Reviewerly/exHarmony huggingface dataset:
Learning Intonation Pattern Embeddings for Arabic Dialect Identification

This article presents a full end-to-end pipeline for Arabic Dialect Identification (ADI) using intonation patterns and acoustic representations. Recent approaches to language and dialect identification use linguistic-aware deep architectures that are able to capture phonetic differences amongst languages and dialects. Specifically, in ADI tasks, different combinations of linguistic features and acoustic representations have been successful with deep learning models. The approach presented in this article uses intonation patterns and hybrid residual and bidirectional LSTM networks to learn acoustic embeddings with no additional linguistic information. Results of the experiments show that intonation patterns for Arabic dialects provide sufficient information to achieve state-of-the-art results on the VarDial 17 ADI dataset, outperforming single-feature systems. The pipeline presented is robust to data sparsity, in contrast to other deep learning approaches that require large quantities of data. We conjecture on the importance of sufficient information as a criterion for optimality in a deep learning ADI task, and more generally, its application to acoustic modeling problems. Small intonation patterns, when sufficient in an information-theoretic sense, allow deep learning architectures to learn more accurate speech representations.
3D shape measurement of shiny surfaces based on optimized combination of fringe patterns of different intensity

Abstract Image saturation is a challenging problem for three-dimensional shape measurement of objects with shiny surfaces using fringe projection profilometry technology. This paper presents a new method based on projection of multiple fringe patterns, providing an effective solution to the problem. First, a set of phase shifted sinusoidal fringe patterns with different intensity levels are projected to the object of interest, and the reflections are captured by the camera. With the captured images a set of masks are created, which are able to pick up the unsaturated data from the captured images. The unsaturated data are then combined based on maximal Signal-to-noise Ratio (SNR) scenario, and the combined images are used to retrieve the phase information. Comparing to existing methods, the proposed technique is advantageous by improved measurement accuracy due to the improved SNR. The performance of the proposed method is verified by the experiments.
Deep Metric Learning with Spherical Embedding

Deep metric learning has attracted much attention in recent years, due to seamlessly combining the distance metric learning and deep neural network. Many endeavors are devoted to design different pair-based angular loss functions, which decouple the magnitude and direction information for embedding vectors and ensure the training and testing measure consistency. However, these traditional angular losses cannot guarantee that all the sample embeddings are on the surface of the same hypersphere during the training stage, which would result in unstable gradient in batch optimization and may influence the quick convergence of the embedding learning. In this paper, we first investigate the effect of the embedding norm for deep metric learning with angular distance, and then propose a spherical embedding constraint (SEC) to regularize the distribution of the norms. SEC adaptively adjusts the embeddings to fall on the same hypersphere and performs more balanced direction update. Extensive experiments on deep metric learning, face recognition, and contrastive self-supervised learning show that the SEC-based angular space learning strategy significantly improves the performance of the state-of-the-art.
Linked Data Creation with ExcelRDF

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.
Applications of Common Entropy for Causal Inference

We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the latent variable. To efficiently compute common entropy, we propose an iterative algorithm that can be used to discover the trade-off between the entropy of the latent variable and the conditional mutual information of the observed variables. We show two applications of common entropy in causal inference: First, under the assumption that there are no low-entropy mediators, it can be used to distinguish causation from spurious correlation among almost all joint distributions on simple causal graphs with two observed variables. Second, common entropy can be used to improve constraint-based methods such as PC or FCI algorithms in the small-sample regime, where these methods are known to struggle. We propose a modification to these constraint-based methods to assess if a separating set found by these algorithms is valid using common entropy. We finally evaluate our algorithms on synthetic and real data to establish their performance.
Pragmatic Issue-Sensitive Image Captioning

Image captioning systems need to produce texts that are not only true but also relevant in that they are properly aligned with the current issues. For instance, in a newspaper article about a sports event, a caption that not only identifies the player in a picture but also comments on their ethnicity could create unwanted reader reactions. To address this, we propose Issue-Sensitive Image Captioning (ISIC). In ISIC, the captioner is given a target image and an issue, which is a set of images partitioned in a way that specifies what information is relevant. For the sports article, we could construct a partition that places images into equivalence classes based on player position. To model this task, we use an extension of the Rational Speech Acts model. Our extension is built on top of state-of-the-art pretrained neural image captioners and explicitly uses image partitions to control caption generation. In both automatic and human evaluations, we show that these models generate captions that are descriptive and issue-sensitive. Finally, we show how ISIC can complement and enrich the related task of Visual Question Answering.
Convergence of meta-learning with task-specific adaptation over partial parameters

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training. A more efficient algorithm ANIL (which refers to almost no inner loop) was proposed recently by Raghu et al. 2019, which adapts only a small subset of parameters in the inner loop and thus has substantially less computational cost than MAML as demonstrated by extensive experiments. However, the theoretical convergence of ANIL has not been studied yet. In this paper, we characterize the convergence rate and the computational complexity for ANIL under two representative inner-loop loss geometries, i.e., strongly-convexity and nonconvexity. Our results show that such a geometric property can significantly affect the overall convergence performance of ANIL. For example, ANIL achieves a faster convergence rate for a strongly-convex inner-loop loss as the number $N$ of inner-loop gradient descent steps increases, but a slower convergence rate for a nonconvex inner-loop loss as $N$ increases. Moreover, our complexity analysis provides a theoretical quantification on the improved efficiency of ANIL over MAML. The experiments on standard few-shot meta-learning benchmarks validate our theoretical findings.
A new partial differential equation for image inpainting

A considerable interest in the inpainting problem have attracted many researchers in applied mathematics community. In fact in the last decade, nonlinear high order partial dierential equations have payed a central role in high quality inpainting developments. In this paper, we propose a technique for inpainting that combines an anisotropic diusion process with an edge-corner enhancing shock ltering. This technique makes use of a partial differential equation that is based on a nonlinear structure tensor which increases the accuracy and robustness of the coupled diusion and shock ltering. A methodology of partition and adjustment is used to estimate the contrast parameters that control the strength of the diffusivity functions. We focus on restoring large missing regions in grey scale images containing complex geometries parts. Our model is extended to a three dimensional case, where numerical experimentations were carried out on lling brain multiple sclerosis lesions in medical images. The efficiency and the competitiveness of the proposed algorithm is numerically compared to other approaches on both synthetic and real images.
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-theart (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity.We use ENAS to perform a microlevel search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM.We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT).In contrast to prior work applying ENAS to NLP tasks, our results are mixed -we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.
Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation

While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.
Generating Adequate Distractors for Multiple-Choice Questions

This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ). Our method is a combination of part-of-speech tagging, named-entity tagging, semantic-role labeling, regular expressions, domain knowledge bases, word embeddings, word edit distance, WordNet, and other algorithms. We use the US SAT (Scholastic Assessment Test) practice reading tests as a dataset to produce QAPs and generate three distractors for each QAP to form an MCQ. We show that, via experiments and evaluations by human judges, each MCQ has at least one adequate distractor and 84\% of MCQs have three adequate distractors.
Improving the Performance of Text Sentiment Analysis using Deep Convolutional Neural Network Integrated with Hierarchical Attention Layer

Sentiment analysis is considered as one of the most essential tasks in the field of natural language processing and cognitive science. In order to enhance the performance of sentiment analysis techniques, it is necessary to not only classify the sentences based on their sentimental labels but also to extract the informative words that contribute to the classification decision. In this regard, deep neural networks based on the attention mechanism have achieved considerable progress in recent years. However, there is still a limited number of studies on attention mechanisms for text classification and especially sentiment analysis. To fill this lacuna, a Convolution Neural Network (CNN) integrated with attention layer is presented in this paper that is able to extract informative words and assign them higher weights based on the context. In the attention layer, the proposed model employs a context vector and tries to measure the importance of a word as the similarity between the context vector and word vector. Then, by integrating the new vectors obtained from the attention layer into sentence vectors, the new generated vectors are used for classification. In order to verify the performance of the proposed model, various experiments were conducted on the Stanford datasets. Based on the results of the experiments, the proposed model not only significantly outperforms other existing studies but also is able to consider the context to extract the informative words which can be considered as a value in analysis and application.
An end-to-end stereo matching algorithm based on improved convolutional neural network

Deep end-to-end learning based stereo matching methods have achieved great success as witnessed by the leaderboards across different benchmarking datasets. Depth information in stereo vision systems are obtained by a dense and accurate disparity map, which is computed by a robust stereo matching algorithm. However, previous works adopt network layer with the same size to train the feature parameters and get an unsatisfactory efficiency, which cannot be satisfied for the real scenarios by existing methods. In this paper, we present an end-to-end stereo matching algorithm based on "downsize" convolutional neural network (CNN) for autonomous driving scenarios. Firstly, the road images are feed into the designed CNN to get the depth information. And then the "downsize" full-connection layer combined with subsequent network optimization is employed to improve the accuracy of the algorithm. Finally, the improved loss function is utilized to approximate the similarity of positive and negative samples in a more relaxed constraint to improve the matching effect of the output. The loss function error of the proposed method for KITTI 2012 and KITTI 2015 datasets are reduced to 2.62 and 3.26% respectively, which also reduces the runtime of the proposed algorithm. Experimental results illustrate that the proposed end-to-end algorithm can obtain a dense disparity map and the corresponding depth information can be used for the binocular vision system in autonomous driving scenarios. In addition, our method also achieves better performance when the size of the network is compressed compared with previous methods.
Instance-based Generalization in Reinforcement Learning

Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels. Understanding the generalization properties of RL is one of the challenges of modern machine learning. Towards this goal, we analyze policy learning in the context of Partially Observable Markov Decision Processes (POMDPs) and formalize the dynamics of training levels as instances. We prove that, independently of the exploration strategy, reusing instances introduces significant changes on the effective Markov dynamics the agent observes during training. Maximizing expected rewards impacts the learned belief state of the agent by inducing undesired instance specific speedrunning policies instead of generalizeable ones, which are suboptimal on the training set. We provide generalization bounds to the value gap in train and test environments based on the number of training instances, and use insights based on these to improve performance on unseen levels. We propose training a shared belief representation over an ensemble of specialized policies, from which we compute a consensus policy that is used for data collection, disallowing instance specific exploitation. We experimentally validate our theory, observations, and the proposed computational solution over the CoinRun benchmark.
Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis

Sentiments in opinionated text are often determined by both aspects and target words (or targets). We observe that targets and aspects interrelate in subtle ways, often yielding conflicting sentiments. Thus, a naive aggregation of sentiments from aspects and targets treated separately, as in existing sentiment analysis models, impairs performance. We propose Octa, an approach that jointly considers aspects and targets when inferring sentiments. To capture and quantify relationships between targets and context words, Octa uses a selective self-attention mechanism that handles implicit or missing targets. Specifically, Octa involves two layers of attention mechanisms for, respectively, selective attention between targets and context words and attention over words based on aspects. On benchmark datasets, Octa outperforms leading models by a large margin, yielding (absolute) gains in accuracy of 1.6% to 4.3%.
Even in a Pandemic, Sunlight is the Best Disinfectant: COVID-19 and Global Freedom of Expression

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Understanding Deep Architecture with Reasoning Layer

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrolled, and used as a specialized layer in the deep architecture, which can be trained end-to-end with other neural components. Although such hybrid deep architectures have led to many empirical successes, the theoretical foundation of such architectures, especially the interplay between algorithm layers and other neural layers, remains largely unexplored. In this paper, we take an initial step towards an understanding of such hybrid deep architectures by showing that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Furthermore, our analysis matches closely our experimental observations under various conditions, suggesting that our theory can provide useful guidelines for designing deep architectures with reasoning layers.
GENERALIZED FUZZY LINGUISTIC CUBIC B-SPLINE CURVE MODEL FOR UNCERTAINTY FUZZY LINGUISTIC DATA

Fuzzy linguistic data is difficult to analyze. It becomes a problem to describe the data in the form of smooth and continuous generic figure. Therefore, the generalization of the problems of linguistic data in the form of curves and surfaces requires a new model such as fuzzy linguistic B-spline by using fuzzy linguistic approach. This paper discusses an approach of fuzzy linguistic point relation with three types of linguistic functions that generate new B-spline curve model.
Comparing Collaboration Fidelity between VR, MR and Video Conferencing Systems: The Effects of Visual Communication Media Fidelity on Collaboration

Video/audio conferencing systems have been used extensively for remote collaboration over many years. Recently, virtual and mixed reality (VR/MR) systems have started to show great potential as communication media for remote collaboration. Prior studies revealed that the creation of common ground between discourse participants is crucial for collaboration and that grounding techniques change with the communication medium. However, it is difficult to find previous research that compares VR and MR communication system performances with video conferencing systems regarding the creation of common ground for collaborative problem solving. On the other hand, prior studies have found that display fidelity and interaction fidelity had significant effects on performance-intensive individual tasks in virtual reality. Fidelity in VR can be defined as the degree of objective accuracy with which the real-world is represented by the virtual world. However, to date, fidelity for collaborative tasks in VR/MR has not been defined or studied much. In this paper, we compare five different communication media for the establishment of common ground in collaborative problem-solving tasks: Webcam, headband camera, VR, MR, and audio-only conferencing systems. We analyzed these communication media with respect to collaborative fidelity components which we defined. For the experiments, we utilized two different types of collaborative tasks: a 2D Tangram puzzle and a 3D Soma cube puzzle. The experimental results show that the traditional Webcam performed better than the other media in the 2D task, while the headband camera performed better in the 3D task. In terms of collaboration fidelity, these results were somehow predictable, although there was a little difference between our expectations and the results.
Pedagogical Potential Of Contemporary Art In The Spiritual And Moral Education Of Schoolchildren

The article is devoted to the problem of realizing the educational potential of contemporary art by teachers in Russian comprehensive schools. The study was conducted with the aim to analyze and assess the current situation in Russian schools related to employing contemporary art as a meaningful basis for the spiritual and moral education of schoolchildren. The focus is on specific perception of contemporary art by schoolteachers, its place and role in their professional activity. The results of online survey among teachers of several Russian regions are presented. The given results make it possible to judge teachers̕ professional and personal attitude to contemporary art and perception of its role in the educational process, to identify the level of teachers competence in the field of contemporary art and culture, to find out the teachers’ need for scholarly-based recommendations on the use of contemporary art to enhance spiritual and moral education of students. The research suggests reasons for teachers not to use works of modern art to address the problems of spiritual and moral education of schoolchildren. It is concluded that, despite the generally positive or neutral attitude of teachers to contemporary art, the educational potential of it in the practice of spiritual and moral education of schoolchildren is not being fully realized, because of teachers’ insufficient level of knowledge in the field of contemporary art.