Reviewerly/exHarmony huggingface dataset:
Musical Control Gestures in Mobile Handheld Devices: Design Guidelines Informed by Daily User Experience

Mobile handheld devices, such as smartphones and tablets, have become some of the most prominent ubiquitous terminals within the information and communication technology landscape. Their transformative power within the digital music domain changed the music ecosystem from production to distribution and consumption. Of interest here is the ever-expanding number of mobile music applications. Despite their growing popularity, their design in terms of interaction perception and control is highly arbitrary. It remains poorly addressed in related literature and lacks a clear, systematized approach. In this context, our paper aims to provide the first steps towards defining guidelines for optimal sonic interaction design practices in mobile music applications. Our design approach is informed by user data in appropriating mobile handheld devices. We conducted an experiment to learn links between control gestures and musical parameters, such as pitch, duration, and amplitude. A twofold action—reflection protocol and tool-set for evaluating the aforementioned links—are also proposed. The results collected from the experiment show statistically significant trends in pitch and duration control gesture mappings. On the other hand, amplitude appears to elicit a more diverse mapping approach, showing no definitive trend in this experiment.
Towards Semantics-Enhanced Pre-Training: Can Lexicon Definitions Help Learning Sentence Meanings?

Self-supervised pre-training techniques, albeit relying on large amounts of text, have enabled rapid growth in learning language representations for natural language understanding. However, as radically empirical models on sentences, they are subject to the input data distribution, inevitably incorporating data bias and reporting bias, which may lead to inaccurate understanding of sentences. To address this problem, we propose to adopt a human learner's approach: when we cannot make sense of a word in a sentence, we often consult the dictionary for specific meanings; but can the same work for empirical models? In this work, we try to inform the pre-trained masked language models of word meanings for semantics-enhanced pre-training. To achieve a contrastive and holistic view of word meanings, a definition pair of two related words is presented to the masked language model such that the model can better associate a word with its crucial semantic features. Both intrinsic and extrinsic evaluations validate the proposed approach on semantics-orientated tasks, with an almost negligible increase of training data.
Probabilistic Graph Reasoning for Natural Proof Generation

In this paper, we investigate the problem of reasoning over natural language statements.Prior neural based approaches do not explicitly consider the inter-dependency among answers and their proofs.In this paper, we propose PROBR, a novel approach for joint answer prediction and proof generation.PROBR defines a joint probabilistic distribution over all possible proof graphs and answers via an induced graphical model.We then optimize the model using variational approximation on top of neural textual representation.Experiments on multiple datasets under diverse settings (fully supervised, few-shot and zero-shot evaluation) verify the effectiveness of PROBR, e.g., achieving 10%-30% improvement on QA accuracy in few/zero-shot evaluation.Our codes and models can be found at https://github.com/changzhisun/PRobr/.
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children's ability to understand others' thoughts, feelings, and desires (or "mindreading").We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating.We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems.To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 -a new corpus of children's performance on tests of mindreading, consisting of 10,320 question-answer pairs.We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points.Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems.The taskspecific augmentations generally outperform task-agnostic augmentations.Automatic augmentations based on vectors (GloVe, FastText) perform the worst.We find that systems trained on MIND-CA generalize well to UK-MIND-20.We demonstrate that data augmentation strategies also improve the performance on unseen data.
A Traceability Dataset for Open Source Systems

Software engineers use requirement-to-method trace matrices to indicate the methods implementing different system requirements. Requirement-to-method trace matrices pinpoint the exact method implementing each requirement, which facilitates software maintenance and bug fixing. The code structure of a system can be used to make predictions about requirement-to-method traces. In this paper, we present a data set documenting the requirement-to-method traces as well as the code structure (methods, variables, etc.) for four open source systems. The code structure was obtained by parsing the systems under consideration and extracting the methods, variables, etc. The requirement-to-method trace matrices were obtained by resorting to students as well as to the original developers of the systems who provided us with the list of requirement-to-method traces.
The Application of Internet Big Data and Support Vector Machine in Risk Warning

Abstract With the strengthening of macro-control of the real estate industry and the intensification of market competition, it is of great significance for the steady development of the industry to establish an accurate and effective early-warning mechanism for enterprise financing risk. Taking the A-share real estate listed companies in China in 2019 as target, this paper collects financial information of the relevant companies from 2010 to 2019, supplemented the risk sample data from 2005 to 2010, and reduced classification imbalance by removing discrete points and using SMOTE. When the financing risk evaluation system of listed real estate companies is constructed with capital as the entry point, the stochastic forest algorithm is used to finally select five important characteristic dimensions, namely, current ratio, equity financing ratio, operating income, current liability ratio and receivable turnover ratio. This paper establishes SVM early warning model, PSO-GA-BP optimization model and KNN early warning model to predict financing risks of real estate companies. It is concluded that, by comparing their applicability and advantages and disadvantages, SVM after feature screening and sample processing has better performance in the real estate financing risk early warning, which can provide some references for enterprise decision makers, investors and regulatory authorities.
Features of virtual communication as a new field of communication

The Internet has brought into our life previously non-existent ways of communication and new forms of language existence. With the growing popularity of social networks, the functioning of the English language has attracted the interest of many linguists. Modernization of the linguistic personality takes place in conjunction with the development of a virtual picture of the world, which reproduces life in the vastness of the Internet. Values replace each other. If earlier language education was popular, today it is computer education. That is why a tendency has arisen to explain in a simplified language, which contradicts the norms of traditional communication. Moreover, the leading role in virtual communication belongs to the English language, since the birthplace of the Internet and the main supplier of network terminology is English-speaking America.
Improved Differentially Private Euclidean Distance Approximation

This work shows how to privately and more accurately estimate Euclidean distance between pairs of vectors. Input vectors x and y are mapped to differentially private sketches $x'$ and $y'$, from which one can estimate the distance between x and y. Our estimator relies on the Sparser Johnson-Lindenstrauss constructions by Kane & Nelson (Journal of the ACM 2014), which for any 0<α,β<1/2 have optimal output dimension k=Θ(α^-2 łog(1/β)) and sparsity s=O(α^-1 łog(1/β)). We combine the constructions of Kane & Nelson with either the Laplace or the Gaussian mechanism from the differential privacy literature, depending on the privacy parameters $\varepsilon$ and δ. We also suggest a differentially private version of Fast Johnson-Lindenstrauss Transform (FJLT) by Ailon & Chazelle (SIAM Journal of Computing 2009) which offers a tradeoff in speed for variance for certain parameters. We answer an open question by Kenthapadi et al. (Journal of Privacy and Confidentiality 2013) by analyzing the privacy and utility guarantees of an estimator for Euclidean distance, relying on Laplacian rather than Gaussian noise. We prove that the Laplace mechanism yields lower variance than the Gaussian mechanism whenever δ<β^O(1/α). Thus, our work poses an improvement over the work of Kenthapadi et al. by giving a more efficient estimator with lower variance for sufficiently small δ. Our sketch also achieves pure differential privacy as a neat side-effect of the Laplace mechanism rather than the approximate differential privacy guarantee of the Gaussian mechanism, which may not be sufficiently strong for some settings. Our main result is a special case of more general, technical results proving that one can generally construct unbiased estimators for Euclidean distance with a high level of utility even under the constraint of differential privacy. The bulk of our analysis is proving that the variance of the estimator does not suffer too much in the presence of differential privacy.
A Hybrid Model-Based and Learning-Based Approach for Classification Using Limited Number of Training Samples

The fundamental task of classification given a limited number of training data samples is considered for physical systems with known parametric statistical models. The standalone learning-based and statistical model-based classifiers face major challenges towards the fulfillment of the classification task using a small training set. Specifically, classifiers that solely rely on the physics-based statistical models usually suffer from their inability to properly tune the underlying unobservable parameters, which leads to a mismatched representation of the system's behaviors. Learning-based classifiers, on the other hand, typically rely on a large number of training data from the underlying physical process, which might not be feasible in most practical scenarios. In this paper, a hybrid classification method -- termed HyPhyLearn -- is proposed that exploits both the physics-based statistical models and the learning-based classifiers. The proposed solution is based on the conjecture that HyPhyLearn would alleviate the challenges associated with the individual approaches of learning-based and statistical model-based classifiers by fusing their respective strengths. The proposed hybrid approach first estimates the unobservable model parameters using the available (suboptimal) statistical estimation procedures, and subsequently use the physics-based statistical models to generate synthetic data. Then, the training data samples are incorporated with the synthetic data in a learning-based classifier that is based on domain-adversarial training of neural networks. Specifically, in order to address the mismatch problem, the classifier learns a mapping from the training data and the synthetic data to a common feature space. Simultaneously, the classifier is trained to find discriminative features within this space in order to fulfill the classification task.
Forest Optimization Algorithm Implementation using Sphere Mathematical Function

Forest Optimization Algorithm (FOA), a recent evolutionary algorithm suitable for continuous nonlinear optimization problems. It is inspired by a few trees in the forest that can last for several decades while other trees can only live for a short time. In FOA, the tree seeding technique is simulated so that certain seeds fall directly under the leaves, while others are dispersed over a large area by natural processes and animals that feed on the seeds or fruits. In this paper, we used the sphere mathematical function to implement FOA as a step-by-step process, and the iteration-based results are displayed. The findings of the experiments demonstrated that FOA performed well in certain data sets from the UCI repository..
Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution

We propose a new embedding method, named Quantile–Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile–quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimensionality reduction methods, such as PCA, t-SNE, and deep metric learning, for better representation or visualization of data. We propose QQE in both unsupervised and supervised forms. QQE can also transform a distribution to either an exact reference distribution or its shape. We show that QQE allows for better discrimination of classes in some cases. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.
Circuit-Depth Reduction of Unitary-Coupled-Cluster Ansatz by Energy Sorting

Quantum computation represents a revolutionary approach for solving problems in quantum chemistry. However, due to the limited quantum resources in the current noisy intermediate-scale quantum (NISQ) devices, quantum algorithms for large chemical systems remains a major task. In this work, we demonstrate that the circuit depth of the unitary coupled cluster (UCC) and UCC-based ansatzes in the algorithm of variational quantum eigensolver can be significantly reduced by an energy-sorting strategy. Specifically, subsets of excitation operators are first pre-screened from the operator pool according to its contribution to the total energy. The quantum circuit ansatz is then iteratively constructed until the convergence of the final energy to a typical accuracy. For demonstration, this method has been successfully applied to molecular and periodic systems. Particularly, a reduction of 50\%$\sim$98\% in the number of operators is observed while retaining the accuracy of the origin UCCSD operator pools. This method can be straightforwardly extended to general parametric variational ansatzes.
One-Point Gradient-Free Methods for Smooth and Non-smooth Saddle-Point Problems

In this paper, we analyze gradient-free methods with one-point feedback for stochastic saddle point problems $$\min _{x}\max _{y} \varphi (x, y)$$ . For non-smooth and smooth cases, we present an analysis in a general geometric setup with the arbitrary Bregman divergence. For problems with higher order smoothness, the analysis is carried out only in the Euclidean case. The estimates we have obtained repeat the best currently known estimates of gradient-free methods with one-point feedback for problems of imagining a convex or strongly convex function. The paper uses three main approaches to recovering the gradient through finite differences: standard with a random direction, as well as its modifications with kernels and residual feedback. We also provide experiments to compare these approaches for the matrix game.
&lt;i&gt;The British Sentimental Novel Corpus&lt;/i&gt; (&lt;i&gt;BSNC&lt;/i&gt;) and the ROC-DDC alternation at the level of the individual

This article provides a description of the British Sentimental Novel Corpus (BSNC) and a case study that explores, at the level of the individual, the relation between the Reaction Object Construction (ROC) and the Direct Discourse Construction (DDC). The BSNC is a large-scale specialised corpus that comprises full novels of eleven canonical authors across three generations from 19th century British fiction. It aims at studying language as both a social and a cognitive phenomenon and, in line with a recent trend in historical sociolinguistics, at exploring the interaction between individual and aggregate levels (see Fonteyn 2017; Hilpert 2020; Petré et al. 2019). The first part describes the methodological principles that underlie the design and compilation of the BSNC. In the second part, we present a new case study that aims to determine whether our previous aggregate findings also hold at the individual level. The results serve to confirm our hypothesis: first, individual changes in the ROC and the DDC run in parallel across almost the entire 19th century, correlating most significantly between 1851 and 1860. Second, the aggregate-level division of labour between these two functionally similar constructions turned out to be a feature of all authors in the BSNC. Last, the ROC-DDC alternation has been attested in an important proportion of the BSNC novels, with only a relatively small group of texts using solely the older and less extravagant variant (i.e. the DDC). This suggests that the alternation as such represents a cognitive reality for these individual writers across their lifespan.
Status and prospects for the libraries of academic research institutes (as the case study of SB RAS Novosibirsk Scientific Center libraries). Part 2

The authors analyze the survey of the Novosibirsk Scientific Center’s libraries related to the relevant services provided by academic libraries. In Part 2, the authors discuss the findings for traditional and innovative services; attitude of library directors toward the newest services, and the latest developments. On the whole, the libraries of SB RAS research institutes enforce the widest range of activities and services based on the newest technologies. However, to offer modern information services, the librarians have to interact with researchers (experts) as their obvious partners. Through analyzing the survey results, the authors came to the following conclusions: all libraries have been accomplishing their traditional tasks; despite their adaptation to a new environment, as far as implementation of information services is concerned, they have remained at the level 1990s – early 2000s; library top managers demonstrate interest toward new knowledge and skills needed to offer innovative services to their users.
Competent Third Parties and Content Moderation on Platforms: Potentials of Independent Decision-Making Bodies From A Governance Structure Perspective

ABSTRACT After many years of much-criticized opacity in the field of content moderation, social media platforms are now opening up to a dialogue with users and policymakers. Until now, liability frameworks in the United States and in the European Union (EU) have set incentives for platforms not to monitor user-generated content—an increasingly contested model that has led to (inter alia) practices and policies of noncontainment. Following discussions on platform power over online speech and how contentious content benefits the attention economy, there is an observable shift toward stricter content moderation duties in addition to more responsibility with regard to content. Nevertheless, much remains unsolved: the legitimacy of platforms' content moderation rules and decisions is still questioned. The platforms' power over the vast majority of communication in the digital sphere is still difficult to grasp because of its nature as private, yet often perceived as public. To address this issue, we use a governance structure perspective to identify potential regulatory advantages of establishing cross-platform external bodies for content moderation, ultimately aiming at providing insights about the opportunities and limitations of such a model.
Bayesian Distributional Policy Gradients

Distributional Reinforcement Learning (RL) maintains the entire probability distribution of the reward-to-go, i.e. the return, providing more learning signals that account for the uncertainty associated with policy performance, which may be beneficial for trading off exploration and exploitation and policy learning in general. Previous works in distributional RL focused mainly on computing the state-action-return distributions, here we model the state-return distributions. This enables us to translate successful conventional RL algorithms that are based on state values into distributional RL. We formulate the distributional Bellman operation as an inference-based auto-encoding process that minimises Wasserstein metrics between target/model return distributions. The proposed algorithm, BDPG (Bayesian Distributional Policy Gradients), uses adversarial training in joint-contrastive learning to estimate a variational posterior from the returns. Moreover, we can now interpret the return prediction uncertainty as an information gain, which allows to obtain a new curiosity measure that helps BDPG steer exploration actively and efficiently. We demonstrate in a suite of Atari 2600 games and MuJoCo tasks, including well known hard-exploration challenges, how BDPG learns generally faster and with higher asymptotic performance than reference distributional RL algorithms.
Business communication style as a factor in the design of a digital employee in education

The problems of digital transformation as the main trend in the development of education and it’s connection with the educational ecosystem are discussed. The transformation of the role of the teacher in the university in connection with the digitalization of society is noted. The place of the digital employee as an element of the social component of the ecosystem is indicated. The approaches to the design of the image of a digital employee are characterized: from the point of view of the components of the educational environment, the students’ perceptions of the teacher as a partner in educational interactions and the peculiarities of business communication in joint activities. Style features take into account the integration of both external and internal human resources, and are formed by arbitrary selection from a set of potential components of the style. In the aspect of comfortable interaction, students’ ideas about a comfortable teacher are analyzed. The study involved students with different periods of study at the university. Dispersion analysis revealed differences in ideas about a comfortable teacher, both of an affirmative supervenience and non-adaptive, requiring additional attention in organizing the educational process. Correlation analysis revealed the connection between personality traits and the coefficient showing the divergence between one’s own style and the style of a comfortable teacher. The options for designing a digital teacher by distributing functions between a robot and a human are discussed. Recommendations are indicated that can be applied both for real university teachers and digital employees.