Reviewerly/exHarmony huggingface dataset:
Implementation of the model of formation of entrepreneurial competences in modern pedagogical education: theoretical and practical aspects

The subject of this research is the formation of entrepreneurial competences of students majoring pedagogy. The goal of this work consists in formulation of theoretical provisions and analysis of organizational-pedagogical conditions for implementation of the model of formation of entrepreneurial competences in educational process of the Ussuriysk branch (School of Pedagogy) of Far Eastern Federal University. The development of entrepreneurial culture of the society currently becomes one of the priority objectives of education as a social institution. In the authors&amp;rsquo; opinion, its solution first and foremost lied in the formation of entrepreneurial competences of future pedagogues as the major plank of education system. The research methodology is based on systemic approach and includes the methods of theoretical analysis of the state of problem in scientific literature, studying and generalization of approaches towards building a model for the formation of entrepreneurial competences, and analysis of documentation of educational institutions. The scientific novelty consists in emphasizing the need for the formation of entrepreneurial competences of students majoring pedagogy; proposal of the structural-conceptual model of the educational process and determination of its key organizational-pedagogical conditions; as well as assessment of practical implementation of such model.
Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants

Installation of surveillance cameras in nuclear power plants is critical to protecting the facilities against terrorist attacks or monitoring the reactor operator. This has led to large amounts of video surveillance data, creating a demand for automatic detection of anomalies or suspicious movements. Tracking human motion from video sequences is a notable technique used for detecting anomalies in human behavior and is currently achieved with the use of a depth camera. However, depth cameras require a complicated camera system and their field of view is limited. To overcome this problem, there is a need for recognizing human motion in wide-angle images – a view that often causes distortion. In this study, we devised a method for tracking human motion through wideangle image distortion. The main contribution of this study is a methodology that automatically estimates the transformation parameters needed to improve the accuracy of motion recognition; these parameters are applied to a distorted wide-angle image in every frame. We propose a new multi-layered convolutional neural architecture for estimating the locations of human joints in images and transformation parameters simultaneously. When applied to distorted wide-angle images, the robustness of our method is demonstrated through a quantitative evaluation of human joint location prediction. In addition, we compare our method with a motion tracking system and an infrared-camera-based motion capture system to demonstrate its ability to handle wide-angle and close-range images.
[Papers] Multimodal Important Scene Detection in Far-view Soccer Videos Based on Single Deep Neural Architecture

The details of the matches of soccer can be estimated from visual and audio sequences, and they correspond to the occurrence of important scenes.Therefore, the use of these sequences is suitable for important scene detection.In this paper, a new multimodal method for important scene detection from visual and audio sequences in far-view soccer videos based on a single deep neural architecture is presented.A unique point of our method is that multiple classifiers can be realized by a single deep neural architecture that includes a Convolutional Neural Network-based feature extractor and a Support Vector Machine-based classifier.This approach provides a solution to the problem of not being able to simultaneously optimize different multiple deep neural architectures from a small amount of training data.Then we monitor confidence measures output from this architecture for the multimodal data and enable their integration to obtain the final classification result.
Local transformations with a singular wavelet

The paper considers a local wavelet transform with a singular basis wavelet. The problem of nonparametric approximation of a function is solved by the use of the sequence of local wavelet transforms. Traditionally believed that the wavelet should have an average equal to zero. Earlier, the author considered singular wavelets when the average value is not equal to zero. As an example, the delta-shaped functions, participated in the estimates of Parzen – Rosenblatt and Nadara – Watson, were used as a wavelet. Previously, a sequence of wavelet transforms for the entire numerical axis and finite interval was constructed for singular wavelets. The paper proposes a sequence of local wavelet transforms, a local wavelet transform is defined, the theorems that formulate the properties of a local wavelet transform are proved. To confirm the effectiveness of the algorithm an example of approximating the function by use of the sum of discrete local wavelet transforms is given.
Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

This paper provides the classification of the review texts on a smartphone application posted on social media.We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus.The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT).
Give your Text Representation Models some Love: the Case for Basque

Word embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many small companies and research groups tend to use models that have been pre-trained and made available by third parties, rather than building their own. This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora. In addition, monolingual pre-trained models for non-English languages are not always available. At best, models for those languages are included in multilingual versions, where each language shares the quota of substrings and parameters with the rest of the languages. This is particularly true for smaller languages such as Basque. In this paper we show that a number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER. This work sets a new state-of-the-art in those tasks for Basque. All benchmarks and models used in this work are publicly available.
Human Activity Recognition Algorithm Based on One-Dimensional Convolutional Neural Network

Human activity recognition (HAR) is widely used in healthcare, personal fitness, physical training and military, etc.How to distinguish various human activities accurately (such as running, walking, walking upstairs and downstairs, jumping and standing) has become an important problem in human-computer interaction.The computer vision method requires a large amount of computing resources, and it is not highly accuracy and can be easily disturbed by other objects in the background.The sensor-based method can achieve high accuracy, and it requires few computing resources, and is not disturbed by the background.This paper proposes a method based on the one-dimensional convolutional neural network (1D-CNN) to classify the sensor signals of some different activities.For comparison, this paper applies some widely used methods to accomplish the recognition task with the same dataset.Then, it tests the proposed 1D-CNN model with different datasets, for the purpose of testing its generality across users.The experimental results show that the proposed model achieves an accuracy of 95.12% with the said datasets, which is higher than those of the other methods by about 8% on average.This indicates that the proposed method has good performance in terms of generality across users, and at the same time provides a higher accuracy.The obtained results can improve the accuracy of current technologies.
Adversarial Learning for Personalized Tag Recommendation

We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image classification. However, there are usually multiple tags associated with an image. The existing works on multi-label classification are mainly based on lab curated labels. Humans assign tags to their images differently, which is mainly based on their interests and personal tagging behavior. In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets. The user-preference is learned within the network in an unsupervised way where the network performs joint optimization for user-preference and visual encoding. A joint training of user-preference and visual encoding allows the network to efficiently integrate the visual preference with tagging behavior for a better user recommendation. In addition, we propose the use of adversarial learning, which enforces the network to predict tags resembling user-generated tags. We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE. The proposed method achieves significantly better performance on both the datasets when compared to the baselines and other state-of-the-art methods. The code is publicly available at this https URL.
Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems

Blind modulation classification is an important step to implement cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in the MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time-frequency analysis method based on the windowed short-time Fourier transform is used to analyse the time-frequency characteristics of time-domain modulated signals. Then the extracted time-frequency characteristics are converted into RGB spectrogram images, and the convolutional neural network based on transfer learning is applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module is used to fuse the classification results of all the receive antennas. Through simulations, we analyse the classification performance at different signal-to-noise ratios (SNRs), the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of -4 dB and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at -4 dB and 10 dB, respectively. This outperforms the existing classification methods based on baseband signals.
A Study of Highest Perfusion Zones as Biometric Representation

Biometrics focuses on simulate the human ability to associate one or a set of corporal features of a person in a unique way by uses a specific representation, this representation is knows as identity. Visible spectrum face recognition is the identification way more natural which has a higher universality, collectability and acceptability front to the other biometrics modalities, but is weak in the invariance and distinctiveness criterion to be a good biometric. In order to improve the face recognition, the infrared spectrum arises as a good representation to solve these drawbacks in biometric identification. Buddharaju et al., proposed a process by which the vascular net is detected [4] . However, Wu et al., criticized this approach by not take into account the heat transfer between the environment and the person in the time to take the image and proposed a modification of image thermogram and show that it is a better solution to make up for the heat change [19, 21]. This paper is written intending to know if there is a significant difference between both approach to be used as biometric representation. We found that the normalization of the thermograms, proposed by Wu, do not affect the distinctive zones of high blood perfusion to be used as biometric representation.
1D representation of Laplacian eigenmaps and dual k ‐nearest neighbours for unified video coding

This study proposes a framework of video coding based on Laplacian eigenmaps (LEM) and its related embedding and reconstruction algorithm (ERA). Firstly, a one-dimensional (1D) representation of LEM is adopted to achieve an extremely low bit per pixel (BPP). Secondly, dual k-nearest neighbours, which keeps neighbour relationships both in high-dimensional data space and low-dimensional representation space and overcomes the disadvantage of classical non-linear dimensionality reduction methods which cannot preserve the neighbour properties in both of the spaces, based ERA of LEM is employed to gain extraordinarily high peak-signal-to-noise ratio (PSNR). Thirdly, a unified framework of video coding is fit for intra-frame, inter-frame and multi-view video coding. Finally, it is evaluated by simulation experiments that, in the situation of low bitrate transmission, the proposed method can attain better performance of BPP and PSNR than that of the state-of-the-art methods, such as highly efficient video coding.
Applying Cyclical Learning Rate to Neural Machine Translation

In training deep learning networks, the optimizer and related learning rate are often used without much thought or with minimal tuning, even though it is crucial in ensuring a fast convergence to a good quality minimum of the loss function that can also generalize well on the test dataset. Drawing inspiration from the successful application of cyclical learning rate policy for computer vision related convolutional networks and datasets, we explore how cyclical learning rate can be applied to train transformer-based neural networks for neural machine translation. From our carefully designed experiments, we show that the choice of optimizers and the associated cyclical learning rate policy can have a significant impact on the performance. In addition, we establish guidelines when applying cyclical learning rates to neural machine translation tasks. Thus with our work, we hope to raise awareness of the importance of selecting the right optimizers and the accompanying learning rate policy, at the same time, encourage further research into easy-to-use learning rate policies.
GUNGEN-Heartbeat: A Support System for High Quality Idea Generation Using Heartbeat Variance

We propose an idea generation support system known as the “GUNGEN-Heartbeat” that uses heartbeat variations for creating high quality ideas during brainstorming. This system shows “An indication of a check list” or “An indication to promote deep breathing” at time beyond a value with variance of heart rates. We also carried out comparison experiments to evaluate the usefulness of the system.
Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study Dataset

Natural language processing supported requirements engineering is an area of research and development that seeks to apply NLP techniques, tools and resources to a variety of requirements documents or artifacts to support a range of linguistic analysis tasks performed at various RE phases. Such tasks include detecting language issues, identifying key domain concepts and establishing traceability links between requirements. This article surveys the landscape of NLP4RE research to understand the state of the art and identify open problems. The systematic mapping study approach is used to conduct this survey, which identified 404 relevant primary studies and reviewed them according to five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of the practice, and the NLP technologies used. Results: 1) NLP4RE is an active and thriving research area in RE that has amassed a large number of publications and attracted widespread attention from diverse communities; 2) most NLP4RE studies are solution proposals having only been evaluated using a laboratory experiment or an example application; 3) most studies have focused on the analysis phase, with detection as their central linguistic analysis task and requirements specification as their commonly processed document type; 4) 130 new tools have been proposed to support a range of linguistic analysis tasks, but there is little evidence of adoption in the long term, although some industrial applications have been published; 5) 140 NLP techniques, 66 NLP tools and 25 NLP resources are extracted from the selected studies.
Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework

In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.
Distance and Equivalence between Finite State Machines and Recurrent Neural Networks: Computational results

The need of interpreting Deep Learning (DL) models has led, during the past years, to a proliferation of works concerned by this issue. Among strategies which aim at shedding some light on how information is represented internally in DL models, one consists in extracting symbolic rule-based machines from connectionist models that are supposed to approximate well their behaviour. In order to better understand how reasonable these approximation strategies are, we need to know the computational complexity of measuring the quality of approximation. In this article, we will prove some computational results related to the problem of extracting Finite State Machine (FSM) based models from trained RNN Language models. More precisely, we'll show the following: (a) For general weighted RNN-LMs with a single hidden layer and a ReLu activation: - The equivalence problem of a PDFA/PFA/WFA and a weighted first-order RNN-LM is undecidable; - As a corollary, the distance problem between languages generated by PDFA/PFA/WFA and that of a weighted RNN-LM is not recursive; -The intersection between a DFA and the cut language of a weighted RNN-LM is undecidable; - The equivalence of a PDFA/PFA/WFA and weighted RNN-LM in a finite support is EXP-Hard; (b) For consistent weight RNN-LMs with any computable activation function: - The Tcheybechev distance approximation is decidable; - The Tcheybechev distance approximation in a finite support is NP-Hard. Moreover, our reduction technique from 3-SAT makes this latter fact easily generalizable to other RNN architectures (e.g. LSTMs/RNNs), and RNNs with finite precision.
PointAR: Efficient Lighting Estimation for Mobile Augmented Reality

We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2nd order spherical harmonics coefficients. This estimated spherical harmonics coefficients can be directly utilized by rendering engines for supporting spatially variant indoor lighting, in the context of augmented reality. Our key insight is to formulate the lighting estimation as a point cloud-based learning problem directly from point clouds, which is in part inspired by the Monte Carlo integration leveraged by real-time spherical harmonics lighting. While existing approaches estimate lighting information with complex deep learning pipelines, our method focuses on reducing the computational complexity. Through both quantitative and qualitative experiments, we demonstrate that PointAR achieves lower lighting estimation errors compared to state-of-the-art methods. Further, our method requires an order of magnitude lower resource, comparable to that of mobile-specific DNNs.
MODELLING SUPPORT SYSTEMS FOR SELECTING PROFESSIONS FOR APPLICANTS IN THE CONTENT OF PERSONALIZATION OF EDUCATION

Various methods are currently being used in examining the initial “START” knowledge of applicants and their placement for specialties. Studies show that applicants are placed on the decreasing principle in terms of their overall scores at universities. In this case, applicants with a high level of knowledge are placed in the prestigious specialties as medicine and law as they require high results. Though, while applying for other professions, the applicants do not perform enough results on the key disciplines for the profession, they are placed in those professions when the general results enable it. This causes them to face a number of problems while working both in education process and in the industry. To avoid this problem and to place applicants in a specialty that is more relevant to their level of knowledge, the introduction of an individual approach to the evaluation of initial level of knowledge may be more promising. This article presents a modeling of the "evaluation – placement" support system for the individual approach to assessing applicants' knowledge and positioning them in relevant specialties. The main goal of the system is to give each applicant the opportunity to choose and study the specialty that is more relevant to their knowledge and skills, as well as to analyze the results for each discipline along with the overall results. The system is implemented using fuzzy logic based artificial neural networks. The network consists of 100 neurons in the input layer, two hidden layers and one output layer. The number of neurons at the output is the same as the number of specialties taught at university.
Study on the Computational Trust and Its Model

Abstract The application of block chain technology has greatly improved our digital ability in work and daily life. By fostering greater trust, blockchain technology has not only increased productivity, but is sure to reshape the relationship between individuals and businesses, leading us toward a true digital economy. For hundreds of millions of people and tens of billions of smart devices, this could mean hundreds of billions of smart contracts running autonomously and significantly enhancing global synergies. This paper aims to explore the computational trust model to demonstrate how the trust be computationalised. By building a global blockchain infrastructure, we are spreading trust, and computational trust will be a ubiquitous part of our society. As more businesses and individuals join in, more people will benefit from this new technology, and the future of human society will become more inclusive, transparent, and trustworthy.