Reviewerly/exHarmony huggingface dataset:
BLACK SWAN GLOBAL MARKET: ANALYSIS OF THE EFFECT OF THE COVID-19 DEATH RATE ON THE VOLATILITY OF EUROPEAN FOOTBALL CLUB STOCK PRICES (CASE STUDY OF JUVENTUS F.C., MANCHESTER UNITED, AJAX AMSTERDAM AND BORUSSIA DORTMUND)

Abstract: The Covid-19 pandemic that hit the world also directly affected financial markets and global stock markets; this condition in economic terminology is known as the Black Swann Global Market Effect. Black Swan Global Market Effect is also experienced by sports industries in the financial industry, the football industry. The purpose of this paper is to see whether there is an influence between the Covid-19 pandemic conditions on the share value of several major European football clubs, namely Ajax Amsterdam, Borussia Dortmund, Juventus F.C., and Manchester United, as a result of the Black Swan Global Market Effect. The data used in this paper is time-series data from March 2020 to August 2020. Meanwhile, to answer the black swan effect phenomenon, the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) method is used. The results showed that stocks that were the object of research (Ajax, Borussia Dortmund, Juventus, and Machester United) showed a large response to bad News (an increase in deaths due to covid-19). Abstrak:Pandemic covid-19 yang mengantam dunia juga secara langsung mempengaruhi pasar keuangan serta pasar saham global, kondisi ini dalam terminology ekonomi dikenal sebagai Black Swann Global Markert Effect. Black Swan Global Market Effect hal ini juga dialami industry-industri olahraga yang berada dalam industry keuangan tersebut salah satunya industry sepakbola.Tujuan penulisan ini adalah untuk melihat apakah terdapat pengaruh antara kondisi pandemic covid-19 terhadap nilai saham beberapa klub sepakbola besar eropa yaitu Ajax Amsterdam, Borussia Dortmund, Juventus FC, dan Manchester United sebagai akibat dari Black Swan Global Market Effect.Data yang digunakan dalam penulisan ini adalah data time series dari bulan maret 2020 hingga agustus 2020. Sementara untuk menjawab fenomoena black swan effect ini digunakan metode Threshold Generalized Autoregressive Conditional Heteroskedacity (TGARCH). Hasil Penelitian menunjukkan bahwa, saham-saham yang menjadi objek penelitian (Ajax, Borussia Dortmund, Juventus, dan Machester United) menunjukan respons yang besar terhadap bad news (peningkatan jumlah kematian akibat covid-19). Black Swan Global Market, Pandemi Covid-19, TGARCH Models
Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications (e.g., passage-based retrieval and high-quality information extraction). However, long documents, containing hundreds or even thousands of pages and a variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper position on the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. As for evaluations, we find that previous studies ignore the error that the depth of a node is correct while its path to the root is wrong. Since such mistakes may worsen the downstream applications seriously, a new measure is developed for a more careful evaluation. The empirical experiments based on thousands of long documents from Chinese financial market, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972 6, 0.729 1 and 0.957 8 in the Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.
ARCritique: Supporting Remote Design Critique of Physical Artifacts through Collaborative Augmented Reality

Design critique sessions require students and instructors to jointly view and discuss physical artifacts. However, in remote learning scenarios, available tools (such as videoconferencing) are insufficient due to ineffective, inefficient communication of spatial information. This paper presents ARCritique, a mobile augmented reality application that combines KinectFusion and ARKit to allow users to 1) scan artifacts and share the resulting 3D models, 2) view the model simultaneously in a shared virtual environment from remote physical locations, and 3) point to and draw on the model to aid communication. A preliminary evaluation of ARCritique revealed great potential for supporting remote design education.
Facial Expression Recognition Using 3D Points Aware Deep Neural Network

Point cloud-based Deep Neural Networks (DNNs) have gained increasing attention as an insightful solution in the study field of geometric deep learning. Point set aware DNNs have proven capable of dealing with the unstructured data type and successful in 3D data applications such as 3D object classification, segmentation and recognition. On the other hand, two major challenges remain understudied when it comes to the use of point cloud-based DNNs for 3D facial expression (FE) recognition. The first challenge is the lack of large labelled 3D facial data. The second is how to obtain a point-based discriminative representation of 3D faces. To address the first issue, we suggest to enlarge the used dataset by generating synthetic 3D FEs. For the second one, we propose to apply a level-curve based sampling strategy in order to exploit crucial geometric information. The conducted experiments show promising results reaching 97.23% on the enlarged BU-3DFE dataset.
Mathematic-systemic researching with the involvement of fractals when processing of space surveillance systems on recognition of waste disposal objects

The article analyzes the factors that stimulate research and development of an automated system for processing and analyzing satellite imagery data. In particular, the study of satellite imagery for the presence of waste disposal objects is considered as an application. This problem is of particular interest when conducting scientific research. The development of automated control systems (ACS) in the field of space image (SI) recognition is characterized primarily by the approach of interest, i.e. for what purpose and for solving of which particular problem of the subject area the system is created. Obviously, the system is a set of certain information technologies and the main factor in the development of an automated system is and remains the study of peculiarities of creating an appropriate information system (IS). The article proposes a methodological approach to the processing and analysis of data by the IS. The approach is based on the conceptual idea of fractal sets. This enhances the detection of abnormal signals in difficult phono-target environments. Testing is conducted on the detection of WDS under conditions of a low signal-to-noise ratio.
Handwritten Equations Solver using CNN

Robust handwritten character recognition is a tricky job in the area of image processing. Among all the problems handwritten mathematical expression recognition is one of the complicated issues in the area of computer vision research. Segmentation and classification of specific characters makes the task more difficult. In our project, handwritten numbers and symbols are read and further addition, subtraction and multiplication operations are carried out. This project involves the study of Convolutional Neural Networks and deployment of the model using Flask. Various libraries such as opencv, keras etc. Were used for this project. For classification of specific characters we apply Convolutional Neural Network. Each of the correct detection, character string operations is used for the solution of the equation. Finally the experimental resultsshow the great effectiveness of our proposed system.This system is helpful for students who want to get the answers to the handwritten equation.This can be further expanded to more complex equations and can be trained on more user data to further improve accuracy.
AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

Graph Convolutional Network (GCN) is adopted to tackle the problem of convolution operation in non-Euclidean space. Previous works on GCN have made some progress, however, one of their limitations is that the design of Adjacency Matrix (AM) as GCN input requires domain knowledge and such process is cumbersome, tedious and error-prone. In addition, entries of a fixed Adjacency Matrix are generally designed as binary values (i.e., ones and zeros) which can not reflect the real relationship between nodes. Meanwhile, many applications require a weighted and dynamic Adjacency Matrix instead of an unweighted and fixed AM, and there are few works focusing on designing a more flexible Adjacency Matrix. To that end, we propose an end-to-end algorithm to improve the GCN performance by focusing on the Adjacency Matrix. We first provide a calculation method called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entropy</i> to update the matrix. Then, we perform the search strategy in a continuous space and introduce the Deep Deterministic Policy Gradient (DDPG) method to overcome the drawback of the discrete space search. Finally, we integrate the GCN and reinforcement learning into an end-to-end framework. Our method can automatically define the Adjacency Matrix without prior knowledge. At the same time, the proposed approach can deal with any size of the matrix and provide a better AM for network. Four popular datasets are selected to evaluate the capability of our algorithm. The method in this paper achieves the state-of-the-art performance on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cora</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pubmed</i> datasets, with the accuracy of 84.6% and 81.6% respectively.
A Topology on a Hyper BCI-algebra Generated by a Hyper-order

In this paper, we introduce an operator on a hyper BCI-algebra via application of aleft hyper-order. The family consisting of the images of subsets under the operator turns out to be a base for some topology on the hyper BCI-algebra. We investigate some important properties of the induced topology on certain hyper BCI-algebras. In particular, we show that the generated topology on a non-trivial hyper subalgebra of an ordered hyper BCI-algebra coincides with the relative topology on this hyper subalgebra.
Image Representation Learning by Transformation Regression

Self-supervised learning is a thriving research direction since it can relieve the burden of human labeling for machine learning by seeking for supervision from data instead of human annotation. Although demonstrating promising performance in various applications, we observe that the existing methods usually model the auxiliary learning tasks as classification tasks with finite discrete labels, leading to insufficient supervisory signals, which in turn restricts the representation quality. In this paper, to solve the above problem and make full use of the supervision from data, we design a regression model to predict the continuous parameters of a group of transformations, i.e., image rotation, translation, and scaling. Surprisingly, this naive modification stimulates tremendous potential from data and the resulting supervisory signal has largely improved the performance of image representation learning. Extensive experiments on four image datasets, including CIFAR10, CIFAR100, STL10, and SVHN, indicate that our proposed algorithm outperforms the state-of-the-art unsupervised learning methods by a large margin in terms of classification accuracy. Crucially, we find that with our proposed training mechanism as an initialization, the performance of the existing state-of-the-art classification deep architectures can be preferably improved.
Development Status and Trend of Industrial Robot in China

With the continuous development of the times, technology is flourishing and the world is entering the era of artificial intelligence, industrial robots will replace humans as the backbone of the manufacturing industry. The continuous development and application of industrial robotics is conducive to building a new advantage for China’s manufacturing, promoting the transformation and upgrading of China’s industry, accelerating the construction of China’s manufacturing power, and making an important contribution to China’s economic development. Based on this, the article describes the current situation of industrial robots in China, the challenges faced, and the future development trend.
Analysis of Adi Soemarmo Solo Airport Parking Payment System

The writing of the Semester Final Project with the title Analysis of the Parking Payment System for Adi Soemarmo Airport Solo was compiled based on the results of observations at the exit gate of Adi Soemarmo Airport Solo. Transactions on parking payments often cause problems due to several factors, especially the time or process is quite long because sometimes the money given is too large then the cashier takes too long to give change, besides that sometimes passengers do not prepare the money they want to pay in advance and often passengers pay in a situation of insufficient money and this causes queues or jams at the gate exit. The research objective given by the author later is to provide the best solution for airport parking payment systems. In addition to making it easier for passengers, this will greatly facilitate cashiers when carrying out work operations. This payment application system is called u-nik or electronic money. Where u-nik functions to transfer the money balance data contained in u-nik to a computer using a system called AINO. So that payments occur without spending additional cash. With the existence of non-cash payment transactions using the AINO system, it is hoped that it can facilitate and provide speed in making parking payment transactions without the need to carry cash.
A Platform for Health Record Management of the Conscripts in the Hellenic Navy

eMass project aims to digitalize the medical examination procedure of recruitment phase of conscripts in the Hellenic Navy. eMass integrates recruits’ Electronic Health Record (EHR), while allows a pre-screening test, through portable telemedicine equipment. The data will be exploited to assess the individual’s cardiovascular risk through appropriate digital tools and algorithms. The eMass digital platform, will be accessible to health experts involved in the recruitment procedure for further assessment and processing. Recruits’ personal data is stored in the database encrypted using Advanced Encryption Standard (AES). eMass solution contributes to beneficial management and medical data analysis, preventing inessential physical or medical examinations minimizing danger of possible errors and reducing time-consuming processes. Moreover, eMass exploits Electronic Health Record data through a machine-learning based cardiovascular risk assessment tool.
Manipulator Oriented Grasp Control Based on Image Recognition

The ocean is rich in resources and plays a vital role in human development and social progress. The Marine environment is dangerous and complex, and it is very difficult to carry out Marine exploitation and survey by manual work. The fishing of Marine products in shallow sea culture mainly adopts artificial underwater fishing, which is very dangerous and has low fishing efficiency, which greatly limits the development of Marine economy. As an efficient and convenient underwater working equipment, underwater robot can be equipped with a mechanical arm to catch sea cucumbers, effectively reducing the high cost and danger brought by artificial fishing. Under this background, this paper studies the automatic grasping control system of manipulator. The application of underwater robot to carry a robotic arm to capture sea cucumbers effectively reduces the high cost and danger of artificial fishing.
Clinical Relevance of Pharmacist Intervention: Development of a Named Entity Recognition Model on Unstructured Comments

We developed a clinical named entity recognition model to predict clinical relevance of pharmacist interventions (PIs) by identifying and labelling expressions from unstructured comments of PIs. Three labels, drug, kidney and dosage, had a great inter-annotator agreement (&gt;60%) and could be used as reference labelization. These labels also showed a high precision (&gt;70%) and a variable recall (50–90 %).
Analisis Manajemen Pembelajaran Online Berbasis Media Sosial WhatsApp untuk Meningkatkan Motivasi dan Hasil Belajar Siswa Materi Sistem Pencernaan

This research is a quantitative descriptive study. The research sample were 58 students at SMPN-1 Mentaya Hilir Utara, Kotawaringin Timur Regency. The learning management instrument used an assessment questionnaire for planning, organizing, implementing, and evaluating learning which was assessed by 4 observers. learning outcomes were analyzed using classical assumption tests consisting of normality tests and homogeneity tests, as well as hypothesis testing using the Paired sample T test and Independent sample T test. The results of the study found that the management of online learning based on whatsApp social media was carried out "good" and the learning motivation of students was in the "medium" category. Online learning based on whatsApp social media has a significant effect on improving the learning outcomes of students' digestive system material, so it can be recommended as an alternative learning model during a pandemic.
Applying Grammar-Based Compression to RDF

Data compression for RDF knowledge graphs is used in an increasing number of settings. In parallel to this, several grammar-based graph compression algorithms have been developed to reduce the size of graphs. We port gRePair—a state-of-the-art grammar-based graph compression algorithm—to RDF (named RDFRePair). We compare this promising technique with respect to the compression ratio to the state-of-the-art approaches for RDF compression dubbed HDT, HDT++ and OFR as well as a \(k^2\)-trees-based RDF compression. We run an extensive evaluation on 40 datasets. Our results suggest that RDFRePair achieves significantly better compression ratios and runtimes than gRePair. However, it is outperformed by \(k^2\) trees, which achieve the overall best compression ratio on real-world datasets. This better performance comes at the cost of time, as \(k^2\) trees are clearly outperformed by OFR w.r.t. compression and decompression time. A pairwise Wilcoxon Signed Rank Test suggests that while OFR is significantly more time-efficient than HDT and \(k^2\) trees, there is no significant difference between the compression ratios achieved by \(k^2\) trees and OFR. In addition, we point out future directions for research. All code and datasets are available at https://github.com/dice-group/GraphCompression and https://hobbitdata.informatik.uni-leipzig.de/rdfrepair/evaluation_datasets/, respectively.
COVID-19 AND THE DIGITAL TRANSFORMATION OF EDUCATION: THE CASE OF LATVIA AND LITHUANIA

Educational ecosystem is facing rapid changes due emerging technologies and their rapid penetration to daily use. When the COVID-19 pandemic emerged, it only accelerated many of these trends. Nevertheless, some education systems have been able to adapt to the changing situation and digital transformation more easily than others. Digital competence is essential for learning, work and active participation in society in digital transformation context. Given the pressure of change on existing learning institutions and learning models, ICT offers broad opportunities for developing a different view. In order for digital education actors to adapt to the digital transformation in the education sector, they also need to have the skills needed to use technology effectively. However, there is a lack of computer and technological literacy. In Latvia and Lithuania, about one in three workers has limited or no digital skills, and most STEM vacancies remain unfilled because workers do not have the necessary competencies and are not inclined to study or retrain. The aim of the study is to assess the effect of dynamic capabilities for added value educational outcomes during COVID-19 recession. The results of the study revealed that dynamic capabilities have a direct positive eﬀect on value based education outcomes.
Photorealism in Mixed Reality: A Systematic Literature Review

In Augmented Reality systems, virtual objects are combined with real objects, both three dimensional, interactively and at run-time. In an ideal scenario, the user has the feeling that real and virtual objects coexist in the same space and is unable to differentiate the types of objects from each other. To achieve this goal, research on rendering techniques have been conducted in recent years. In this paper, we present a Systematic Literature Review aiming to identify the main characteristics concerning photorealism in Mixed and Augmented Reality systems to find research opportunities that can be further exploited or optimized. The objective is to verify if exists a definition of photorealism in Mixed and Augmented Reality. We present a theoreticalfundamental over the most used methods concerning realism in Computer Graphics. Also, we want to identify the most used methods and tools to enable photorealism in Mixed and Augmented Reality systems.
Research on GRU and CNN Combined Recommendation Model with Self-Attention Mechanism

In recent years, recommendation models have been widely used in various fields, but the existing recommendation models mostly focus on user historical behavior learning or the analysis of the internal relationship of items, and it is difficult to take into account the effective extraction of deep-level user features and item features. In view of this, this paper studies and proposes a GRU and CNN combined recommendation model with Self-Attention mechanism. The model uses GRU and Self-Attention mechanisms to extract user features, combines with CNN to capture the local relevant features of the item. Then through the full connection calculation of each feature, the prediction rating is obtained and the recommendation is generated. This paper uses MovieLens 1M data set and Amazon Digital Music data set for experiments. The results show that compared with other deep learning-based recommendation models and traditional recommendation models, the model proposed in this paper has achieved better results in both MSE and MAE indicators.