Reviewerly/exHarmony huggingface dataset:
Latent Convergent Cross Mapping

Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems. However, accurately inferring causal structures within a phenomenon based on observational data only is still an open problem. Indeed, this type of data usually consists in short time series with missing or noisy values for which causal inference is increasingly difficult. In this work, we propose a method to uncover causal relations in chaotic dynamical systems from short, noisy and sporadic time series (that is, incomplete observations at infrequent and irregular intervals) where the classical convergent cross mapping (CCM) fails. Our method works by learning a Neural ODE latent process modeling the state-space dynamics of the time series and by checking the existence of a continuous map between the resulting processes. We provide theoretical analysis and show empirically that Latent-CCM can reliably uncover the true causal pattern, unlike traditional methods.
Usability Assessment for Classroom-Based Mobile Application to Enhance Communication

This paper presents a research about usability testing for a mobile application named CoMAC: A Communication Mobile Application for Classroom. CoMAC is an application that developed for the usage of pre-school to handle the administrative task and monitor child’s assessment. This research paper focus on the usability testing part where the evaluation method is based on the quantitative methodology conducted on people who has a teaching and parenting background. This testing is important because it could help the researcher to identify the level of performance of the application and level of satisfaction of the user. The results of the testing show that the application evaluated is suitable for pre-school and have some room that can be improved.
THE RESEARCH WORK OF FUTURE COMPUTER SCIENCE TEACHERS IN UKRAINE

Due to the conducted scientific literature analysis in the article, the main stages (preparatory-target, procedural-substantive, experimental-effective) of the organization of scientific-activity of future teachers of computer science in Ukraine are covered. The need to use information and communication technologies (ICT) in the organization of research work of future teachers of computer science is emphasized. The article defines the concept of “research work of higher education seekers”. The analysis of the available information and communication technologies allowed to single out the following types of means for the organization of research work of future teachers of computer science: means of search, storage and placement of scientific materials; ICT tools for collaboration; ICT tools for verification of materials of performed scientific works; means of checking software code for plagiarism; ICT tools to support experimental research; ICT tools to create demonstration material. It is noted that the main characteristics of ICT for the organization of research work: individualization of scientific work; search engine optimization; convenient means for cooperation, etc. The article identifies areas for further research on this topic.
МЕТОДИ ВІДНОВЛЕННЯ ВІДСУТНІХ ДАНИХ У ІНТЕРФЕЙСІ ВЕЛИКИХ ДАНИХ

Known methods are investigated and a new method of missing data imputation is proposed.The biggest problem of the known methods of missing data imputation is only the processing of structured data, whensemi-structured and unstructured data dominate the real applications of the modern world. In addition, it is very important toanalyze the hidden dependencies in the data set, as well as to take into account the nature of the data set and to assume theabsence of data for each data source separately. Therefore, it is promising to use the big data paradigm for pre-processing andprocessing information from different sources, consisting of continuous numerical data and categorical data. However, the nature ofthe missing data in different data sources is also different. Thus, the main idea is to analyze data from different sources based onthe specifics and nature of these sources.The idea of the proposed method of calculating missing data is to process structured and semi-structured data based ona hierarchy of objects, as well as a set of functional dependencies and the development of association rules. This issue is veryimportant for big data interfaces, as most of the information is available in a semi-structured form. The proposed method createsadditional data values using domain and functional dependencies based on several calculation methods and adds these values tothe existing training data.
The Impact of R&D Expenditures on Firms’ Productivity in the Formal Private Manufacturing Sector in the Eastern Europe and Central Asia Economies and in the Middle East and North Africa Region

This paper examine s -  through the lens of theory  – the  impact of R&D spending on productivity in  s mall and  m edium  e nterprises SME’s  in the Middle East and North Africa & Eastern Europe and Central Asia nations, using the BEEPS 2013 database. The aim is to contribute to the existing empirical work and drawing the conclusions from the firm level ECA and MENA dataset about the impact of R&D on labour productivity particularly in the manufacturing sector in more than 4000 firms across 28 nations in  the  ECA  region  and 3275 firms throughout 9 count r ies in  the  MENA  region . To better allow for firm heterogeneity in the analysis, the study adopted  t wo types of matching analysis. T he propensity score matching (PSM) and  the  Mahalanobis distance matching  ( MDM ). The findings of this paper show  that the re is statistically significant impact of  R&D spending ( Treatment ) on firms performance proxied by output per worker as the ( Outcome ) variable.  The novelty of this research is inspired  by the argument of  (King and Nielsen, 2016) . This is where they suggested that  the propensity score  matching techniques ,  could approximate a low - standard experimental design , and could ignore much of the potentially useful information without efficient use, leaving us with higher imbalance, model dependence, and ultimately bias. Thus,  these recent development s  in the matching methods suggested that the conclusions drawn from PSM analysis are best supported  by a second estimator, such as MDM, which has the property of double robustness, and  reduce s  imbalance, model dependence, and bias. To  a great extent, the above ( King and Nielsen, 2016) argument  is proved  to be  true. This is where the impact of R&D expenditures on firms’ productivity was found to be statistically significant  – as mentioned above –  in the outcomes resulted from using both the PSM and MDM, but the difference is that the  imbalance in the confounding covariates, appeared to be extremely lower, and the bias reduction reached 100% in some covariates in the MDM outcomes compared to the PSM results.
Does the Introduction of Stock Exchange Markets BoostEconomic Growth in African Countries

We assess whether the introduction of private equity capital markets effects economic growth in African countries. We address this issue by focussing on stock exchange markets as the predominant type of new equity markets,using a Diff-in-Diffregressionmethod.The analysis uses a panel data set from48 Sub-Saharan countries over the time range of 1970-2018.23 countries are part of the “treated” group–which introduced international stock exchanges–and 25 “untreated” countries serve as the control group. Our results show that when compared with the time period priortothe introduction of stock exchange markets, GDP per capita rises by the amount of 532US$ (around 40% of the Sub-Saharan average) after theintroduction of equity capital markets inthe treated countries.Overthe tenyears post introduction, the effect is hump-shaped, with effects becoming statistically significant from the first year after implementation, with a peak in Year 5, and it then becomes statistically insignificant from then onwards.
ANALISIS PENERAPAN SISTEM INFORMASI AKUNTANSI PERSEDIAAN BARANG DI TOKO MP ONE STATIONARY 16C METRO BARAT

Wahyu, Nur, Laila 2020. “Application of Inventory Accounting Information System Goods in the MP OneStationary 16C Metro Barat (Case Study of 2017 FEB UM Metro Accounting Diploma Students) ". Faculty ofEconomics and Business, University of Muhammadiyah Metro. Advisors (1) Suyanto, S.E., M.Si.Akt.CA.,ACPA., CRA. Advisors (2) Angga Kurniawan, S.E., M.Si. In business activities in this globalization era, the development of information technology isindispensable to expand business networks and to facilitate internal corporate decision-making. The level ofcompetition which is quite tight makes business actors more use of accounting information systems to managetheir business activities properly. This type of research is qualitative, namely research procedures that produce descriptive data in theform of words from the phenomena found in the field observed by the researcher. This study aims to determinethe application of accounting information systems in the MP One Stationary 16C Metro Barat. The results obtained from this study are the accounting information system implemented in the MP OneStationary Store along with the components that support the application of the Inventory Accounting Information System in the MP One Stationary Store.
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning

Most existing Grammatical Error Correction (GEC) methods based on sequence-to-sequence mainly focus on how to generate more pseudo data to obtain better performance. Few work addresses few-shot GEC domain adaptation. In this paper, we treat different GEC domains as different GEC tasks and propose to extend meta-learning to few-shot GEC domain adaptation without using any pseudo data. We exploit a set of data-rich source domains to learn the initialization of model parameters that facilitates fast adaptation on new resource-poor target domains. We adapt GEC model to the first language (L1) of the second language learner. To evaluate the proposed method, we use nine L1s as source domains and five L1s as target domains. Experiment results on the L1 GEC domain adaptation dataset demonstrate that the proposed approach outperforms the multi-task transfer learning baseline by 0.50 $F_{0.5}$ score on average and enables us to effectively adapt to a new L1 domain with only 200 parallel sentences.
Committee on Cataloging: Description &amp; Access Summer Meeting 2020

Summary of the committee meeting of the CC:DA held at the ALA annual convention.
Symmetries, Graph Properties, and Quantum Speedups

Aaronson and Ambainis (2009) and Chailloux (2018) showed that fully symmetric (partial) functions do not admit exponential quantum query speedups. This raises a natural question: how symmetric must a function be before it cannot exhibit a large quantum speedup? In this work, we prove that hypergraph symmetries in the adjacency matrix model allow at most a polynomial separation between randomized and quantum query complexities. We also show that, remarkably, permutation groups constructed out of these symmetries are essentially the only permutation groups that prevent super-polynomial quantum speedups. We prove this by fully characterizing the primitive permutation groups that allow super-polynomial quantum speedups. In contrast, in the adjacency list model for bounded-degree graphs (where graph symmetry is manifested differently), we exhibit a property testing problem that shows an exponential quantum speedup. These results resolve open questions posed by Ambainis, Childs, and Liu (2010) and Montanaro and de Wolf (2013).
Secure Codecity with Evolution: Visualizing Security Vulnerability Evolution of Software Systems

The analysis of large-scale software and finding security vulnerabilities while its evolving is difficult without using supplementary tools, because of the size and complexity of today's systems. However just by looking at a report, doesn't transmit the overall picture of the system in terms of security vulnerabilities and its evolution throughout the project lifecycle. Software visualization is a program comprehension technique used in the context of the present and explores large amounts of information precisely. For the analysis of security vulnerabilities of complex software systems, Secure Codecity with Evolution is an interactive 3D visualization tool that can be utilized. Its studies techniques and methods are used for graphically illustrating security aspects and the evolution of software. The Main goal of the proposed Framework defined as uplift, simplify, and clarify the mental representation that a software engineer has of a software system and its evolution in terms of its security. Static code was visualised based on a city metaphor, which represents classes as buildings and packages as districts of a city. Identified Vulnerabilities were represented in a different color according to the severity. To visualize a number of different aspects, A large variety of options were given. Users can evaluate the evolution of the security vulnerabilities of a system on several versions using Matrices provided which will help users go get an overall understanding about security vulnerabilities varies with different versions of software. This framework was implemented using SonarQube for software vulnerability detection and ThreeJs for implementing the City Metaphor. The evaluation results evidently show that our framework surpasses the existing tools in terms of accuracy, efficiency and usability.
Development of social intelligence of future speech pathologist

The problem of development of social intelligence of the future speech pathologist is in the sphere of the person professional growth with an emphasis on development of the parties of social intelligence. The article is devoted to the analysis of psychological determinants of development of the intellect for the students who will be speech pathologists. Influence of the socially-psychological factors on a social intellect is examined here. At different age stages of the individual the knowledge of the specifics of social intelligence from preschool to adulthood, makes it possible to predict the understanding of social reality. Theoretical generalization of the data obtained in our research provide an opportunity to determine the basic psychological qualities that is necessary for the successful performance of the functions of a speech pathologists. The well-known periodization of R. Selman shows that social intelligence develops throughout all person’s life. Learning the skills to solve complex social problems can be an essential area for improving the developing personality and adult personality. If this is not taught purposefully, the vast majority of society will not be able to realize its potential in situations of social interaction in full. Features of preparation of the speech pathologists for professional activity are caused by philosophy of a modern society development. The culture of building high human relations, teaching students this - is an indisputable value of modern society. Revealing the specifics of social intelligence at each stage of the ontogenetic development of the personality of each student who will be defectologist determines for himself his potential and resources. The principle of parity in the process of professional development of future special educators requires subjective interaction in tandem “teacher-student”, where the teacher not so much teaches, but helps the student to learn independently, as a result of which the student receives not only knowledge but also skills. The leading principles of professional development of the future pathologist should also include the principle of dialogue.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks. The initial impression driven by our experimental results suggests that data heterogeneity is the dominant factor in the effectiveness of attacks and it may be a redemption for defending against backdooring as it makes the attack less efficient, more challenging to design effective attack strategies, and the attack result also becomes less predictable. However, with further investigations, we found data heterogeneity is more of a curse than a redemption as the attack effectiveness can be significantly boosted by simply adjusting the client-side backdooring timing. More importantly,data heterogeneity may result in overfitting at the local training of benign clients, which can be utilized by attackers to disguise themselves and fool skewed-feature based defenses. In addition, effective attack strategies can be made by adjusting attack data distribution. Finally, we discuss the potential directions of defending the curses brought by data heterogeneity. The results and lessons learned from our extensive experiments and analysis offer new insights for designing robust federated learning methods and systems
Effectiveness of using computer technologies for distance work of tutor-psychologist

Researched is the problem of the use of computer technologies in the activities of a teacher-psychologist in remote interaction with students with the aim of formation of effective learning process. The relevance of the issue lies in the complexity of the current social, economic and geopolitical situation, which ambiguously affects the educational process and the work of teachers in the current conditions. By creating specialized constructs using computer technologies, a teacher-psychologist can work much more widely and with the greatest coverage with those subjects of the educational process who are interested in this or for whom psychological support is an important element of normal life, stabilizing their psychological state and comfortable behavior in society. The research is aimed at studying the experience of teachers-psychologists in regional universities who present specialized computer programs for remote testing of students and teachers (students and faculty), as well as evaluating these constructs and developing recommendations for diversifying solutions in adapting such tests to real University practice in order to increase their effectiveness. In conclusion, we developed a construct for evaluating the effectiveness of remote work of a teacher-psychologist in higher education when analyzing psychological problems of university audience.
Extending near fault earthquakes catalogs using convolutional neural network and single-station waveforms

&amp;lt;p&amp;gt;The machine learning (ML) algorithms have already found their application in standard seismological procedures, such as earthquake detection and localization, phase picking, earthquake early warning system, etc. They are progressively becoming superior methods since one can rapidly scan voluminous data and detect earthquakes, even if buried in highly noisy time series.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We here make use of ML algorithms to obtain more complete near fault seismic catalogs and thus better understand the long-term (decades) evolution of seismicity before large earthquakes occurrence. We focus on data recorded before the devastating L&amp;amp;#8217;Aquila earthquake (6 April 2009 01:32 UTC, Mw6.3) right beneath the city of L&amp;amp;#8217;Aquila in the Abruzzo region (Central Italy). Before this event sparse stations were available, reducing the magnitude completeness of standard catalogs.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We adapted existing convolutional neural networks (CNN) for earthquake detection, localisation and characterization using a single-station waveforms. The CNN is applied to 29 years of data (1990 to 2019) recorded at the AQU station, located near the city of L&amp;amp;#8217;Aquila (Italy). The pre-existing catalog maintained by Istituto nazionale di geofisica e vulcanologia is used to define labels and train and test the CNN. We are here interested in classifying the continuous three-component waveforms into four categories, noise/earthquakes, distance (location), magnitude and depth, where each category consists of several nodes. Existing seismic catalogs are used to label earthquakes, while the noise events are randomly selected between the catalog events, evenly represented by daytime and night-time periods.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We prefer CNN over other methods, since we can use seismograms directly with very minor pre-processing (e.g. filtering) and we do not need any prior knowledge of the region.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/p&amp;gt;
PyART: Python API Recommendation in Real-Time

API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are project-specific. In this paper, we propose a novel approach, PyART, to recommend APIs for Python programs in real-time. It features a light-weight analysis to derives so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-the-art tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement.