Reviewerly/exHarmony huggingface dataset:
Adapting Cross-Lingual Model to Improve Vietnamese Dependency Parsing

Dependency parsing is the task of analyzing the syntax of a sentence into a direct binary relational graph. Many languages have had the state-of-the-art model in this task and used it as a knowledge base to solve more complex problems. However, to achieve high accuracy in a dependency parsing model, it takes significant time and labor to build a large amount of annotated treebanks. For languages with little or no annotated treebanks, some approaches have been studied to induce a dependency parser from treebanks of high-resource languages to solve this problem. In this paper, we propose an approach to building a cross-lingual model to parse Vietnamese as a low-resource target language. The model uses English as a supportive high-resource source language to induce a Vietnamese parser. To remove the differences in syntaxes and lexicons of English and Vietnamese when training the model, the approach uses a filtering algorithm to choose English sentences having syntaxes as same as Vietnamese sentences based on Euclidean distance. The result shows that the proposed model significantly improves accuracy compared with models using only supervised mono-lingual treebanks.
ONTOLOGICAL MODELLING OF THE KNOWLEDGE BASE OF THE TRAVEL ORGANIZATION

In modern conditions of society development, increasing degree and pace of integration of information technology achievements in the field of human life, traditional approaches to building information systems become too cumbersome or cease to be effective. One of the ways to solve this problem is to develop knowledge-based systems. The work is devoted to ontological modeling of a new subject area "travel organization". The ontology is considered in the context of knowledge exchange. The created travel ontology is quite modern and relevant today. The developed ontological model of the knowledge base in this area can be implemented on thematic web resources and greatly facilitate the semantic search for information within the subject area in comparison with existing ones. A terminological dictionary from this subject area is defined using the concept of terminological system. An analysis of the possibilities of the Protege ontology development environment for modeling a specific subject area of "travel". The basic principle of ontology modeling in the form of a semantic network is chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area of "travel". The use of the Protege environment to implement the ontological model of the knowledge base allowed to use the advantages and features of the created model of "travel organization", such as: functionality, transitivity, reflectivity, structuring, completeness, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base is chosen. Testing of the developed ontological knowledge base was carried out and a rather high level of its correctness in the process of information retrieval was confirmed. The average metric on the SUM metric for all users is equal to 82.95%, which is an acceptable indicator of the ontological knowledge base. When using the classical relational model of database organization to implement the "travel" database, the average SUM metric for 10 users is 73.68%. An example of the developed ontology in Protege is considered, a graphic representation of the basic graph of the ontological mode "travel" is given, the model includes 10 classes and subclasses, for each class and subclass 2 properties-relations and from 2 to 10 properties-data are defined, an example is given classes of the ontological model of "travel", an example of "properties-relations" of the ontological model of "travel" is given, an example of "properties-data" of the ontological model of "travel" is given. Possible directions of further development of the ontological model "travel organization" are formulated.
Development of a Geoinformation Monitoring Module for the Khankalsky Geothermal Deposit in the Chechen Republic

Geothermal energy is one of the renewable energy sources that can become an alternative to increasingly scarce fossil fuels. The Chechen Republic, in particular, the Khankalskoe field has a huge potential of geothermal energy. This research work involves the development, on the basis of a domestic geoinformation systems (further GIS), of a module “Geothermy” (further GIS-module) to monitor the exploited geothermal fields in order to protect the environment from possible negative impacts. The GIS-module contains many functions such as a map view mode, queries, navigation through the monitoring region and displaying the properties of geothermal wells, operational access to databases in order to improve the efficiency of management decision-making. The proposed method in conjunction with geoinformation monitoring of the geological environment, using modern methods and technologies makes it possible to identify zones of dynamic activity of the Earth’s surface, areas of thermal water spill, as well as processes associated with the release of carbon dioxide, which will subsequently help prevent the emergence of emergency situations.
FrameNet: Tabular Image Preprocessing Based on UNet and Adaptive Correction

Detecting and recognizing objects in images with complex backgrounds and deformations is a challenging task. In this work, we propose FrameNet, while a deep table lines segmentation network based on our Res18UNet with an adaptive deformation correction algorithm for correcting the table lines. We use Itinerary/Receipt of E-ticket for Air Transport to evaluate our methods. The experiment results show that our Res18UNet can reduce the number of parameters and improve the speed of image segmentation without significantly reducing the segmentation accuracy, and our correction method can better correct the perspective deformation and some distorted tabular images with no dependence on pixel-level ground truth image. In addition, we also apply our model and method to VAT invoice dataset and prove that they also have better transfer ability.
Design of Intelligent Recognition Model for English Translation Based on Deep Machine Learning

Machine translation has developed vigorously these years. However, there are still many problems in machine translation that need to be solved urgently. The aim of the thesis is to study the designing of intelligent recognition model for English translation based on deep machine learning. The neural translation system of this article separates larger words and sentences into translation, and then corrects errors, reducing the problem of poor neural translation performance when recognizing sentence length in English translation. Experimental research shows that the use of the English translation intelligent recognition model of this thesis improves the efficiency of 85% in the convergence speed of the original translation training.
Formation and Development of Information and Communication Competencies of Pedagogical Universities Students: Experience of Ukraine

The content of information and communication competence (ICC) is defined by researchers differently. In Ukraine the investigation of its formation in pedagogical university students takes place in the process of studying, systematizing, and generalizing Ph.D. dissertations defended in specialized academic councils of Ukraine. The purpose of this article is to analyze dissertations for the degree of Candidate and Doctor of Sciences devoted to ICC of future teachers during their professional training at pedagogical universities of Ukraine. The research analysis is based on the overview of dissertations for 2010–2020 and is performed according to 5 identified criteria: general characteristics; theoretical framework; research significance; quantitative indicators of the experiment; tools of experimental research. Using a set of methodological approaches, researchers developed proposals for the formation of future teachers’ ICC, based on pedagogical experiment results. It is proved that the effectiveness of forming ICC is positively influenced by the following factors: the created innovative models considering the specifics of teacher training in the relevant field; author's methods based on the use of mobile, computer, cloud-based technologies; educational and methodical provision for special courses in the practical application of IC technologies, etc. Particular attention is paid to the justification of appropriate pedagogical conditions and the development and adaptation of diagnostic techniques for conducting the monitoring of components of ICC at different stages of the pedagogical experiment.
Rinascimento: Playing <i>Splendor</i>-Like Games With Event-Value Functions

In the realm of games research, artificial general intelligence algorithms often use score as the main reward signal for learning or playing actions. However, this has shown its limitations in scenarios where the rewards are very rare or absent until the end of the game. The problem is even more severe when the computational budget available is limited. This article proposes a new approach based on event logging: the game state triggers an event every time one of its features changes. These events are processed by an event-value function (EF) that assigns a value to a single action or a sequence. Experiments show that this approach can mitigate the problem of scarce rewards and improve the artificial intelligence performance compared with both the point-based heuristics and state-value functions. Furthermore, this represents a step forward in a finer control of the strategy adopted by the artificial agent, by describing a much richer and controllable behavioral space through EFs. Tuned EFs are able to neatly synthesize the relevance of the events in the game. Agents using an EF are also more robust when playing games with several opponents.
Fine-Tuning of a BERT-Based Uncased Model for Unbalanced Text Classification

Unbalanced datasets make it hard for text classifiers to learn well. Having limited information in minority classes makes it difficult to classify the unbalanced texts. In this study, a BERT-based uncased model is developed and fine-tuned to address the unbalanced text classification problem. We use the BERT model with 12 layers and 110 M parameters in order to classify an unbalanced dataset, taking into account both the majority and minority groups. The model is being fine-tuned by varying the learning rate and the maximum token length. Precision, recall, and f-measure are used to measure the performance of the BERT-based uncased model to classify text.
A Framework for the Evaluation of Factors Affecting Smart Contract Adoption and Enforceability in Port Supply Chain Industry in Ghana

This study examines the adoption of smart contracts from the legal, technological, and cultural perspectives in the port supply chain network and how these factors influence smart contracts enforceability. This study follows a Design Science Research (DSR) approach that guides developing IT artefacts and their use in practice. To develop our framework, relevance was achieved by investigating shortcomings of smart contract implementations relative to enforceability within the knowledge base of blockchain technology implementations. Additionally, thirty-five major stakeholders were interviewed for their lived experiences. The constant comparative analytical method was applied using open coding for initial categorization of smart contract concepts and axial coding for removal of overlapping concepts while iteratively testing the concepts against data. The factors that emerge from the papers and the expert statements on the enforceability of the smart contract in the ports were abstracted and analysed for their interconnections. This was translated into a framework by semantically grouping the factors in categories. The study contributes to the scientific literature by synthesizing and operationalizing previous research efforts and empirical evidence from a case study into a novel framework for the evaluation of smart contracts adoption and enforceability. Practitioners can use the framework to understand the main factors of success or failure of the broader adoption of smart contracts in the port supply chain.
Visual Dialogue

Visual dialogue is an important and complicated vision language task that processes the visual features of images and textual features of captions, questions and histories to answer questions. To accomplish this task, the machine must exhibit the abilities of perception, multimodal reasoning, relationship mining and visual coreference resolutionVisual coreference resolution. In this chapter, we briefly describe the challenges associated with this method and introduce the two benchmarks. Subsequently, a comprehensive review of the associated methods is presented, which are classified into four categories.
Different Texture Segmentation Techniques: Review

Segmentation is an important feature in the field of image processing and plays an important role in various applications of image processing and computer vision. The segmentation of image can be done on the basis of texture known as texture segmentation which is a process of dividing the image into various parts depending on the different textures present within the image to be processed. Texture being one of the most important features in image processing has various methods available for the segmentation process like Gabor filter, thresholding, cluster based methods, edge based methods, supervised and unsupervised segmentation, region based methods, histogram based methods etc. the paper discusses about various methods used for texture segmentation used in image analysis and address about the advantages and disadvantages of the methods and the challenges faced. The main objective is to study various methods available in the research for segmentation of texture based images.
A High-Capacity and High-Security Generative Cover Steganography Algorithm

Steganography is an important technology in the field of data hiding. The application of deep learning in the field of steganalysis has led to the rapid development of steganalysis techniques, which seriously threatens the security of steganography. In addition, capacity is another important factor that limits the safety of steganography. The larger capacity of the Steganographic image (stego image), the poorer its visual quality and security. To improve the security of steganography, this paper proposes a cover generative steganography, which constructs Generator, Encoder-Decoder, Steganalysis Optimization Network, and Steganalysis Adversarial Network. The Generator input original images output generate covers which more suitable for embedding secret images; Encoder-Decoder uses existing steganographic models to embed and extract secret images from covers and stego images; Steganalysis Optimization Network optimizes the current optimal steganalysis parameters; Steganalysis Adversarial Network evaluates the security of steganography and provides steganalysis adversarial loss (SDO_loss) to the Generator. In addition, we use the stego image and cover loss (SMSE_loss), extract images and secret image loss (XMSE_loss), and steganalysis adversarial loss (SDO_loss) jointly to form the total loss function of the generator. When embedding secret images of the generated cover, it has lower color distortion and higher resistance to steganography through the joint loss function. It is experimentally demonstrated that our model can reduce the color distortion and the steganalysis detection accuracy by about 30–40% with higher security than the previous steganography model for hiding color images based on deep learning.
Improving Students’ Learning Experience Using Simulated Environments in Applied Degree Education in Architecture, Engineering, and Construction

An applied degree education is about using work-integrated learning approaches in student learning. Unfortunately, the Covid-19 pandemic has severely affected how applied education may continue to enrich students’ knowledge of the real world. In various degree programs, a simulated environment is an essential part of the curriculum and applying it to applied degree education cannot be ignored. A simulated environment plays an essential role in linking theoretical knowledge acquired in the classroom to the real world. It exposes students to real-world problems but in a controlled simulated environment. This chapter introduces several approaches in creating simulated environments for applied degree programs in Architecture, Engineering, and Construction (AEC). It discusses the key benefits of using technologies, such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and the A Real Organization Unit Simulated as Life (AROUSAL) system, to improve the student learning experience in applied degree education in AEC.
Software Tool for Thematic Evolution Analysis of Scientific Publications in Spanish

Retrieving information from text documents on a specific theme is of great interest in different areas. A difficulty that is always present when documents from different periods are analyzed is the thematic evolution, an aspect that impacts the quality and results of the searches carried out. Identifying changes in terminology and the evolution in study themes is of great interest to disciplines such as bibliometrics and scientometrics. This article describes the development of a software tool capable of implementing the steps of the methodology suggested in [2] and [3]. Using various bibliometric indicators, this methodology can analyze thematic evolution in scientific documents. In addition, the software tool proposed here will allow the user to experiment with new metrics and modifications to the methodology more easily by applying incremental iterative development. The tests carried out allow us stating that the inclusion index is an adequate metric to select the most relevant theme relationships, facilitating the understanding and visualization of the results obtained.
“Negative” Results—When the Measured Quantity Is Outside the Sensor’s Range—Can Help Data Processing

In many real-life situations, we know the general form of the dependence $$y=f(x,c_1,\ldots ,c_m)$$ between physical quantities, but the values $$c_i$$ need to be determined experimentally, based on the results of measuring x and y. In some cases, we do not get any result of measuring y since the actual value is outside the range of the measuring instrument. Usually, such cases are ignored. In this paper, we show that taking these cases into account can help data processing—by improving the accuracy of our estimates of $$c_i$$ and thus, by improving the accuracy of the resulting predictions of y.
Design and Implementation of Management Information System Based on Network Environment

The main purpose of developing the student management information system based on the school factor network environment is to realize the automatic processing of university student management information. This paper introduces and describes the student management information system of Shandong Institute of architecture and engineering from the aspects of development background, design date standard, information sharing, function design, technical characteristics and operation environment. While focusing on the organization, storage and extraction of data, the system also takes a variety of measures, such as the setting of multi-level permissions to ensure data security; the use of triggers and stored procedures to ensure data consistency; the automatic generation of combination conditions and the setting of various code bases to ensure system flexibility.
Association Extraction and Recognition of Multiple Emotion Expressed in Social Texts

Detecting the sentiment people present in social media such as tweets is important for politics, commerce, education and so on. The task of multiple emotion recognition in texts is to predict a set of emotion labels that expressed in sentences. There are still some shortcomings in the current works: 1) the dependencies among emotions are not well modeled due to the complex combinatorial features of them, 2) the semantics of emotion labels as well as the semantic correlations between emotion labels and sentences are not fully considered. In this paper, in the purpose of capturing the dependencies between emotions, we propose a new method by using Graph Convolutional Network (GCN) based on a label co-occurrence matrix building from the dataset, and a Convolutional Neural Network (CNN) is used to capture the syntactic and semantic information in the sentences through different convolutional filters, the outputs of GCN and CNN are multiplied together to fuse their features as the last output. Experiments on SemEval2018 Task1: E-c multi-label emotion recognition problem show that metrics have been significantly improved, and our approach obviously obtains the dependencies among emotions described by Pointwise Mutual Information (PMI) which measures the correlations between emotions both in the true test labels and predicted labels.
SelectGoREATech: Selection of Goal-Oriented Requirements Elicitation and Analysis Techniques

Requirements elicitation techniques are used to understand and identify the software requirements. Several methods have been developed to elicit the software requirements according to the stakeholders' needs. However, it has been observed that a single requirements elicitation technique cannot identify software requirements. So, different techniques are employed to understand and elicit the requirements of the software. Based on our review, we found that existing methods do not support the selection of requirements elicitation when a goal-oriented approach is used to identify the software requirements. Therefore, to address this issue, a methodology has been proposed to select the goal-oriented requirements elicitation techniques, i.e., SelectGoREATech. Finally, the explanation of the proposed methodology is discussed with the help of an example.
Design of a Brief Perceptual Loss Function with Hadamard Codes

Perceptual loss functions are central to an ever-increasing number of tasks across computer vision. Those functions rely on high dimensional real-valued deep feature vectors. This paper proposes a succinct perceptual loss function based on Hadamard codes. The representation obtained is binary; it has a tiny memory footprint of only 128 bytes per image in the ImageNet collection. Moreover, the representative is agnostic to the architecture, enabling the use of ResNet or EfficientNet with less parameters than VGG or AlexNet, which have been the standard architecture for many tasks. We obtained high accuracy using the proposed binary codes. Paired with the Half-Space Proximal, an instance-based classifier alternative to $k$NN, our proposal improves SOTA performance in knowledge transfer across several image databases.