Reviewerly/exHarmony huggingface dataset:
A unified view of density-based methods for semi-supervised clustering and classification

Abstract Semi-supervised learning is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we first introduce a unified view of density-based clustering algorithms. We then build upon this view and bridge the areas of semi-supervised clustering and classification under a common umbrella of density-based techniques. We show that there are close relations between density-based clustering algorithms and the graph-based approach for transductive classification. These relations are then used as a basis for a new framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. In addition, we generalize the core algorithm in our framework, HDBSCAN*, so that it can also perform semi-supervised clustering by directly taking advantage of any fraction of labeled data that may be available. Experimental results on a large collection of datasets show the advantages of the proposed approach both for semi-supervised classification as well as for semi-supervised clustering.
Text2Math: End-to-end Parsing Text into Math Expressions

We propose Text2Math, a model for semantically parsing text into math expressions.The model can be used to solve different math related problems including arithmetic word problems (Roy and Roth, 2017;Liang et al., 2018) and equation parsing problems (Roy et al., 2016).Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process.Empirical results on benchmark datasets demonstrate the efficacy of our approach.
Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management

Interference Management is a vast topic present in many disciplines. The majority of wireless standards suffer the drawback of interference intrusion and the network efficiency drop due to that. Traditionally, interference management has been addressed by proposing signal processing techniques that minimize their effects locally. However, the fast evolution of future communications makes difficult to adapt to new era. In this paper we propose the use of Deep Learning techniques to present a compact system for interference management. In particular, we describe two subsystems capable to detect the presence of interference, even in high Signal to Interference Ratio (SIR), and interference classification in several radio standards. Finally, we present results based on real signals captured from terrestrial and satellite networks and the conclusions unveil the courageous future of AI and wireless communications.
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation

Variational encoder-decoders have achieved well-recognized performance in the dialogue generation task.Existing works simply assume the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively.To address the issues, we propose to use the Dirichlet distribution with flexible structures to characterize the latent variables in place of the traditional Gaussian distribution, called Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder model (Dir-VHRED).Based on which, we further find that there is redundancy among the dimensions of latent variable, and the lengths and sentence patterns of the responses can be strongly correlated to each dimension of the latent variable.Therefore, controllable responses can be generated through specifying the value of each dimension of the latent variable.Experimental results on benchmarks show that our proposed Dir-VHRED yields substantial improvements on negative log-likelihood, word-embeddingbased and human evaluations.
UCAM Biomedical Translation at WMT19: Transfer Learning Multi-domain Ensembles

The 2019 WMT Biomedical translation task involved translating Medline abstracts.We approached this using transfer learning to obtain a series of strong neural models on distinct domains, and combining them into multidomain ensembles.We further experiment with an adaptive language-model ensemble weighting scheme.Our submission achieved the best submitted results on both directions of English-Spanish.
Robust Self-Adaptation Fall-Detection System Based on Camera Height

Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.
On Testing for Biases in Peer Review

We consider the issue of biases in scholarly research, specifically, in peer review. There is a long standing debate on whether exposing author identities to reviewers induces biases against certain groups, and our focus is on designing tests to detect the presence of such biases. Our starting point is a remarkable recent work by Tomkins, Zhang and Heavlin which conducted a controlled, large-scale experiment to investigate existence of biases in the peer reviewing of the WSDM conference. We present two sets of results in this paper. The first set of results is negative, and pertains to the statistical tests and the experimental setup used in the work of Tomkins et al. We show that the test employed therein does not guarantee control over false alarm probability and under correlations between relevant variables, coupled with any of the following conditions, with high probability can declare a presence of bias when it is in fact absent: (a) measurement error, (b) model mismatch, (c) reviewer calibration. Moreover, we show that the setup of their experiment may itself inflate false alarm probability if (d) bidding is performed in non-blind manner or (e) popular reviewer assignment procedure is employed. Our second set of results is positive, in that we present a general framework for testing for biases in (single vs. double blind) peer review. We then present a hypothesis test with guaranteed control over false alarm probability and non-trivial power even under conditions (a)--(c). Conditions (d) and (e) are more fundamental problems that are tied to the experimental setup and not necessarily related to the test.
A Hybrid Model for Globally Coherent Story Generation

Automatically generating globally coherent stories is a challenging problem.Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences.Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite datademanding.We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus.The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan.Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.
Writing Anxiety among Indonesian EFL Students: Factors and Strategies

Writing anxiety is a term for a wide variety of apprehensive and pessimistic feelings about writing.The purpose of this study is to identify factors contributing to writing anxiety experienced by EFL students in writing essays and the strategies they apply to manage it.The possible factors of the anxiety were elaborated into close-ended questionnaire items that were distributed to the sampled students followed by open-ended questionnaire item on strategies to deal with the anxiety.The findings indicate that from several potentially anxiety producing factors of essay writing, essay assignment that must be done in the class is the most dominant factor of writing anxiety experienced by the students.Collaborative works in pairs or small groups for idea development and essay construction are the common strategies adopted to minimize the anxiety.The findings imply that when students are given the opportunity to use the preferred strategies, their writing anxiety can be manageable.
Hammering Mizar by Learning Clause Guidance (Short Paper).

We describe a very large improvement of existing hammer-style proof automation over large ITP libraries by combining learning and theorem proving. In particular, we have integrated state-of-the-art machine learners into the E automated theorem prover, and developed methods that allow learning and efficient internal guidance of E over the whole Mizar library. The resulting trained system improves the real-time performance of E on the Mizar library by 70% in a single-strategy setting.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation

This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking. Although there are many solutions for low-resource scenarios, such as multilingual NMT and back-translation, we have empirically confirmed their limited success when restricted to in-domain data. We therefore propose to exploit out-of-domain data through transfer learning, by using it to first train a multilingual NMT model followed by multistage fine-tuning on in-domain parallel and back-translated pseudo-parallel data. Our approach, which combines domain adaptation, multilingualism, and back-translation, helps improve the translation quality by more than 3.7 BLEU points, over a strong baseline, for this extremely low-resource scenario.
Real-Time Gesture Recognition Using 3D Sensory Data and a Light Convolutional Neural Network

In this work, we propose an end-to-end system that provides both hardware and software support for real-time gesture recognition. We apply a convolutional neural network over 3D rotation data of finger joints rather than over vision-based data, in order to extract high-level intentions (features) users are trying to convey. A pair of customized motion capturing gloves are designed with inertial measurement unit (IMU) sensors to obtain gestural datasets for network training and real-time recognition. A network reduction strategy has been developed to appropriately reduce a network's complexity in both depth and width dimensions while maintaining a high recognition accuracy with the classification model produced by the network. The classification model is able to classify new data samples by scanning a real-time stream of joint rotations during the use of the gloves. Our evaluation results expose the relationships between the network reduction hyperparameters and the change of recognition accuracy. Based on the evaluation, we are able to determine an appropriate version of the light network and achieve 98% accuracy.
Estimation of Parameters in the SIR Epidemic Model Using Particle Swarm Optimization

Susceptible, Infected and Resistant (SIR) models are used to observe the spread of infection from infected populations into healthy populations.Stability analysis of the model is done using the Routh-Hurwitz criteria, basic reproduction number or the Lyapunov Stability.For stability analysis, parameters value are needed and these values are usually assumed.Given data cannot be used to determine the parameter values of SIR model because analytic solution of system of nonlinear differential equation cannot be determined.In this article, we determine the parameters of the exponential growth model, logistic model and SIR models using the Particle Swarm Optimization (PSO) algorithm.The SIR model is solved numerically using the Euler method based on the parameter values determined by PSO.The simulation results show that the PSO algorithm is good enough in determining the parameters of the three models compared to analytical methods and the Gauss-Newton's method.Based on the average hypothesis test the relative error obtained from the PSO algorithm to determine the parameters is less than 3% with a significance level of 1%.
Steganographic Techniques Classification According to Image Format

In this work, we present a classification of steganographic methods applicable to digital images. We also propose a classification of steganographic methods according to the type of image used. We noticed there are no methods that can be applied to all image formats. Each type of image has its characteristics and each steganographic method operates on a precise colorimetric representation. This classification provides an overview of the techniques used for the steganography of digital images
When Disinformation Studies Meets Production Studies: Social Identities and Moral Justifications in the Political Trolling Industry

The field of disinformation studies remains relatively silent about questions of identity, motivation, labor, and morality. Drawing from a one-year ethnographic study of disinformation producers employed in digital black ops campaigns in the Philippines, this article proposes that approaches from production studies can address gaps in disinformation research. We argue that approaching disinformation as a culture of production opens inquiry into the social conditions that entice people to this work and the creative industry practices that normalize fake news as a side gig. This article critically reflects on the methodological risks and opportunities of ethnographic research that subverts expectations of the exceptionally villainous troll and instead uses narratives of creative workers’ complicity and collusion to advance holistic social critique and local-level disinformation interventions.
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification

On the NLP4IF 2019 sentence level propaganda classification task, we used a BERT language model that was pre-trained on Wikipedia and BookCorpus as team ltuorp ranking #1 of 26. It uses deep learning in the form of an attention transformer. We substituted the final layer of the neural network to a linear real valued output neuron from a layer of softmaxes. The backpropagation trained the entire neural network and not just the last layer. Training took 3 epochs and on our computation resources this took approximately one day. The pre-trained model consisted of uncased words and there were 12-layers, 768-hidden neurons with 12-heads for a total of 110 million parameters. The articles used in the training data promote divisive language similar to state-actor-funded influence operations on social media. Twitter shows state-sponsored examples designed to maximize division occurring across political lines, ranging from “Obama calls me a clinger, Hillary calls me deplorable, ... and Trump calls me an American” oriented to the political right, to Russian propaganda featuring “Black Lives Matter” material with suggestions of institutional racism in US police forces oriented to the political left. We hope that raising awareness through our work will reduce the polarizing dialogue for the betterment of nations.
Detecting Systemic Data Quality Issues in Electronic Health Records.

Secondary analysis of electronic health records for clinical research faces significant challenges due to known data quality issues in health data observationally collected for clinical care and the data biases caused by standard healthcare processes. In this manuscript, we contribute methodology for data quality assessment by plotting domain-level (conditions (diagnoses), drugs, and procedures) aggregate statistics and concept-level temporal frequencies (i.e., annual prevalence rates of clinical concepts). We detect common temporal patterns in concept frequencies by normalizing and clustering annual concept frequencies using K-means clustering. We apply these methods to the Columbia University Irving Medical Center Observational Medical Outcomes Partnership database. The resulting domain-aggregate and cluster plots show a variety of patterns. We review the patterns found in the condition domain and investigate the processes that shape them. We find that these patterns suggest data quality issues influenced by system-wide factors that affect individual concept frequencies.
Estrategias de aprendizaje en la educación a distancia

This paper reflects on some of the difficulties they face in distance education, with the purpose of deriving ideas that serve to improve the quality of the courses and terminal efficiency in this educational modality. A review of the main theories that support it is made and the context where it is inserted is analyzed, taking as reference the most important documents that govern education in Mexico, in which the goals to be achieved in this area are established. Likewise, the specialized literature on the subject is reviewed and its arguments are analyzed in the light of the authors' experience, which allows to identify strengths and weaknesses in this field, as well as to propose strategies to face problems in this type of courses.
Neural Arithmetic Units

Neural networks can approximate complex functions, but they struggle to perform exact arithmetic operations over real numbers. The lack of inductive bias for arithmetic operations leaves neural networks without the underlying logic needed to extrapolate on tasks such as addition, subtraction, and multiplication. We present two new neural network components: the Neural Addition Unit (NAU), which can learn to add and subtract; and Neural Multiplication Unit (NMU) that can multiply subsets of a vector. The NMU is to our knowledge the first arithmetic neural network component that can learn multiplication of a vector with a large hidden size. The two new components draw inspiration from a theoretical analysis of recent arithmetic components. We find that careful initialization, restricting parameter space, and regularizing for sparsity is important when optimizing the NAU and NMU. Our results, compared with previous attempts, show that the NAU and NMU converges more consistently, have fewer parameters, learns faster, does not diverge with large hidden sizes, obtains sparse and meaningful weights, and can extrapolate to negative and small numbers.
The Potential Use of Social Media on Malaysian Primary Students to Improve Writing

English has always been the second language taught in both primary and secondary schools in Malaysia. Students need to master English for communication, job requirements, advancement in science and technology and many other reasons. One of the most difficult components to master is the writing skill. As it requires knowledge in grammar, spelling, punctuation and vocabulary to produce a good piece of writing, students must be fully equipped with all the essential abilities. Henceforth, this study aims to investigate the potential use of social media in improving the writing skill among primary students. The respondents of this research comprised of 47 ESL primary school students from a rural area in one of the states in Malaysia. The students’ perceptions were measured through a questionnaire comprising a survey and open-ended questions. The finding of this study indicated that most respondents have access to social media and that they perceived social media as beneficial for the ESL educators to incorporate ICT into their teaching repertoire. It is concluded that incorporating social media in the classroom breaks the barriers in learning and converts it into a constructive learning environment.
An efficient construction of key-dependent substitution box based on chaotic sine map

A substitution box is a core component of the popular symmetric-key algorithms. However, the major problem of the conventional substitution boxes is the statistic behavior, which is employed as a fixed-size lookup table. To solve the fixed-size lookup table problem, various substitution box construction methods were proposed with key control, but it is hard to enhance all cryptographic properties, for example, linear and differential probabilities. Thus, chaos is applied for key control in designing robust substitution boxes due to unpredictable and random-like behavior. Moreover, the confusion and diffusion properties of cryptography can be achieved by chaos. This article introduces an efficient construction of a key-dependent substitution box based on the mixing property of the chaotic sine map. The substitution box so constructed has very low differential and linear approximation probabilities. The experimental results confirmed that the proposed method to construct substitution box has acceptable cryptographic properties to resist against various cryptanalysis.
Conservative Uncertainty Estimation By Fitting Prior Networks

Obtaining high-quality uncertainty estimates is essential for many applications of deep neural networks. In this paper, we theoretically justify a scheme for estimating uncertainties, based on sampling from a prior distribution. Crucially, the uncertainty estimates are shown to be conservative in the sense that they never underestimate a posterior uncertainty obtained by a hypothetical Bayesian algorithm. We also show concentration, implying that the uncertainty estimates converge to zero as we get more data. Uncertainty estimates obtained from random priors can be adapted to any deep network architecture and trained using standard supervised learning pipelines. We provide experimental evaluation of random priors on calibration and out-of-distribution detection on typical computer vision tasks, demonstrating that they outperform deep ensembles in practice.
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models

The contrastive divergence algorithm is a popular approach to training energy-based latent variable models, which has been widely used in many machine learning models such as the restricted Boltzmann machines and deep belief nets. Despite its empirical success, the contrastive divergence algorithm is also known to have biases that severely affect its convergence. In this article we propose an unbiased version of the contrastive divergence algorithm that completely removes its bias in stochastic gradient methods, based on recent advances on unbiased Markov chain Monte Carlo methods. Rigorous theoretical analysis is developed to justify the proposed algorithm, and numerical experiments show that it significantly improves the existing method. Our findings suggest that the unbiased contrastive divergence algorithm is a promising approach to training general energy-based latent variable models.