Reviewerly/exHarmony huggingface dataset:
USING WEB QUEST TECHLOGIES IN FUTURE PRIMARY SCHOOL TEACHERS’ TRAINING

The intensive development of educational technologies is presenting serious challenges to education in Ukraine, which requires to review educational content, forms, teaching methods and technologies for promoting pupils’ cognitive activities, increasing their motivation that enhance the development of professional and key life competencies, which enables to bring up a personality ready for independent creative professional activities. The main innovations in education are interactive technologies along with ICT, therefore there is an urgent need in training future primary school teachers in using those technologies, particularly in organizing educational web quest. The notion of quest, types of quests, quest technologies and the innovating teaching method such as web quest technology are analyzed in the article. The experience in using web quest technology in training future primary school teachers has been examined in Ukraine and other countries. In this work, the peculiarities of using web quest technology are considered and the necessity of using new approaches in training future primary school teachers is substantiated, the advantages of integrating modern internet technologies into educational process, the usage of web quests at lessons and during self-studying are described. The notion of web quest is clarified, its typology is given, as well as the possibilities of using this innovative technology in educational process in a higher educational institution is considered. In the article the practical usage of web quest on the platform Padlet is described, the peculiarities of organizing work based on web quest technology for a teacher and the requirements to a student’s level of training are given. Using web quest technology with the virtual interactive board Padlet in training future primary school teachers is quite an efficient way to develop theoretical and practical competencies of a specialist who will be able to plan their own professional activity and work in a team to achieve the set objectives.
Capturing User Intention to Enhance Image Retrieval and Re-Ranking Efficiency Using One Click Image Search

A Novel data fusion technique to support text-based and content-based image retrieval combining different heterogeneous features is proposed. The user need to give just a single click on an query image and images recovered by content based search are re-positioned dependent on their visual and texture similitudes to the query image.Textual and visual expansions are integrated to capture user intention without additional human feedback. Expanded keywords helps in extending positive model images and furthermore develop the image pool to include more relevant images. A lot of visual features which are both efficient and effective for image search are chosen. The n-dimensional feature vector for both colour and texture is reduced to single dimension each, used for comparing the similarity with query image using suitable distance metrics. Further only the images retrieved as a result of text based search and image re-ranking process are compared during run time for finding the similar images; not the entire database. This considerably reduces the computational complexity and improves the search efficiency. With improved feature extraction capturing textual and visual similarities, the proposed one click image search framework gives a productive robotized recovery of comparable images giving promising results with improvement in retrieval efficiency.
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives

Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations, including but not limited to discourse with no clear objective.Along with these requirements, agents are expected to extrapolate intent from the user's dialogue even when subjected to non-canonical forms of speech.This depends on the agent's comprehension of paraphrased forms of such utterances.Especially in low-resource languages, the lack of data is a bottleneck that prevents advancements of the comprehension performance for these types of agents.In this regard, here we demonstrate the necessity of extracting the intent argument of non-canonical directives in a natural language format, which may yield more accurate parsing, and suggest guidelines for building a parallel corpus for this purpose.Following the guidelines, we construct a Korean corpus of 50K instances of question/command-intent pairs, including the labels for classification of the utterance type.We also propose a method for mitigating class imbalance, demonstrating the potential applications of the corpus generation method and its multilingual extensibility.
SPECIFICITY OF PROOF IN CASES OF INFRINGEMENT OF INTELLECTUAL PROPERTY RIGHTS ON SITES ON THE INTERNET

In recent years, the rapid development of both technical progress and activities on the Internet, including the economic one, could not but affect the improvement of existing and the emergence of new mechanisms for violating the rights of intellectual property subjects, gives rise to various disputes between participants in the relevant relations, and such disputes is no exception, since each category of court cases has its own peculiarities of consideration. However, analyzing the judicial practice, a number of procedural problems related to evidence and proving in the field of intellectual property rights protection on Internet sites were identified. – Object of Research: What is the key of proof? – Where? and Who? need to collect evidence? – What is meant by belonging and admissibility of evidence? – What are the ways of securing facts as means of proof on the Internet? Investigated problem: to give a legal assessment not only to the norms of national and international legislation, but also to the practical experience of using the institution of proof in the protection of intellectual property rights raised on sites on the Internet. The main scientific results: the procedural nuances of proving the violation of intellectual property rights on sites on the Internet are highlighted, problematic points are identified, and the proposed optimal ways to overcome them, both in pre-trial and in court. The area of practical use of the research results: fixation, preservation of the evidence itself, their assessment, both in pre-trial and in court. Innovative technological product: an algorithm (technical and legal) securing factual data from the Internet site for submitting them to court. Scope of application of the innovative technological product: the application of the algorithm for securing factual data from the Internet site when protecting intellectual property rights should be carried out in accordance with the rules of the current Economic Procedure Code of Ukraine, namely Chapter 5 "Evidence" of Section 1.
Multi Object Tracking for Similar Instances: A Hybrid Architecture

Tracking and segmentation of moving objects in videos continues to be the central problem in the separation and prediction of concurrent episodes and situation understanding. Along with critical issues such as collision avoidance, tracking and segmentation have numerous applications in other disciplines, including medicine research. To infer the potential side effects of a given treatment, behaviour analysis of la-boratory animals should be performed, which can be achieved via tracking. This presents a difficult task due to the special circumstances, such as the highly similar shape and the unpredictable movement of the subject creatures, but a precise solution would accelerate research by eliminating the need of manual supervision. To this end, we propose Cluster R-CNN, a deep architecture that uses clustering to segment object instances in a given image and track them across subsequent frames. We show that pairwise clustering coupled with a recurrent unit successfully extends Mask R-CNN to a model capable of tracking and segmenting highly similar moving and occluded objects, providing proper results even in certain cases where related networks fail. In addition to theoretical background and reasoning, our work also features experiments on a unique rat tracking data set, with quantitative results to compare the aforementioned model with other architectures. The proposed Cluster R-CNN serves as a baseline for future work towards achieving an automatic monitoring tool for biomedical research.
Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model

In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.
Development of VR Tactile Educational Tool for Visually Impaired Children: Adaptation of Optical Motion Capture as a Tracker

This paper deals with basic research on a tactile virtual reality (VR) system for visually impaired children using optical motion capture and a haptic device.Generally, tactile educational tools for blind children or teaching materials have been produced one by one according to their size and shape.As a result, they take a lot of time to produce and are expensive.In recent years, since 3D printers have been adopted to make tactile educational tools, these problems are being solved.However, it is difficult to use tactile educational tools to show objects moving or growing.We are attempting to develop tactile educational tools for presenting the shapes of objects to visually impaired children using VR.In this paper, as the first step, the development of a trial system allowing an object placed in virtual space to be touched by combining optical motion capture and a haptic device is explained.From the experimental results, the system was found to be effective for expressing a simple-shaped object in virtual space to a user without visual information.
Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index

Detection and analysis of informative keypoints is a fundamental problem in image analysis and computer vision. Keypoint detectors are omnipresent in visual automation tasks, and recent years have witnessed a significant surge in the number of such techniques. Evaluating the quality of keypoint detectors remains a challenging task owing to the inherent ambiguity over what constitutes a good keypoint. In this context, we introduce a reference based keypoint quality index which is based on the theory of spatial pattern analysis. Unlike traditional correspondence-based quality evaluation which counts the number of feature matches within a specified neighborhood, we present a rigorous mathematical framework to compute the statistical correspondence of the detections inside a set of salient zones (cluster cores) defined by the spatial distribution of a reference set of keypoints. We leverage the versatility of the level sets to handle hypersurfaces of arbitrary geometry, and develop a mathematical framework to estimate the model parameters analytically to reflect the robustness of a feature detection algorithm. Extensive experimental studies involving several keypoint detectors tested under different imaging scenarios demonstrate efficacy of our method to evaluate keypoint quality for generic applications in computer vision and image analysis.
Evaluating Word Embeddings on Low-Resource Languages

The analogy task introduced by Mikolov et al. (2013) has become the standard metric for tuning the hyperparameters of word embedding models.In this paper, however, we argue that the analogy task is unsuitable for lowresource languages for two reasons: (1) it requires that word embeddings be trained on large amounts of text, and (2) analogies may not be well-defined in some low-resource settings.We solve these problems by introducing the OddOneOut and Topk tasks, which are specifically designed for model selection in the low-resource setting.We use these metrics to successfully tune hyperparameters for a lowresource emoji embedding task and word embeddings on 16 extinct languages.The largest of these languages (Ancient Hebrew) has a 41 million token dataset, and the smallest (Old Gujarati) has only a 1813 token dataset.
Deep artificial neural network based on environmental sound data for the generation of a children activity classification model

Children activity recognition (CAR) is a subject for which numerous works have been developed in recent years, most of them focused on monitoring and safety. Commonly, these works use as data source different types of sensors that can interfere with the natural behavior of children, since these sensors are embedded in their clothes. This article proposes the use of environmental sound data for the creation of a children activity classification model, through the development of a deep artificial neural network (ANN). Initially, the ANN architecture is proposed, specifying its parameters and defining the necessary values for the creation of the classification model. The ANN is trained and tested in two ways: using a 70-30 approach (70% of the data for training and 30% for testing) and with a k-fold cross-validation approach. According to the results obtained in the two validation processes (70-30 splitting and k-fold cross validation), the ANN with the proposed architecture achieves an accuracy of 94.51% and 94.19%, respectively, which allows to conclude that the developed model using the ANN and its proposed architecture achieves significant accuracy in the children activity classification by analyzing environmental sound.
Bata-Unet: Deep Learning Model for Liver Segmentation

In computer vision, image segmentation is defined as process of a partition of an image in a number of regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning revolution traditional handcrafted features were used for liver segmentation but with deep learning the features are obtained automatically. There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using 3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in we added a batch-normalization layer an after each convolution layer in both construction path and expanding path. The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for 3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we compare it with them.
Guiding Attention for Self-Supervised Learning with Transformers

In this paper, we propose a simple and effective technique to allow for efficient selfsupervised learning with bi-directional Transformers.Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities.We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns.Our method is agnostic to the actual pretraining objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in lowresource settings.Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance. 1
WEIDJ: Development of a new algorithm for semi-structured web data extraction

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.
Local earthquake tomography at the Los Humeros geothermal field, Mexico

&amp;lt;p&amp;gt;The GEMex&amp;lt;sup&amp;gt;*&amp;lt;/sup&amp;gt; project is a recently finalized European-Mexican collaboration that aimed to improve the understanding of two geothermal fields: Acoculco and Los Humeros Volcanic Complex . These sites are located in the Trans-Mexican Volcanic Belt, a region that hosts numerous active volcanoes and is favorable for geothermal exploitation. Currently, the &amp;amp;#160;Los Humeros Volcanic Complex is one of Mexico&amp;amp;#8217;s main geothermal systems with an installed capacity of ~95MW. Many studies have been performed at this site since the 70s highlighting several features and characteristics of the shallow subsurface. However a thorough knowledge of structures and behavior of the system at greater depths is still quite sparse. Hence one main objective of the GEMex project was to conduct several geological, geochemical, and geophysical experiments to investigate deeper structures for future development of local and regional geothermal resources.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this framework, for the period of one year (September 2017 to September 2018), a seismic array consisting of 45 seismic stations was set to record continuously at the Los Humeros Volcanic Complex. In this study we analyzed the continuous seismic records to detect the micro-seismicity mainly related to exploitation activities. After applying a recursive STA/LTA detection algorithm, we assembled and manually picked P- and S- phases of a catalog of about 500 local events. The detected events were mostly clustered around injection wells, with fewer events located close to known structures. We use the retrieved catalog to derive a new minimum 1D velocity model for the Los Humeros site. We then performed a joint inversion to obtain the 3D Vp and Vp/Vs structures of the geothermal field. A post-processing averaging of several inversions was also computed to increase resolution of the investigated region. In this study we will show the derived Vp and Vp/Vs models for the &amp;amp;#160;Los Humeros Volcanic Complex to emphasize various underground structures and potentially identify possible variations due to changes in temperature, fluid content, and rock porosity.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;*This project has received funding from the European Union&amp;amp;#8217;s Horizon 2020 research and innovation programme under grant agreement No. 727550 and the Mexican Energy Sustainability Fund CONACYT-SENER, project 2015-04-68074. We thank the Comisi&amp;amp;#243;n Federal de Electricidad (CFE) for kindly granting the access to the geothermal field for installation and maintenance of seismic stations.&amp;lt;/p&amp;gt;
Design Principles for Blockchain-Enabled Point Exchange Systems: An Action Design Research on a Polycentric Collaborative Network for Loyalty Programs

In order to improve the customer experiences and explore value of loyalty programs, the operators are extending the scope of point redemption through creating a strategic polycentric collaborative network (CN) across multiple LPs. The point exchange is considered as a selectable approach. Because of the complexity, multicurrency and security risk of the CN, the existing process for exchanging points is cumbersome. However, the challenge of corresponding information system (IS) design received few attentions, which is critical for the success of the CN. We fill the gap by introducing blockchain technology and conducting an action design research (ADR). We partnered with a consulting corporate to design blockchain-enabled point exchange systems for the polycentric CN of loyalty programs. Our ADR study is in progress and the preliminary findings of new design principles may contribute to design theory in the blockchain and IS literature, and enrich both research and practice on LP.
Divide and Conquer: From Complexity to Simplicity for Lay Summarization

We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20.The task is to produce nontechnical summaries of scholarly documents.The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article.We propose a two step divide-and-conquer approach.First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method.Next, we perform abstractive summarization on these extractions and systematically merge the abstractions.We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary.Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.