Reviewerly/exHarmony huggingface dataset:
Dense Video Captioning Using Unsupervised Semantic Information

We introduce a method to learn unsupervised semantic visual information based on the premise that complex events (e.g., minutes) can be decomposed into simpler events (e.g., a few seconds), and that these simple events are shared across several complex events. We split a long video into short frame sequences to extract their latent representation with three-dimensional convolutional neural networks. A clustering method is used to group representations producing a visual codebook (i.e., a long video is represented by a sequence of integers given by the cluster labels). A dense representation is learned by encoding the co-occurrence probability matrix for the codebook entries. We demonstrate how this representation can leverage the performance of the dense video captioning task in a scenario with only visual features. As a result of this approach, we are able to replace the audio signal in the Bi-Modal Transformer (BMT) method and produce temporal proposals with comparable performance. Furthermore, we concatenate the visual signal with our descriptor in a vanilla transformer method to achieve state-of-the-art performance in captioning compared to the methods that explore only visual features, as well as a competitive performance with multi-modal methods. Our code is available at https://github.com/valterlej/dvcusi.
Network analysis of verbal communications in the novel The Master and Margarita by M.A.Bulgakov

A network analysis of the structure of verbal communications in one of the most popular Russian novels of the Soviet era The Master and Margarita by Bulgakov has been carried out. The structure of the novel is complex (a story within a story). Moreover, the real-world-characters and the other-world-characters are interacting in the novel. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only paired verbal communications into account leads to the result, that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the conversations was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coefficient were computed to characterize the network. The structure of the communities in the network was also analysed. In addition to the obvious large communities-the characters from the Yershalaim part of the novel and the characters of the Moscow part-the analysis also revealed a fine structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Koroviev, Margarita, Bezdomny, Woland, Behemoth, Azazello, Bosoi, Warenukha, Master, and Levi Matthew.
An Applied Variation Anomaly Technique for Detection of Irregular Values in Data Mining

Information superiority is significant to organizations. With the use of data mining, Anomalous data values detection is a most important step in many data related applications. Anomalous data make the performance of data analysis difficult. The presence of anomalous data value can also pose serious problems for researchers. In fact, in appropriate handling of the Anomalous data values in the analysis may introduce bias and can result in misleading conclusions being drawn from a research study and can also limit the generalize ability of the research findings. There are numerous techniques for Anomalous data detection, while using Inliers and Outlier techniques and their different measures in data mining. This article introduces anomalous data detection algorithm that should be used in data mining systems. Basic approaches currently used for solving this Anomalous data values finding, problem are considered, and their results are discussed using table.
Processing M.A. Castrén's Materials: Multilingual Typed and Handwritten Manuscripts

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castr\'en (1813-1852). The Finno-Ugrian Society is publishing Castr\'en's manuscripts as new critical and digital editions, and at the same time different research groups have also paid attention to these materials. We discuss the workflows and technical infrastructure used, and consider how datasets that benefit different computational tasks could be created to further improve the usability of these materials, and also to aid the further processing of similar archived collections. We specifically focus on the parts of the collections that are processed in a way that improves their usability in more technical applications, complementing the earlier work on the cultural and linguistic aspects of these materials. Most of these datasets are openly available in Zenodo. The study points to specific areas where further research is needed, and provides benchmarks for text recognition tasks.
Deriving discriminative classifiers from generative models

We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)$, with the observation $y$ and the target $x$, one computes generative classifiers by firstly considering $p(x, y)$ and then using the Bayes rule to calculate $p(x | y)$. A discriminative model is directly given by $p(x | y)$, which is used to compute discriminative classifiers. However, recent works showed that the Bayesian Maximum Posterior classifier defined from the Naive Bayes (NB) or Hidden Markov Chain (HMC), both generative models, can also match the discriminative classifier definition. Thus, there are situations in which dividing classifiers into "generative" and "discriminative" is somewhat misleading. Indeed, such a distinction is rather related to the way of computing classifiers, not to the classifiers themselves. We present a general theoretical result specifying how a generative classifier induced from a generative model can also be computed in a discriminative way from the same model. Examples of NB and HMC are found again as particular cases, and we apply the general result to two original extensions of NB, and two extensions of HMC, one of which being original. Finally, we shortly illustrate the interest of the new discriminative way of computing classifiers in the Natural Language Processing (NLP) framework.
Hybrid Quantum-Classical Algorithm for Hydrodynamics

A new model of nonlinear charged quantum relativistic fluids is presented. This model can be discretized into Discrete Time Quantum Walks (DTQWs), and a new hybrid (quantum-classical) algorithm for implementing these walks on NISQ devices is proposed. High resolution (up to $N=2^{17}$ grid points) hybrid numerical simulations of relativistic and non-relativistic hydrodynamical shocks on current IBM NISQs are performed with this algorithm and shown to reproduce equivalent simulations on classical computers. This work demonstrates that nonlinear fluid dynamics can be simulated on NISQs, and opens the door to simulating other, quantum and non-quantum fluids, including plasmas, with more general quantum walks and quantum automata.
Surrogate Likelihoods for Variational Annealed Importance Sampling

Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling. By contrast MCMC methods like Hamiltonian Monte Carlo do not share these properties but remain attractive since, contrary to parametric methods, MCMC is asymptotically unbiased. For these reasons researchers have sought to combine the strengths of both classes of algorithms, with recent approaches coming closer to realizing this vision in practice. However, supporting data subsampling in these hybrid methods can be a challenge, a shortcoming that we address by introducing a surrogate likelihood that can be learned jointly with other variational parameters. We argue theoretically that the resulting algorithm permits the user to make an intuitive trade-off between inference fidelity and computational cost. In an extensive empirical comparison we show that our method performs well in practice and that it is well-suited for black-box inference in probabilistic programming frameworks.
Keyphrase Generation Beyond the Boundaries of Title and Abstract

Keyphrase generation aims at generating phrases (keyphrases) that best describe a given document. In scholarly domains, current approaches to this task are neural approaches and have largely worked with only the title and abstract of the articles. In this work, we explore whether the integration of additional data from semantically similar articles or from the full text of the given article can be helpful for a neural keyphrase generation model. We discover that adding sentences from the full text particularly in the form of summary of the article can significantly improve the generation of both types of keyphrases that are either present or absent from the title and abstract. The experimental results on the three acclaimed models along with one of the latest transformer models suitable for longer documents, Longformer Encoder-Decoder (LED) validate the observation. We also present a new large-scale scholarly dataset FullTextKP for keyphrase generation, which we use for our experiments. Unlike prior large-scale datasets, FullTextKP includes the full text of the articles alongside title and abstract. We will release the source code to stimulate research on the proposed ideas.
Probing for Labeled Dependency Trees

Probing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected or unlabeled parse trees which do not capture the full task. This work introduces DepProbe, a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods. Leveraging its full task coverage and lightweight parametrization, we investigate its predictive power for selecting the best transfer language for training a full biaffine attention parser. Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work. Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as well as which benefits a full parser's non-linear parametrization provides.
Quantum computational advantage implies contextuality

We show that a separation between the class of all problems that can efficiently be solved on a quantum computer and those solvable using probabilistic classical algorithms in polynomial time implies the generalized contextuality of quantum algorithms. Our result subsumes versions of Gottesman-Knill theorem as special cases.
Reading Task Classification Using EEG and Eye-Tracking Data

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading. We analyze whether machine learning methods are able to classify these two tasks using eye-tracking and EEG features. We implement models with aggregated sentence-level features as well as fine-grained word-level features. We test the models in within-subject and cross-subject evaluation scenarios. All models are tested on the ZuCo 1.0 and ZuCo 2.0 data subsets, which are characterized by differing recording procedures and thus allow for different levels of generalizability. Finally, we provide a series of control experiments to analyze the results in more detail.
Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction  Techniques for Text-Independent Writer Identification

Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier writer identification relied on handcrafted features to reveal pieces of differences between writers. Recent work with the advent of convolutional neural network, deep learning-based methods have evolved. In this paper, three different deep learning techniques - spatial attention mechanism, multi-scale feature fusion and patch-based CNN were proposed to effectively capture the difference between each writer's handwriting. Our methods are based on the hypothesis that handwritten text images have specific spatial regions which are more unique to a writer's style, multi-scale features propagate characteristic features with respect to individual writers and patch-based features give more general and robust representations that helps to discriminate handwriting from different writers. The proposed methods outperforms various state-of-the-art methodologies on word-level and page-level writer identification methods on three publicly available datasets - CVL, Firemaker, CERUG-EN datasets and give comparable performance on the IAM dataset.
Interpretation of Emoji-Only Messages by Message Producers and Receivers

Emoji play a significant role in mutual communication. It was seen that emoji might carry different meanings depending on groups or cultures, although they are considered as a language valid for everyone. This study investigated how individuals responded to the questions directed to them using emoji, how they described the basic emotions, and what the individuals who read these messages understood. The study consisted of two stages. At the first stage, the participants were asked to respond to 10 questions, for which answers with concrete and abstract contents were sought, by using first emoji-only and then only text. Moreover, they were requested to express the eight basic emotions they were shown only with emoji. The participants also determined a satisfaction level for the emoji responses. At the second stage, a different group of participants scored the satisfaction levels by displaying the question. It was found out that a message consisting of emoji might be evaluated by the receiver in a different way, even if it has been created with a high satisfaction level.
New Hard-thresholding Rules based on Data Splitting in High-dimensional Imbalanced Classification

In binary classification, imbalance refers to situations in which one class is heavily under-represented. This issue is due to either a data collection process or because one class is indeed rare in a population. Imbalanced classification frequently arises in applications such as biology, medicine, engineering, and social sciences. In this paper, for the first time, we theoretically study the impact of imbalance class sizes on the linear discriminant analysis (LDA) in high dimensions. We show that due to data scarcity in one class, referred to as the minority class, and high-dimensionality of the feature space, the LDA ignores the minority class yielding a maximum misclassification rate. We then propose a new construction of hard-thresholding rules based on a data splitting technique that reduces the large difference between the misclassification rates. We show that the proposed method is asymptotically optimal. We further study two well-known sparse versions of the LDA in imbalanced cases. We evaluate the finite-sample performance of different methods using simulations and by analyzing two real data sets. The results show that our method either outperforms its competitors or has comparable performance based on a much smaller subset of selected features, while being computationally more efficient.
Effects of “English-Only Policy” on Students’ Fluency and Motivation Level in Speaking English

The study aims to examine the influence of English-only policy on students’ fluency and motivation level to speak English. The respondents of the study are 20 secondary school students in Hulu Perak who are divided into two groups; experimental group and control group. The research design of this study is quantitative through quasi-experimental and survey. A pre and post-speaking test and a questionnaire which consists of 9 items using 5-point Likert scale are used for data collection. The data is analysed through mean scores of pre and post-test between experimental and control group and the total score of each respondent. The findings of pre and post-test indicate that the mean scores in the experimental group yield a higher improvement (3.20) than in the control group (1.80). Furthermore, the survey findings show that most respondents have a moderate level of motivation (60%), followed by high motivation level (40%). It can be concluded that English-only policy is effective on students’ speaking fluency and students have moderate to high levels of motivation to speak English language in English-only policy lessons. The implication of this study is that teachers can use English-only policy in English language lessons to improve students’ speaking fluency and motivation level to speak English. Apart from that, it can be a benchmark for school administration to implement the approach at school level and for the Ministry of Education to provide training courses for teachers to ensure in-depth implementation of the teaching approach.
Risk bounds for aggregated shallow neural networks using Gaussian prior

Analysing statistical properties of neural networks is a central topic in statistics and machine learning. However, most results in the literature focus on the properties of the neural network minimizing the training error. The goal of this paper is to consider aggregated neural networks using a Gaussian prior. The departure point of our approach is an arbitrary aggregate satisfying the PAC-Bayesian inequality. The main contribution is a precise nonasymptotic assessment of the estimation error appearing in the PAC-Bayes bound. We also review available bounds on the error of approximating a function by a neural network. Combining bounds on estimation and approximation errors, we establish risk bounds that are sharp enough to lead to minimax rates of estimation over Sobolev smoothness classes.
Organizational and Pedagogical Conditions for the Development of Social and Communicative Competence of Future Educational Managers

A modern leader must have not only professional knowledge, but also such important and necessary qualities as purposefulness, energies, a sense of responsibility, sociability, organizational skills, an ability to reflect, i.e. social and communicative competence, which is key in the structure of professional competence of an educational manager and allows for effective communication in the context of social interaction.The aim of the study is to develop, theoretically substantiate and experimentally test the system of organizational and pedagogical conditions for the development of social and communicative competence of master students.The authors set a number of tasks, including clarification of the concept of "social and communicative competence of master students" based on the analysis of scientific research; development of a system of organizational and pedagogical conditions for the development of social and communicative competence of master students; determination of the state and problems in the development of social and communicative competence of master students of the Federal State Budgetary Educational Institution of Higher Education "Yaroslav-the-Wise Novgorod State University" (Veliky Novgorod) on the basis of a diagnostic study; implementation of the system of organizational and pedagogical conditions for the development of social and communicative competence of master students under the "Educational Management" program of the "Pedagogical Education" direction; generalization and description of research results.To solve the set tasks, a system-synergetic approach and the principle of cooperation and corporatism were used.The result of the theoretical part of the research is: clarification of the concept of "social and communicative competence of master students" as the ability of students for effective communication in conditions of social interaction; the structure of the concept under consideration includes four competence-components: communicative competence (the ability to actively interact with people around, to reflect on one's communication tactics), social competence (social interaction), organizational competence (the ability to organize and manage the work of a team), reflexive competence (selfassessment, analysis of one's own activities and activities of colleagues); determination of the system of organizational and pedagogical conditions for the development of social and communicative competence of master students, including regulatory support of educational activities, creation of an information and educational environment, competitive organization of classroom and extracurricular educational activities, monitoring the development of social and communicative competence of master students.The results of the study confirmed the hypothesis that the process of developing the social and communicative competence of master students will be effective if a system of organizational and pedagogical conditions is developed and implemented.
Exemplar-free Online Continual Learning

Targeted for real world scenarios, online continual learning aims to learn new tasks from sequentially available data under the condition that each data is observed only once by the learner. Though recent works have made remarkable achievements by storing part of learned task data as exemplars for knowledge replay, the performance is greatly relied on the size of stored exemplars while the storage consumption is a significant constraint in continual learning. In addition, storing exemplars may not always be feasible for certain applications due to privacy concerns. In this work, we propose a novel exemplar-free method by leveraging nearest-class-mean (NCM) classifier where the class mean is estimated during training phase on all data seen so far through online mean update criteria. We focus on image classification task and conduct extensive experiments on benchmark datasets including CIFAR-100 and Food-1k. The results demonstrate that our method without using any exemplar outperforms state-of-the-art exemplar-based approaches with large margins under standard protocol (20 exemplars per class) and is able to achieve competitive performance even with larger exemplar size (100 exemplars per class).
One-Shot Learning from a Demonstration with Hierarchical Latent Language

Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedures and generalize their execution to other contexts. In this work, we introduce DescribeWorld, an environment designed to test this sort of generalization skill in grounded agents, where tasks are linguistically and procedurally composed of elementary concepts. The agent observes a single task demonstration in a Minecraft-like grid world, and is then asked to carry out the same task in a new map. To enable such a level of generalization, we propose a neural agent infused with hierarchical latent language--both at the level of task inference and subtask planning. Our agent first generates a textual description of the demonstrated unseen task, then leverages this description to replicate it. Through multiple evaluation scenarios and a suite of generalization tests, we find that agents that perform text-based inference are better equipped for the challenge under a random split of tasks.