Reviewerly/exHarmony huggingface dataset:
Laughter Synthesis: Combining Seq2seq Modeling with Transfer Learning

Despite the growing interest for expressive speech synthesis, synthesis of nonverbal expressions is an under-explored area. In this paper we propose an audio laughter synthesis system based on a sequence-to-sequence TTS synthesis system. We leverage transfer learning by training a deep learning model to learn to generate both speech and laughs from annotations. We evaluate our model with a listening test, comparing its performance to an HMM-based laughter synthesis one and assess that it reaches higher perceived naturalness. Our solution is a first step towards a TTS system that would be able to synthesize speech with a control on amusement level with laughter integration.
Gouy phase of type-I SPDC-generated biphotons

We consider a double Gaussian approximation to describe the wavefunction of twin photons (also called a biphoton) created in a nonlinear crystal via a type-I spontaneous parametric downconversion (SPDC) process. We find that the wavefunction develops a Gouy phase while it propagates, being dependent of the two-photon correlation through the Rayleigh length. We evaluate the covariance matrix and show that the logarithmic negativity, useful in quantifying entanglement in Gaussian states, although Rayleigh-dependent, does not depend on the propagation distance. In addition, we show that the two-photon entanglement can be connected to the biphoton Gouy phase as these quantities are Rayleigh-length-related. Then, we focus the double Gaussian biphoton wavefunction using a thin lens and calculate a Gouy phase that is in reasonable agreement with the experimental data of D. Kawase et al. published in Ref. [1].
Quantum Phase Estimation Algorithm for Finding Polynomial Roots

Abstract Quantum algorithm is an algorithm for solving mathematical problems using quantum systems encoded as information, which is found to outperform classical algorithms in some specific cases. The objective of this study is to develop a quantum algorithm for finding the roots of n th degree polynomials where n is any positive integer. In classical algorithm, the resources required for solving this problem increase drastically when n increases and it would be impossible to practically solve the problem when n is large. It was found that any polynomial can be rearranged into a corresponding companion matrix, whose eigenvalues are roots of the polynomial. This leads to a possibility to perform a quantum algorithm where the number of computational resources increase as a polynomial of n. In this study, we construct a quantum circuit representing the companion matrix and use eigenvalue estimation technique to find roots of polynomial.
Efficient Supervision for Robot Learning Via Imitation, Simulation, and Adaptation

Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation.
Rapid communication Likelihood theory in a quantum world: Tests with quantum coins and computers

By repeated trials, one can determine the fairness of a classical coin with a confidence which grows with the number of trials. A quantum coin can be in a superposition of heads and tails and its state is most generally a density matrix. Given a string of qubits representing a series of trials, one can measure them individually and determine the state with a certain confidence. We show that there is an improved strategy which measures the qubits after entangling them, which leads to a greater confidence. This strategy is demonstrated on the simulation facility of IBM quantum computers.
Exploiting Game Theory for Analysing Justifications

Abstract Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning; its most central concept is a justification: an explanation why a property holds (or does not hold) in a model. In this paper, we continue the study of justification theory by means of three major contributions. The first is studying the relation between justification theory and game theory. We show that justification frameworks can be seen as a special type of games. The established connection provides the theoretical foundations for our next two contributions. The second contribution is studying under which condition two different dialects of justification theory (graphs as explanations vs trees as explanations) coincide. The third contribution is establishing a precise criterion of when a semantics induced by justification theory yields consistent results. In the past proving that such semantics were consistent took cumbersome and elaborate proofs. We show that these criteria are indeed satisfied for all common semantics of logic programming.
Active Online Learning in the Binary Perceptron Problem

The binary perceptron is the simplest artificial neural network formed by $N$ input units and one output unit, with the neural states and the synaptic weights all restricted to $\pm 1$ values. The task in the teacher--student scenario is to infer the hidden weight vector by training on a set of labeled patterns. Previous efforts on the passive learning mode have shown that learning from independent random patterns is quite inefficient. Here we consider the active online learning mode in which the student designs every new Ising training pattern. We demonstrate that it is mathematically possible to achieve perfect (error-free) inference using only $N$ designed training patterns, but this is computationally unfeasible for large systems. We then investigate two Bayesian statistical designing protocols, which require $2.3 N$ and $1.9 N$ training patterns, respectively, to achieve error-free inference. If the training patterns are instead designed through deductive reasoning, perfect inference is achieved using $N\!+\!\log_{2}\!N$ samples. The performance gap between Bayesian and deductive designing strategies may be shortened in future work by taking into account the possibility of ergodicity breaking in the version space of the binary perceptron.
Peripheral Blood Leukocyte Image Segmentation Based on Deep Convolutional Neural Network

Counting and analysis of leukocyte on peripheral blood smears have great significance to assist doctors’ pathological diagnosis. Thus, accurate and automatic segmentation of leukocyte from blood smear images is a fundamental task. To achieve this task, we proposed an end-to-end peripheral blood leukocyte segmentation method based on deep convolutional neural networks, where the encoder-decoder network is its basic structure. Specifically, the proposed method first utilizes the feature encoder to extract multi-scale leukocyte features. Then, the context-aware feature decoder is introduced to reconstruct the segmentation mask of leukocyte. Finally, each layer generates a prediction map in the decoder module, and all these prediction maps are averaged to be the final prediction map. To demonstrate the effectiveness of the proposed method, we performed qualitative and quantitative comparisons with several existing methods on two publicly available datasets and a private dataset. Experimental results validate that the proposed method is superior for peripheral blood leukocyte image segmentation in terms of accuracy and robustness.
THE USE OF BOARD GAME IN TEACHING SPEAKING TO YOUNG LEARNERS

The purpose of this study was to present a literature review of the advantages of using board game media in teaching speaking to young learners. The researcher examined seventeen articles and theses related to the use of media board games in teaching speaking. The results of the literature review were used to identify the advantages of using board game media in teaching speaking especially to young learners. The advantages of using board game in teaching speaking to young learners are as followed board game provides a fun learning, attracts the students’ attention to participate actively, and helps the students to enjoy communicating verbally. The results of this study were expected to be able to be used as an information and reference material for teachers or researchers for further research. In short, the use of board games in teaching speaking to young learners can be a new strategy that can be used by English teachers.
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

We consider the classical setting of optimizing a nonsmooth Lipschitz continuous convex function over a convex constraint set, when having access to a (stochastic) first-order oracle (FO) for the function and a projection oracle (PO) for the constraint set. It is well known that to achieve ϵ-suboptimality in high-dimensions, Θ(ϵ−2) FO calls are necessary. This is achieved by the projected subgradient method (PGD). However, PGD also entails O(ϵ−2) PO calls, which may be computationally costlier than FO calls (e.g. nuclear norm constraints). Improving this PO calls complexity of PGD is largely unexplored, despite the fundamental nature of this problem and extensive literature. We present first such improvement. This only requires a mild assumption that the objective function, when extended to a slightly larger neighborhood of the constraint set, still remains Lipschitz and accessible via FO. In particular, we introduce MOPES method, which carefully combines Moreau-Yosida smoothing and accelerated first-order schemes. This is guaranteed to find a feasible ϵ-suboptimal solution using only O(ϵ−1) PO calls and optimal O(ϵ−2) FO calls. Further, instead of a PO if we only have a linear minimization oracle (LMO, a la Frank-Wolfe) to access the constraint set, an extension of our method, MOLES, finds a feasible ϵ-suboptimal solution using O(ϵ−2) LMO calls and FO calls—both match known lower bounds, resolving a question left open since White (1993). Our experiments confirm that these methods achieve significant speedups over the state-of-the-art, for a problem with costly PO and LMO calls.
New Features in the Second Version of the Cadabra Computer Algebra System

In certain scientific domains, there is a need for tensor operations. To facilitate tensor computations, computer algebra systems are employed. In our research, we have been using Cadabra as the main computer algebra system for several years. Recently, an operable second version of this software was released. In this version, a number of improvements were made that can be regarded as revolutionary ones. The most significant improvements are the implementation of component computations and the change in the ideology of the Cadabra’s software mechanism as compared to the first version. This paper provides a brief overview of the key improvements in the Cadabra system.
Improving Reporting of Tumor Size in Synoptic Reports

Context.— Tumor size is an important prognostic feature in many synoptic reports. The best format to report this feature is not clearly defined. Objective.— To define formatting features that impact the significance of tumor size. Design.— We reviewed multiple formatting features of tumor size in synoptic reports and correlated them with size distribution, reproducibility, and other pathologic features. Results.— Reporting tumors in millimeters rather than centimeters was more precise because of reduced rounding error and was significantly more reproducible (P = .01). Tumor sizes where the pathologist was concerned that the size may be underestimated are associated with significantly higher tumor N stage than tumors of similar size that are not so identified. Reported tumor sizes in multifocal tumors are also associated with significantly higher N stage than unifocal tumors of the same size. Conclusions.— Tumor sizes should be reported in millimeters, and when tumors are reported as either “at least” a specific size or as “multifocal” this information should also be recorded because these sizes likely underestimate the true biologic potential of the tumor.
Assessing Capsule Networks with Biased Data

Machine learning based methods achieves impressive results in object classification and detection. Utilizing representative data of the visual world during the training phase is crucial to achieve good performance with such data driven approaches. However, it not always possible to access bias-free datasets thus, robustness to biased data is a desirable property for a learning system. Capsule Networks have been introduced recently and their tolerance to biased data has received little attention. This paper aims to fill this gap and proposes two experimental scenarios to assess the tolerance to imbalanced training data and to determine the generalization performance of a model with unfamiliar affine transformations of the images. This paper assesses dynamic routing and EM routing based Capsule Networks and proposes a comparison with Convolutional Neural Networks in the two tested scenarios. The presented results provide new insights into the behaviour of capsule networks.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling

Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervised domain adaptation algorithm to solve the cross-lingual text labeling problems.Experiments on cross-lingual document classification and NER demonstrate the proposed domain adaptation approach advances the stateof-the-art results by a large margin.Specifically, we improve MDD by efficiently optimizing the margin loss on the source domain via Virtual Adversarial Training (VAT).This bridges the gap between theory and the loss function used in the original work Zhang et al. (2019b), and thereby significantly boosts the performance.Our numerical results also indicate that VAT can remarkably improve the generalization performance of both domains for various domain adaptation approaches.
NLP Summarization: Abstractive Neural Headline Generation Over A News Articles Corpus

Most of NLP research fields (Translation, Classification, Dialogue Systems ...) have been revolutionized by the rise of deep learning methods, which rely on the new dense and low-dimensional feature representation. We present in this article the basic training techniques of Word Embeddings as well as the recent works on Abstractive Neural Summarizers. We also introduce our trained French Word Embeddings, further used as the embedding layer to implement our baseline French Neural Summarizer for the headline generation task, using the RNN (Recurrent Neural Network) Encoder-Decoder architecture.
Context-Aware Approach to Intelligent Decision Support Based on User Digital Traces

A context-aware approach to intelligent decision support based on user digital traces is proposed. The concept of human digital life with regard to intelligent decision support is discussed. The aims of addressing this concept in diverse domains are clarified and approaches to modelling human digital life are identified. In the proposed approach, digital traces serve as a source of information to reveal user preferences and decision-making behaviour. Perspectives on decision support based on user digital traces are developed. The research outcomes are the specification of requirements to intelligent decision support based on user digital traces, the principles, conceptual framework and information model of such support. The principles form the basis for the conceptual framework of intelligent decision support based on user digital traces. Components of the conceptual model are user profiles; a user digital life model that structures information containing in the digital traces; group patterns that describe preferences and decision-making behavior shared by a user group; and a decision maker ontology. The information model defines information flows between the framework’s components, identifies tasks that require solutions to implement the framework and offers techniques for this. The novelties of the research are applying the concept of human digital life to intelligent decision support and context-dependent ontological inference of the type of user as a decision-maker, which determines a group of users sharing their preferences and behaviours with the active user, to predict a recommended decision. The paper contributes to the areas of modelling human digital life and intelligent decision support.
Learning a Twofold Siamese Network for RGB-T Object Tracking

Visual object tracking works as a key component for many instrumentation and measurement applications such as UAV systems, optical tracking and measuring systems. This paper investigates how to implement accurate RGB-T tracking by integrating the complementary information from RGB and thermal sources. Inspired by the success of Siamese networks in the RGB tracking field, we design a twofold Siamese network for RGB-T tracking, which is composed of an RGB branch and a thermal branch. Each branch is a Siamese network, which can be utilized to compute similarities between the search image and the exemplar image. The parameters in the RGB branch are kept the same as SiamFC. The thermal branch is initialized with parameter weights of the trained network in SiamFC and fine-tuned with constructed thermal image pairs to better capture the target characteristics in the thermal data. Two criteria are further proposed to measure the confidence degrees of response maps obtained by these two modalities. The final response map is computed by adaptively fusing them according to their confidence degrees. The maximum location on the final response map is identified as the target location, and the target scale is obtained through a simple multi-scale search. Experiments on the recently public benchmark RGB-T234 demonstrate the effectiveness of our proposed method when compared to other state-of-the-art trackers. The high performance of our proposed trakcer makes it easy to be implemented in embedded system.
Market power and cost efficiency in the African banking industry

Purpose In this study, we test the so-called “Quiet Life Hypothesis” (QLH), which postulates that banks with market power are less efficient. Design/methodology/approach We employ instrumental variable Ordinary Least Squares, Fixed Effects, Tobit and Logistic regressions. The empirical evidence is based on a panel of 162 banks consisting of 42 African countries for the period 2001–2011. There is a two-step analytical procedure. First, we estimate Lerner indices and cost efficiency scores. Then, we regress cost efficiency scores on Lerner indices contingent on bank characteristics, market features and the unobserved heterogeneity. Findings The empirical evidence does not support the QLH because market power is positively associated with cost efficiency. Originality/value Owing to data availability constraints, this is one of the few studies to test the QLH in African banking.
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models

Abstract Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inference on edge probabilities. Although these solutions are routinely implemented, there is a lack of formal approaches to test if a given external node partition is in line with the endogenous clustering structure encoding stochastic equivalence patterns among the nodes in the network. To fill this gap, we develop a formal Bayesian testing procedure which relies on the calculation of the Bayes factor between a stochastic block model with known grouping structure defined by the exogenous node partition and an infinite relational model that allows the endogenous clustering configurations to be unknown, random and fully revealed by the block–connectivity patterns in the network. A simple Markov chain Monte Carlo method for computing the Bayes factor and quantifying uncertainty in the endogenous groups is proposed. This strategy is evaluated in simulations, and in applications studying brain networks of Alzheimer’s patients.
Recurrent memory networks: modeling long short-term user preferences for session-based recommendation

In many online systems where recommendations are applied, interactions between a user and the system are usually organized into sessions. This paper studies how to model user preferences in session-based recommendation systems. Existing studies have either assumed that sessions are independent from each other and ignore long-term information from historical sessions, or treat the users short-term preferences in a session as static, which cannot fully characterize user behavior in practical scenarios. Thus, we propose the recurrent memory network (RMN), which is an RNN-based framework that unifies the users long-term and short-term preferences in session-based recommendation. The key component of the proposed RMN is preference memory, which stores a users long-term interests. In addition, in the RMN, we design an intra-session memory reader and inter-session memory writer to facilitate explicit characterization of short-term (i.e., within a session) user preferences variation and long-term (i.e., cross-session) user preference transfer, respectively. The results obtained in extensive experiments on real-world datasets for movie and job recommendations demonstrate that the proposed RMN achieves substantial gains over state-of-the-art baselines.
El uso de las TIC digitales por parte del personal docente y su adecuación a los modelos vigentes

In the article we present a study in which it is intended to verify the importance and adequacy of the use of information and communication technologies (ICT) in the school environment for its greater adaptation to reality. This question is reflected in the general objective of the same, which focused on determining the use that teachers make of new technologies in the classroom and their impact on the teaching-learning process of their students, taking into account the current weaknesses. on the pedagogical bases used by some of the most recent studies on the use and optimization of digital ICT in the classroom. The study is framed in a quantitative, non-experimental, descriptive and correlational design. 100 teachers of Compulsory Secondary Education (ESO) from institutes of Coimbra (Portugal) participated in the study. An ad hoc questionnaire was developed to discover the use and frequency of new technologies in the classroom by teachers. The data collected in the questionnaires were analyzed using the statistical package Statistical Package for the Social Sciences. The main conclusions confirm that there is still a need for an in-depth debate on the epistemological foundations implicit in the use of new digital tools on whether ICT, as an appropriate teaching tool, according to current educational models, in terms of time-cost for better student performance and academic success. In fact, the debate is more than ever alive, new technologies were increasingly inserted in the educational field, during COVID-19, which also implied the suspension of classroom teaching activities that led to the use of new technologies in the classroom. class immediately.