Reviewerly/exHarmony huggingface dataset:
Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model

Mixture of experts (MoE) models are a class of artificial neural networks that can be used for functional approximation and probabilistic modeling. An important class of MoE models is the class of mixture of linear experts (MoLE) models, where the expert functions map to real topological output spaces. Recently, Gaussian gated MoLE models have become popular in applied research. There are a number of powerful approximation results regarding Gaussian gated MoLE models, when the output space is univariate. These results guarantee the ability of Gaussian gated MoLE mean functions to approximate arbitrary continuous functions, and Gaussian gated MoLE models themselves to approximate arbitrary conditional probability density functions. We utilize and extend upon the univariate approximation results in order to prove a pair of useful results for situations where the output spaces are multivariate. We do this by proving a pair of lemmas regarding the combination of univariate MoLE models, which are interesting in their own rights.
Digital Identity and Distributed Ledger Technology: Paving the Way to a Neo-Feudal Brave New World?

While the digital layer of social interaction continues to evolve, the recently proclaimed hopes in the development of digital identity could be both naïf and dangerous. Rather than just asking ourselves how we could digitize existing features of identity management, and corresponding financial transactions on a community or state level, we submit that truly useful and innovative digital identities need to be accompanied by some significant rethinking of the essential basics behind the organization of the world. Once digital technologies leave the realm of purely on-line or deeply local projects, the confrontation with the world of citizenship’s biases and the random distribution of rights and duties precisely on the presumption of the lack of any choice and absolute pre-emption of any disagreement comes into a direct conflict with all the benefits Distributed Ledger Technology purports to enable. Some proponents of Distributed Ledger Technology-based identity systems envisage ‘cloud communities’ with truly ‘self-sovereign’ individuals picking and choosing which communities they belong to. We rather see a clear risk that when implemented at the global scale, digital identity systems could be deeply harmful, reinforcing and amplifying the most repugnant aspects of contemporary citizenship. In this contribution we present a categorization of existing digital identity systems from a governance perspective, and discuss it on basis of three corresponding case studies which allow us to infer opportunities and limitations of Distributed Ledger Technology based identity. Subsequently, we put our findings in the context of existing preconditions of citizenship law, and conclude with a suggestion of a combination of several tests which we propose to avoid the plunge into a neo-feudal ‘brave new world’. We would like to draw attention to the perspective that applying digital identity without rethinking the totalitarian assumptions behind the citizenship status will result in perfecting the current inequitable system, which is a move away from striving towards justice and a more dignified future of humanity. We see the danger that those might be provided with plenty of opportunities who already do not lack such under current governance structures, while less privileged individuals will witness their already weak position becoming increasingly worse.
A Processing-Oriented Investigation of Inflectional Complexity

Due to the typological diversity of their inflectional processes, \textcolor{red}{some languages are intuitively more difficult than other languages. Yet, finding a single measure to quantitatively assess the comparative complexity of an inflectional system proves an exceedingly difficult endeavour. In this paper we propose to investigate the issue from a processing-oriented standpoint, using data processed by a type of recurrent neural network to quantitatively model the dynamic of word processing and learning in different input conditions. We evaluate the relative complexity of a set of typologically different inflectional systems (Greek, Italian, Spanish, German, English and Standard Modern Arabic) by training a Temporal Self-Organizing Map (TSOM), a recurrent variant of Kohonen's Self-Organizing Maps, on a fixed set of verb forms from top-frequency verb paradigms, with no information about the morphosemantic and morphosyntactic content conveyed by the forms. After training, the behavior of each language-specific TSOM is assessed on different tasks, looking at self-organizing patterns of temporal connectivity and functional responses. Our simulations show that word processing is facilitated by maximally contrastive inflectional systems, where verb forms exhibit the earliest possible point of lexical discrimination. Conversely, word learning is favored by a maximally generalizable system, where forms are inferred from the smallest possible number of their paradigm companions. Based on evidence from the literature and our own data, we conjecture that the resulting balance is the outcome of the interaction between form frequency and morphological regularity. Big families of stem-sharing, regularly inflected forms are the productive core of an inflectional system. Such a core is easier to learn but slower to discriminate. In contrast, less predictable verb forms, based on alternating and possibly suppletive stems, are easier to process but are learned by rote. Inflection systems thus strike a balance between these conflicting processing and communicative requirements, while staying within tight learnability bounds, in line with Ackermann and Malouf's Low Conditional Entropy Conjecture. Our quantitative investigation supports a discriminative view of morphological inflection as a collective, emergent system, whose global self-organization rests on a surprisingly small handful of language-independent principles of word coactivation and competition.
Firefly Algorithm With Luciferase Inhibition Mechanism

Firefly algorithm (FA) was proposed by Yang which inspired from communication between fireflies through flash. As an efficient swarm intelligence algorithm, FA has been successfully applied in real-world applications. However, FA employs full attraction model that the method of selecting fireflies is sequential selection, namely each firefly can be attracted to other all fireflies in the worst case. This causes FA is prone to fall into local optima and high computational complexity. Inspired by the principle of firefly glow, an enhanced FA variant is proposed, namely, firefly algorithm with luciferase inhibition mechanism (LiFA), where luciferase inhibition mechanism is introduced to improve the effectiveness of selection. Besides, adaptive attraction model is also proposed to reduce the computational complexity and balance exploration and exploitation. Experiments are conducted on CEC2013 test suite to verify the performance of LiFA. The results show that LiFA has the best performance in some complex functions when compared other advanced FA variants.
Composite Convolutional Neural Network for Noise Deduction

In order to improve the noise reduction performance and the clarity of denoising images, a composite convolutional neural network composed of the convolutional autoencoder network and the feature reconstruction network is proposed.Multiple convolutional layers are added into the autoencoder to extract the image feature information and improve the denoising performance, and the feature reconstruction network is designed to recover the texture and detail information of the image.The cross-connected structure is used to fuse feature information in the convolutional autoencoder network into the feature reconstruction network.Experimental results show that the proposed method has better noise reduction performance than the existing methods for different noise intensity.More texture and detail information could be retained, and the clearer denoising images could be obtained.
LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition

Biomedical Question Answering (QA) aims at providing automated answers to user questions, regarding a variety of biomedical topics.For example, these questions may ask for related to diseases, drugs, symptoms, or medical procedures.Automated biomedical QA systems could improve the retrieval of information necessary to answer these questions.The MEDIQA challenge consisted of three tasks concerning various aspects of biomedical QA.This challenge aimed at advancing approaches to Natural Language Inference (NLI) and Recognizing Question Entailment (RQE), which would then result in enhanced approaches to biomedical QA.Our approach explored a common Transformer-based architecture that could be applied to each task.This approach shared the same pre-trained weights, but which were then fine-tuned for each task using the provided training data.Furthermore, we augmented the training data with external datasets and enriched the question and answer texts using MER, a named entity recognition tool.Our approach obtained high levels of accuracy, in particular on the NLI task, which classified pairs of text according to their relation.For the QA task, we obtained higher Spearman's rank correlation values using the entities recognized by MER.
Factors Influencing Students’ Motivation to Seek Higher Education—A Case Study at a State University in the Czech Republic

The quality of universities in the present era is the subject of ever more discussion in the Czech Republic and the EU. Quality is determined both quantitatively and qualitatively, and ever more importance is placed on ratings by students and graduates. The objective of this article is to identify and evaluate factors influencing the motivation of students to seek higher education and their expectations regarding the quality of a university. The data was obtained using questionnaire-based investigation among the students of a state university (n = 293) and was analysed using descriptive and multivariate statistics. The results identified seven factors that influence the motivation and expectations of students, of which the most significant are quality personnel providing instruction, building social status, development of competencies, and the quality of the education process in relation to employment. The recommendations presented can help other universities to implement suitable personnel marketing and to gain sustainable competitive advantage.
The Correlation between Reading Self-Efficacy and Reading Comprehension

AbstractThe purpose of this research was to find out the correlation between reading self-efficacy and reading comprehension. The research applied correlational study which used quantitative design. The sample was 273 of twelfth grade students of science class at SMAN 5 Bengkulu. The data were collected by using two instruments: (1) the reading self-efficacy questionnaire which was used to measure the students’ reading self-efficacy and (2) reading comprehension test which was used to know the students’ reading comprehension. The correlation between the two variables was calculated by using Pearson Product Moment in SPSS 15. The result shows that there is a significant correlation between reading self-efficacy and reading comprehension. The score of significance level is &lt; 0.05 (0.000 &lt; 0.05) which means that H1 was accepted and H0 was rejected. So, it can be inferred that the higher the reading self-efficacy, the higher the reading comprehension. Keywords: Reading Comprehension; Self-Efficacy
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders

Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases.In this work, we show that we can effectively recover these types of labels using the learned phrase vectors from deep inside-outside recursive autoencoders (DIORA).Specifically, we cluster span representations to induce span labels.Additionally, we improve the model's labeling accuracy by integrating latent code learning into the training procedure.We evaluate this approach empirically through unsupervised labeled constituency parsing.Our method outperforms ELMo and BERT on two versions of the Wall Street Journal (WSJ) dataset and is competitive to prior work that requires additional human annotations, improving over a previous state-of-the-art system that depends on ground-truth part-of-speech tags by 5 absolute F1 points (19% relative error reduction).
dr4pl: A Stable Convergence Algorithm for the 4 Parameter Logistic Model

The 4 Parameter Logistic (4PL) model has been recognized as a major tool to analyze the relationship between doses and responses in pharmacological experiments.A main strength of this model is that each parameter contributes an intuitive meaning enhancing interpretability of a fitted model.However, implementing the 4PL model using conventional statistical software often encounters numerical errors.This paper highlights the issue of convergence failure and presents several causes with solutions.These causes include outliers and a non-logistic data shape, so useful remedies such as robust estimation, outlier diagnostics and constrained optimization are proposed.These features are implemented in a new R package dr4pl (Dose-Response analysis using the 4 Parameter Logistic model) whose code examples are presented as a separate section.Our R package dr4pl is shown to work well for data sets where the traditional dose-response modelling packages drc and nplr fail.
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track

We introduce our system that is submitted to the restricted track of the BEA 2019 shared task on grammatical error correction 1 (GEC).It is essential to select an appropriate hypothesis sentence from the candidates list generated by the GEC model.A re-ranker can evaluate the naturalness of a corrected sentence using language models trained on large corpora.On the other hand, these language models and language representations do not explicitly take into account the grammatical errors written by learners.Thus, it is not straightforward to utilize language representations trained from a large corpus, such as Bidirectional Encoder Representations from Transformers (BERT), in a form suitable for the learner's grammatical errors.Therefore, we propose to finetune BERT on learner corpora with grammatical errors for re-ranking.The experimental results of the W&I+LOCNESS development dataset demonstrate that re-ranking using BERT can effectively improve the correction performance.
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using multiple MARL benchmarks indicates that our method achieves $2-10\times$ lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to achieve better overall performance.
The Utility of Discourse Parsing Features for Predicting Argumentation Structure

Research on argumentation mining from text has frequently discussed relationships to discourse parsing, but few empirical results are available so far.One corpus that has been annotated in parallel for argumentation structure and for discourse structure (RST, SDRT) are the 'argumentative microtexts' (Peldszus and Stede, 2016a).While results on perusing the gold RST annotations for predicting argumentation have been published (Peldszus and Stede, 2016b), the step to automatic discourse parsing has not yet been taken.In this paper, we run various discourse parsers (RST, PDTB) on the corpus, compare their results to the gold annotations (for RST) and then assess the contribution of automatically-derived discourse features for argumentation parsing.After reproducing the state-of-the-art Evidence Graph model from Afantenos et al. (2018) for the microtexts, we find that PDTB features can indeed improve its performance.
Tanbih: Get To Know What You Are Reading

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what is behind a news story.Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet.In addition, we automatically analyze each article in order to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.
Robustness Verification of Tree-based Models

We study the robustness verification problem of tree based models, including random forest (RF) and gradient boosted decision tree (GBDT). Formal robustness verification of decision tree ensembles involves finding the exact minimal adversarial perturbation or a guaranteed lower bound of it. Existing approaches cast this verification problem into a mixed integer linear programming (MILP) problem, which finds the minimal adversarial distortion in exponential time so is impractical for large ensembles. Although this verification problem is NP-complete in general, we give a more precise complexity characterization. We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles the verification problem can be cast as a max-clique problem on a multi-partite boxicity graph. For low dimensional problems when boxicity can be viewed as constant, this reformulation leads to a polynomial time algorithm. For general problems, by exploiting the boxicity of the graph, we devise an efficient verification algorithm that can give tight lower bounds on robustness of decision tree ensembles, and allows iterative improvement and any-time termination. On RF/GBDT models trained on a variety of datasets, we significantly outperform the lower bounds obtained by relaxing the MILP formulation into a linear program (LP), and are hundreds times faster than solving MILPs to get the exact minimal adversarial distortion. Our proposed method is capable of giving tight robustness verification bounds on large GBDTs with hundreds of deep trees.
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD

We study the convergence of Stochastic Gradient Descent (SGD) for strongly convex objective functions. We prove for all $t$ a lower bound on the expected convergence rate after the $t$-th SGD iteration; the lower bound is over all possible sequences of diminishing step sizes. It implies that recently proposed sequences of step sizes at ICML 2018 and ICML 2019 are {\em universally} close to optimal in that the expected convergence rate after {\em each} iteration is within a factor $32$ of our lower bound. This factor is independent of dimension $d$. We offer a framework for comparing with lower bounds in state-of-the-art literature and when applied to SGD for strongly convex objective functions our lower bound is a significant factor $775\cdot d$ larger compared to existing work.
An Improved Whale Optimization Algorithm for Job-Shop Scheduling Based on Quantum Computing

The traditional swarm intelligence algorithms are inefficient and difficult to converge to the optimal solution of the job-shop scheduling problem (JSP).In this paper, an improved whale optimization algorithm (IWOA) is proposed based on quantum computing to solve the discrete JSP.The algorithm was subjected to the analysis on computing complexity, the demonstration of global convergence, and simulation verification on a benchmark example of the JSP.Through the simulation, our algorithm achieved better minimum value, mean value and optimization success rate than traditional swarm intelligence algorithms.The results prove the convergence accuracy and global search ability of the IWOA.
Perceptual Quality Assessment of UHD-HDR-WCG Videos

High Dynamic Range (HDR) Wide Color Gamut (WCG) Ultra High Definition (4K/UHD) content has become increasingly popular recently. Due to the increased data rate, novel video compression methods have been developed to maintain the quality of the videos being delivered to consumers under bandwidth constraints. This has led to new challenges for the development of objective Video Quality Assessment (VQA) models, which are traditionally designed without sufficient calibration and validation based on subjective quality assessment of UHD-HDR-WCG videos. The large performance variations between different consumer HDR TVs, and between consumer HDR TVs and professional HDR reference displays used for content production, further complicates the task of acquiring reliable subjective data that faithfully reflects the impact of compression on UHD-HDR-WCG videos. In this work, we construct a first-of-its-kind video database composed of PQ-encoded UHD-HDR-WCG content, which is subsequently compressed by H.264 and HEVC encoders. We carry out a subjective study on a professional 4K-HDR reference display in a controlled lab environment. We also benchmark representative Full Reference (FR) and No-Reference (NR) objective VQA models against the subjective data to evaluate their performance on compressed UHD-HDR-WCG video content. The database will be made available to the public, subject to content copyright constraints.
A Study of Two CNN Demosaicking Algorithms

Most cameras capture the information of only one color for a given pixel.This results in a mosaicked image that must be interpolated to get three colors at each pixel.The step going from a mosaicked image to a regular RGB image is called demosaicking.This paper studies two recent demosaicking methods based on convolutional neural networks that achieve artifact-free state-of-the-art results: Deep joint demosaicking and denoising by Gharbi et al. and Color image demosaicking via deep residual learning by Tan et al.We show that these methods beat by almost two decibels the best human-crafted methods, while being faster by one order of magnitude.This, arguably, seals the destiny of human-crafted methods on this subject.