the digital lines of the image by morphological filters, apply a filter median. The third step involves segmenting all of the pectoral muscles, which involves threshold the entire image. The final step is to perform a pectoral muscle removal according to the orientation of the muscle in the image, which will be based on the development of the Hough transform for the recognition of borderline detections of the pectoral muscle. Some results obtained on the different images are discussed and compared with other methods (risk assessments). Evaluation of our method shows a significant improvement in performance in removing the pectoral muscle.&#x0D; Keywords: Breast cancer, Mammogram, Pectoral muscle, Hough transform.
Fast Low-Space Algorithms for Subset Sum

We consider the canonical Subset Sum problem: given a list of positive integers a1, …, an and a target integer t with t > ai for all i, determine if there is an S ⊆ [n] such that Σi∊S ai = t. The well-known pseudopolynomialtime dynamic programming algorithm [Bellman, 1957] solves Subset Sum in O(nt) time, while requiring Ω(t) space.In this paper we present algorithms for Subset Sum with Õ(nt) running time and much lower space requirements than Bellman's algorithm, as well as that of prior work. We show that Subset Sum can be solved in Õ(nt) time and O(log(nt)) space with access to O(log n log log n + log t) random bits. This significantly improves upon the Õ(nt1+∊)-time, Õ(n log t)-space algorithm of Bringmann (SODA 2017). We also give a Õ(n1+∊t)-time, O(log(nt))-space randomized algorithm, improving upon previous (nt)O(1)-time O(log(nt))-space algorithms by Elberfeld, Jakoby, and Tantau (FOCS 2010), and Kane (2010). In addition, we also give a poly log(nt)-space, Õ(n2t)-time deterministic algorithm.We also study time-space trade-offs for Subset Sum. For parameter 1 ≤ k ≤ min{n, t}, we present a randomized algorithm running in Õ((n+t) · k) time and O((t/k) poly log(nt)) space.As an application of our results, we give an Õ(min{n2/∊, n/∊2})-time and poly log(nt)-space algorithm for “weak” ∊-approximations of Subset Sum.
Methods for classically simulating noisy networked quantum architectures

Abstract As research on building scalable quantum computers advances, it is important to be able to certify their correctness. Due to the exponential hardness of classically simulating quantum computation, straight-forward verification through classical simulation fails. However, we can classically simulate small scale quantum computations and hence we are able to test that devices behave as expected in this domain. This constitutes the first step towards obtaining confidence in the anticipated quantum-advantage when we extend to scales that can no longer be simulated. Realistic devices have restrictions due to their architecture and limitations due to physical imperfections and noise. Here we extend the usual ideal simulations by considering those effects. We provide a general methodology for constructing realistic simulations emulating the physical system which will both provide a benchmark for realistic devices, and guide experimental research in the quest for quantum-advantage. We exemplify our methodology by simulating a networked architecture and corresponding noise-model; in particular that of the device developed in the Networked Quantum Information Technologies Hub (NQIT) (Networked Quantum Information Technologies Hub 2018 https://nqit.ox.ac.uk/ ; 2016 private communication. The error model was based on communication with Chris Balance and Tom Harty, mediated through Niel de Beaudrap, early on the NQIT project. Continued collaboration and communication with experimentalists could lead in refinement of the error model, which could be subject for future work.). For our simulations we use, with suitable modification, the classical simulator of Bravyi and Gosset 2016 ( Phys. Rev. Lett. 116 250501). The specific problems considered belong to the class of instantaneous quantum polynomial-time ( IQP ) problems (Shepherd and Bremner 2009 Proc. R. Soc. A 465 141339), a class believed to be hard for classical computing devices, and to be a promising candidate for the first demonstration of quantum-advantage. We first consider a subclass of IQP , defined in Bermejo-Vega et al 2018 ( Phys. Rev. X 8 021010), involving two-dimensional dynamical quantum simulators, before moving to more general instances of IQP , but which are still restricted to the architecture of NQIT.
Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing

Recent work raises concerns about the use of standard splits to compare natural language processing models.We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results.We use this technique to rank six English part-ofspeech taggers across two data sets and three evaluation metrics.
Modularized Syntactic Neural Networks for Sentence Classification

This paper focuses on tree-based modeling for the sentence classification task.In existing works, aggregating on a syntax tree usually considers local information of sub-trees.In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees.In MSNN, each node of a syntax tree is modeled by a label-related syntax module.Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation.We design a tree-parallel mini-batch strategy for efficient training and predicting.Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.
Enhancing Magic Sets with an Application to Ontological Reasoning

Abstract Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and aggregations, and may slow down program evaluation. This paper enhances the magic set technique by preventing the creation of (new) recursive definitions in the rewritten program. It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program. Moreover, the rewritten program is further optimized by the elimination of subsumed rules and by the efficient handling of the cases where binding propagation is lost. The research was stimulated by a challenge on the exploitation of Datalog/dlv for efficient reasoning on large ontologies. All proposed techniques have been hence implemented in the dlv system, and tested for ontological reasoning, confirming their effectiveness.
Detecting Entailment in Code-Mixed Hindi-English Conversations

The presence of large-scale corpora for Natural Language Inference (NLI) has spurred deep learning research in this area, though much of this research has focused solely on monolingual data.Code-mixing is the intertwined usage of multiple languages, and is commonly seen in informal conversations among polyglots.Given the rising importance of dialogue agents, it is imperative that they understand code-mixing, but the scarcity of code-mixed Natural Language Understanding (NLU) datasets has precluded research in this area.The dataset by Khanuja et al. (2020a) for detecting conversational entailment in codemixed Hindi-English text is the first of its kind.We investigate the effectiveness of language modeling, data augmentation, translation, and architectural approaches to address the codemixed, conversational, and low-resource aspects of this dataset.We obtain +8.09% test set accuracy over the current state of the art.
AUTOMATIC EMOTION IDENTIFICATION IN RUSSIAN TEXT MESSAGES

Automatic emotive text analysis has demonstrated its relevance in recent years. In this paper, we address the issue of identification emotions in the text of informal internet-discourse of the Russian language. We consider text messages collected from Telegram and VK. Due to difficulty of such advanced form of sentiment analysis, this paper proposes an integrated approach to combining linguistic methods and machine learning. As a result, an automatic classifier of text messages on expressed emotions is designed. On testing, our model is estimated to provide near-human performance.
Ad-hoc Document Retrieval using Weak-Supervision with BERT and GPT2

We describe a weakly-supervised method for training deep learning models for the task of ad-hoc document retrieval.Our method is based on generative and discriminative models that are trained using weak-supervision based solely on the documents in the corpus.We present an end-to-end retrieval system that starts with traditional information retrieval methods, followed by two deep learning re-rankers.We evaluate our method on three different datasets: a COVID-19 related scientific literature dataset and two news datasets.We show that our method outperforms state-ofthe-art methods; this without the need for the expensive process of manually labeling data.
Single-ion addressing via trap potential modulation in global optical fields

Abstract To date, individual addressing of ion qubits has relied primarily on local Rabi or transition frequency differences between ions created via electromagnetic field spatial gradients or via ion transport operations. Alternatively, it is possible to synthesize arbitrary local one-qubit gates by leveraging local phase differences in a global driving field. Here we report individual addressing of 40 Ca + ions in a two-ion crystal using axial potential modulation in a global gate laser field. We characterize the resulting gate performance via one-qubit randomized benchmarking, applying different random sequences to each co-trapped ion. We identify the primary error sources and compare the results with single-ion experiments to better understand our experimental limitations. These experiments form a foundation for the universal control of two ions, confined in the same potential well, with a single gate laser beam.
Are Multilingual Neural Machine Translation Models Better at Capturing Linguistic Features?

We investigate the effect of training NMT models on multiple target languages.We hypothesize that the integration of multiple languages and the increase of linguistic diversity will lead to a stronger representation of syntactic and semantic features captured by the model.We test our hypothesis on two different NMT architectures: The widely-used Transformer architecture and the Attention Bridge architecture.We train models on Europarl data and quantify the level of syntactic and semantic information discovered by the models using three different methods: SentEval linguistic probing tasks, an analysis of the attention structures regarding the inherent phrase and dependency information and a structural probe on contextualized word representations.Our results show evidence that with growing number of target languages the Attention Bridge model increasingly picks up certain linguistic properties including some syntactic and semantic aspects of the sentence whereas Transformer models are largely unaffected.The latter also applies to phrase structure and syntactic dependencies that do not seem to be developing in sentence representations when increasing the linguistic diversity in training to translate.This is rather surprising and may hint on the relatively little influence of grammatical structure on language understanding.
Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

Aspect-based Sentiment Analysis assists in understanding the opinion of the associated entities helping for a better quality of a service or a product. A model is developed to detect the aspect-based sentiment in Nepali text using Machine Learning (ML) classifier algorithms namely Support Vector Machine (SVM) and Naïve Bayes (NB). The system collects Nepali text data from various websites and Part of Speech (POS) tagging is applied to extract the desired features of aspect and sentiment. Manual labeling is done for each sentence to identify the sentiment of the sentence. Term Frequency – Inverse Document Frequency (TF-IDF) is applied to compute the importance of the words. The feature vectors thus produced are then applied to the Classifier algorithms to predict and classify the sentence. The accuracy obtained by the SVM classifier is 76.8% whereas Bernoulli NB is 77.5%.