Reviewerly/exHarmony huggingface dataset:
Your language is easily recognized: A language clustering software in linguistic analysis

Abstract The development of technology in upholding any discipline of knowledge has been widely extended, particularly in linguistic study. One of the advancements is a language clustering software, functioning to analyse any linguistic data through the sorting procedure, namely Monoconc. Due to its significance, this study is aimed at expounding the practicality and feasibility of this software. To achieve this aim, both qualitative and quantitative research approaches were employed. As results, Monoconc software is recommended to utilize in analysing linguistic corpora. The simplicity, as well as the viability, are the main roots of proposing this concordance software as an alternative to applying.
Competencias comunicativas a través de los marcos de referencia de las lenguas

In this paper, the didactic and training potential of the Reference Frameworks of Languages is studied and discussed, with special attention to the REFIC ( Référentiel de compétences de communication plurilingue en intercompréhension ). While elaboration and dissemination of such Frameworks of Reference have a not too long tradition in Europe, its impact has been such as to consolidate them as the main documents articulating the language teaching practices in European educational contexts. In the didactic paradigm of intercomprehension, the Referentials have undertaken the systematization of its theoretical-methodological foundations, as well as its evaluation practice. On a population of students specialized in language teaching, this study explores the use of the REFIC as a (self) assessment tool of communicative competences, didactic learning in a comparative study of frames, and reception analysis of the REFIC.
An Advanced Version of MDNet for Visual Tracking

Tracking-by-detection is an effective framework for visual tracking tasks. For example, the Multi-Domain Convolution Neural Network (MDNet) achieves outstanding results in multiple benchmarks. However, the tracking performance of MDNet is restricted by two factors. First, the robustness is hindered by the semantic background since the backbone network is trained for the classification task. Second, MDNet discriminates target and background in individual domains and ignores the relationship between samples during the training process. We present an advanced version of MDNet (MDNet+) to solve the problems with two improvements. First, we propose an attention block that forces the network to focus on the most different features between the target and the semantic backgrounds to achieve higher robustness. Second, we introduce a joint loss consists of a basic classification loss and a triplet-loss. With the use of the triplet-loss, we minimize intra-class inertia and maximize the inter-class inertia between samples from multi-domains. The proposed algorithm outperforms the state-of-the-art tracking methods in multiple benchmarks even without dataset-specific parameter tuning.
The model of pedagogical support for the adaptation process of first-year students of secondary professional education at the Presidential College of Multilevel Professional Education of the Russian Academy of National Economy and Public Administration

The article is devoted to the consideration of the model of pedagogical support of the adaptation process of students - freshmen of secondary professional education. The problem of students' adaptation is one of the most urgent not only from the point of view of psychological comfort of students, but also as a necessary condition for the development of professional competencies. In order to solve this problem successfully, it is necessary to identify the reasons that negatively affect the adaptation of students, and to create a model of pedagogical support for first-year students at the stage of adaptation. The structure of the model, its components are presented, the features of the freshmen adaptation process are revealed, the reasons that negatively affect the students' adaptation are identified, the relevance of studying this problem in secondary vocational education is emphasized. The author pays special attention to characterizing the specifics of students' adaptation in the system of secondary vocational education, increasing the efficiency of the existing model in modern conditions. The positive results obtained during the pedagogical experiment confirm that the success of the process of adapting first-year students to college requires qualified pedagogical support, which can be provided only with competent interaction, cooperation of all structures of the educational process.
Principles of organization selection and preprocessing video information. Bioinspired approaches

Expansion application branches of computer vision systems and requirements to them causes the necessities of significant increase of their productivity, efficiency and level of intelligence. The essential limitation of traditional video systems is the semantic gap in how person and video camera perceive the image and the information-time gap between parallel light perception and subsequent pixel processing. The prototype for creating more perfect computer vision systems can be a human visual analyzer. The paper proposes a number of approaches and methods of selective selection and multi-layer processing of video information, which reduce or eliminate these limitations, inspired by the human visual analyzer. This will increase the productivity, efficiency and intellectual level of computer vision systems.
TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology Images using Local Texture, Appearance, and Gradient Features

This work considers the problem of detecting nuclei in H&E stained images. The high intra-class and inter-image variability of nuclei of the images itself calls for a more robust method that is able to handle this high variability. Recently, deep learning has become the most popular method in computer vision with many systems achieving state-of-the-art results for a wide array tasks. However, as a trade-off, deep learning methods require tons of data and computational resources that some may not posses. Thus, this work proposes a traditional computer vision pipeline along with TAG, a local feature-based approach that combines texture, appearance, and gradient features which is used in the task of pixel-per-pixel prediction in the virtue of semantic segmentation. Based from the results, the method was shown to be effective despite the challenges of high variability in the dataset.
Transformer Location Analysis Based on Poi Information

Abstract The location of the transformer is an important part of the power transmission and transformation project. This paper first summarizes and summarizes the predecessors’ rules for transformer location, and draws the following six rules: close to the load center, making the regional power grid layout reasonable, high and low pressure side access lines convenient, station area topography, station area geological. And the application of screening 1451 original poi data to obtain 368 poi data in accordance with the rules of 3 and above, and then introduce the ap clustering model, and apply the data to 9 ap clustering models by setting different parameters. In the optimal result, six clustering points are given, and the scores of the six locations are calculated and normalized to obtain the ranking of the transformers. The first one is the textile street.
Feature Combination Based on Receptive Fields and Cross-Fusion Feature Pyramid for Object Detection

Currently, the state-of-the-art method about object detector in image mainly depends on deep backbones, such as ResNet-50, DarkNet-53, ResNet-101 and DenseNet-169, which benefits for their powerful capability of feature representations but suffers from high computational cost. On the basis of fast lightweight backbone network (i.e., VGG-16), this paper improves the capability of feature representations by combining features of different receptive fields and cross-fusing feature pyramids, and finally establishes a fast and accurate detector. The architecture of our model is designed to integrate FC-CF Net with two sub-modules: FC module and CF module. Inspired by the structure of receptive fields in visual systems of human, we propose a novel method about Feature Combination Based on Receptive Fields module (FC module), which takes the relationship between the size and eccentricity of receptive fields into account, and then combine them with original features for increasing the receptive field and information of the feature map. Furthermore, based on the structure of FPN (Feature Pyramid Network), we design a novel Cross-Fusion Feature Pyramid module (CF module), which combines top-down and bottom-up connections to fuse features across scales, and achieves high-level semantic feature map at all scales. Extensive experiments on PASCAL VOC 2007 and 2012 demonstrate that FC-CF Net achieves state-of-the-art detection accuracy (i.e. 82.4% mAP, 80.5% mAP) with high efficiency (i.e. 69 FPS, 35 FPS).
On the square-based montgomery multiplier for a class of type C.2 pentanomial

In this paper, we continue the study of bit-parallel square-based Montgomery multiplier (MM) by Li et al. (Integration 2016, IEEE Access 2018). A square-based MM use an alternative divide and conquer algorithm split a polynomial dueing to the parties of its term degree. Li et al. developed squared-based MM for irreducible trinomials and Type C.1 pentanomials and showed such types of multipliers are at least as efficient as the multipliers using KA.
Pair Data Division Algorithm For Handling Data Classification

Data heterogeneity creates the complexity for understanding the different domains. Most of domains require sophisticated algorithm to understand the complexity. To handle the data heterogeneity problem, several methods have been proposed (e.g., Support Vector Machine (SVM), Decision Tree, Random Forest, Liner regression etc.). However, these existing methods suffer due to accuracy, outlying. In this paper, we introduce Pair Data Division (PDD) with support of SMV for handling the data heterogeneity. PDD divides the data into the pairs then further pairs are processed using SVM. The PDD algorithm is implanted by using Python and PyCharm is used as Interface Development Environment (IDE). Furthermore, training datasets will be given to PDD for testing purpose. Based on the testing results, we confirm that our proposed algorithm provides better accuracy as compared to known algorithms: Random Forest and Linear regression when handling the data heterogeneity.
A Deep Clustering-Guide Learning for Unsupervised Person Re-identification

Unsupervised person re-identification (RE-ID) has attracted increasing attentions due to its ability to overcome the scalability problem of supervised RE-ID methods. However, it is hard to learn discriminative features without pairwise labels and identity information in unlabeled target domains. To address this problem, we propose a deep clustering-guided model for unsupervised RE-ID that focuses on full mining of supervisions and a complete usage of the mined information. Specifically, we cluster person images from unlabeled target and labeled auxiliary datasets together. On the one hand, although the clustering IDs of unlabeled person images could be directly used as pseudo-labels to supervise the whole model, we further develop a non-parametric softmax variant for cluster-level supervision. On the other hand, since clustering badly suffers from intra-person appearance variation and inter-person appearance similarity in the unlabeled domain, we propose a reliable and hard mining in both intra-cluster and inter-cluster. Concretely, labeled persons (auxiliary domain) in each cluster are used as comparators to learn comparing vectors for each unlabeled persons. Following the consistency of the visual feature similarity and the corresponding comparing vector similarity, we mine reliable positive and hard negative pairs in the intra-cluster, and reliable negative and hard positive pairs in the inter-cluster for unlabeled persons. Moreover, a weighted point-to-set triplet loss is employed to adaptively assign higher (lower) weights to reliable (hard) pairs, which is more robust and effective compared with the conventional triplet loss in unsupervised RE-ID. We train our model with these two losses jointly to learn discriminative features for unlabeled persons. Extensive experiments validate the superiority of the proposed method for unsupervised RE-ID.
Out of the Closet: Lexicon Based Sentiment Analysis on Tweets about Homosexuality

Social media sites such as Twitter have given a platform to people to express their thoughts even on the most sensitive of topics. In this paper, we analyze tweets which are posted by people who acknowledged their sexuality and shared their friends and families' response publicly. In our experiment, we have used a set of Tweets data with specific hashtags to analyze the response. Context-based topic modeling was used to gather tweets surrounding this topic to get the relevant tweets. The unwanted tweets discovered by the topic model was discarded. The sentiment of these tweets was then extracted using novel methods. The motivation for this process was to find out how their declaration of sexuality was perceived. Unfortunately, homophobia is one of the battles humanity continues to fight.
Mass Communications Strategy and Practice of the Research Institutes of the Siberian Branch of the Russian Academy of Sciences

Popularization of scientific knowledge influences the public perception of science. The popularization activities of scientific institutes are organized with the participation of the press services in accordance with the organization media strategy. The interaction of press services with the media contributes to the promotion of the scientific organizations brands and personal brands of scientists, increases the social significance and relevance of research areas, promote the results of scientific research. Currently, there is an increase in publication activity in the popular science field. A study was based on the media activities data of the institutes of the Siberian Branch of the Russian Academy of Sciences. It was conducted the interdependence of the number of media publications on the availability of press structures in scientific institutions. Analysis of the communication activities of scientific organizations allowed to identify three options for interaction strategies with the media of the institutes of the SB RAS.
Hybrid Method for Cluster Analysis of Big Data

In big data analytics, deep interest in a communication known as computer-mediated has cropped up. While using traditional techniques, it is difficult to handle the data which is magnanimous. Hence, there exists a need for improved methods to handle this data since the past methods do not fit properly in all kinds of situations. Normally, there are various steps for the handling of big data like acquisition, preprocessing, and processing and analysis of this data in order to retrieve proper semantics out of that amount of data. In a similar context, clustering has evolved as a popular approach for organizing and analysis of big data. In the present research work, a hybrid method for analysis of big data is proposed. The hybrid approach consists of the blending of K-means, Ward hierarchical along with the interpolation technique. The evaluation of and validation of the proposed approach has been carried out for the city dataset in R language. In the present work, the number of clusters and the size of the data get varied while carrying out the results. The results of the proposed work reflect impressive execution times of the proposed method over the existing ones. The proposed method also presents possible recommendation for extracting specific semantics for providing insights to business recommendations.
Revisiting Approximate Polynomial Common Divisor Problem and Noisy Multipolynomial Reconstruction

In this paper, we present a polynomial lattice method to solve the approximate polynomial common divisor problem. This problem is the polynomial version of the well known approximate integer common divisor problem introduced by Howgrave-Graham (Calc 2001). Our idea can be applied directly to solve the noisy multipolynomial reconstruction problem in the field of error-correcting codes. Compared to the method proposed by Devet, Goldberg and Heninger in USENIX 2012, our approach is faster.
Governing the Generic System Development Process Using Cybernetic Principles

In a growing and a changing world, enterprises need to be constantly re-designed and re-engineered in order to be effective. The effectiveness comes up if various enterprise parts are coherent. Such coherence results from the enterprise ability to implement strategy and to manage change holistically. Thinking about an enterprise as a unified whole raises the need for a governance tool that guides the enterprise wide change. This can be done using the Viable System Model (VSM). In this paper, we consider the VSM as an organismic governance tool that embeds key architecture principles that guide the design of enterprises and their information systems. Those principles are classified along with the Generic System Development Process (GSDP) that depicts the basic steps to take in designing a system. The GSDP involves two systems, an object system that will be (re)designed to support a using system. We consider two scenarios, the first scenario concerns an enterprise that provides services to the environment and the second scenario concerns its supporting information system. We conclude that the VSM is a functional model since most of its principles concern the function design of a business. In addition, we conclude that the VSM ought to be combined with other tools in order to cover the environment scan, the business processes and the information system design.
Adaptive recommendation technology for remote sensing information based on behavior analysis

In view of the changing interests of users of remote sensing information, this paper proposes a self-adaptive recommendation technology for remote sensing information based on behavior analysis. Through real-time collection and feedback of user's browsing, querying and downloading behavior data on the application platform, users can find new concerns and construct attenuation functions. Periodically, users' preferences are revised. The adaptive learning strategy adds users' new preferences to the key information structure, which strengthens the original information preferences. This technology not only takes full account of users' historical interest points, but also dynamically discovers users' new interests, and integrates them reasonably according to interest attenuation model, so as to improve the accuracy of recommendation.
Stability of the local set of limit cycles in digitally controlled relay systems

The widespread use of relay control in technical systems and the use of digital controllers for its implementation, requires the development of appropriate theoretical methods for studying such systems. It is known that time discretization in relay automatic systems leads to the appearance of many limit cycles in local regions of the phase space. Methods have been developed for distinguishing these regions and determining the parameters of periodic motions. Moreover, the issues of assessing the stability of these movements have not been sufficiently studied. In this paper, we develop a criterion that allows us to evaluate the convergence of phase trajectories to a local set of limit cycles. To do this, we consider the mapping of switching surfaces and use the Lyapunov functions. The practical application of the obtained theoretical results by a concrete example is considered.
Intelligent Image Retrieval Based on Multi-swarm of Particle Swarm Optimization and Relevance Feedback

In recent years, Convolutional Neural Networks (CNNs) have promoted greatly the development of image retrieval, intelligent image retrieval still faces challenges. An intrinsic challenge in intelligent image retrieval exists the intention gap between the real intention of the users and the representation of users’ query, besides the well-known semantic gap. To address these problems, we propose a novel method that incorporates a relevance feedback (RF) method with an evolutionary stochastic algorithm, called multi-swarm of particle swarm optimization (MPSO), as a way to grasp the users’ perception of relevance through optimized iterative learning. One main component of our method, MPSO, can effectively prevent the retrieval system from falling into local optimal and dispose of those redundant particles, which can improve the diversity of particles. Moreover, we also present a simple but effective similarity ranking algorithm to increase retrieval speed, which can consider synthetically not only the fitness of each query point in feature space, but also the similarity of the image sequence corresponding to each query point. Extensive experiments on three publicly available datasets demonstrate that our method significantly improves the precision, recall as well as the user experience.
A Comparative Study of ANN Tuning Methods for Multiclass Daily Activity and Fall Recognition

Smart phones and other sensor-enabled devices are very frequently used daily life devices. Movement data obtained by sensors from these devices can be interpreted by artificial intelligence algorithms and this may be critically helpful in some daily life issues. Such a daily activity and fall classification mechanism is particularly important for rapid and accurate medical intervention to the elderly people who live alone. In addition, the real time human activity recognition (HAR) is important for healthcare solutions and better assistance of intelligent personal assistants (IPAs). In this study, the dataset is obtained from 6 different wearable sensors. It contains 20 daily activities and 16 fall motions on the 3060 observations. To classify these movements separately, 3 different Artificial Neural Network (ANN) training algorithms were chosen as the basis. These are gradient descent, momentum with gradient descent and Adam algorithms. Dropout and L2 regularization techniques are used to obtain better results for the test data. The results have shown that the ANN based approach correctly recognizes the daily activities and falls with 94.58% accuracy score on the test set.
Characteristics Extraction of Behavior of Multiplayers in Video Football Game

In the process of behavior recognition of multiplayers for soccer game video, various features of athletes need to be extracted. In this paper, color moments extracted by using color classification learning set are regarded as color feature. Contour features of athletes are extracted by utilizing players silhouettes block extraction and normalization. Hough transform is used to extract the features of coordinates of pitch line, which can be used for camera calibration, rebuilding the stadium, and calculating the coordinate of players in the real scene. The trajectories of players and ball are predicted by using Kalman filter, while trajectories characteristics of player and ball are extracted by using the trajectory growth method. Temporal and spatial interest points are extracted in this paper. Experimental results show that the accuracy of behavior recognition can be greatly improved when these features extracted are used to recognize athlete behavior.