Reviewerly/exHarmony huggingface dataset:
Classifying payment patterns with artificial neural networks: An autoencoder approach

Payments and market infrastructures are the backbone of modern financial systems and play a key role in the economy. One of their main goals is to manage systemic risk, especially in the case of systemically important payment systems (SIPS) serving interbank funds transfers. We develop an autoencoder for the Sistema de Pagos Interbancarios (SPI) of Ecuador, which is the largest SIPS, to detect potential anomalies stemming from payment patterns. Our work is similar to Triepels et al. (2018) and Sabetti and Heijmans (2020). We train four different autoencoder models using intraday data structured in three time-intervals for the SPI settlement activity to reconstruct its related payments network. We introduce bank run simulations to feature a baseline scenario and identify relevant autoencoder parametrizations for anomaly detection. The main contribution of our work is training an autoencoder to detect a wide range of anomalies in a payment system, ranging from the unusual behavior of individual banks to systemic changes in the overall structure of the payments network. We also found that these novel techniques are robust enough to support the monitoring of payments’ and market infrastructures’ functioning, but need to be accompanied by the expert judgement of payments overseers.
Democratizing Faculty Development - Establishing a Training Program at a New Computer Science University in Russia.

Development of faculty teaching capabilities is a key responsibility in the educational process of a university, which requires skilled educators in order to not only present material but present in a manner which is based on current best practices.In this evidence-based paper we examine the building of a training in keeping with current best practices for a minimal overhead cost, by engaging faculty.It has been found that the Instructional Skills Workshop (ISW) was able to improve faculty engagement in the learning process, address individual goals, inspire new educators, and lead to the development of a culture and value set of teaching and learning at Innopolis University.
MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel

Multiphoton Quantum Key Distribution (QKD) has recently been proposed to exchange the secret keys using the rotational of polarization over a multi-stage protocol. It has the ability to outperform the weaknesses of a single photon QKD by improving the generation of key rate and distance range. This paper investigates the theoretical aspects of multiphoton QKD protocol’s performance over free space optic (FSO) networks. The most common setup for quantum communication is the single-beam approach. However, the single-beam setup has limitations in terms of high geometrical loss. In this paper, the symmetry multiple-beam for quantum communication which is called as Multiphoton Quantum Communication-Multiple Beam (MQC-MB) is proposed to transmit the multiphoton from the sender to the receiver in order to minimize the impact of geometrical loss that is faced by the single-beam setup. The analysis was carried out through mathematical analysis by establishing the FSO quantum model with the effects of atmospheric and geometrical loss as well as considering atmospheric turbulence modeled by log-normal distribution. The design criteria of FSO, such as the transmitter, receiver, beam divergence, and diameter of apertures, are analytically investigated. The numerical results demonstrate that the MQC-MB outperforms the single-beam in terms of reducing channel loss by about 8 dB and works well under strong turbulence channel. Furthermore, the MQC-MB reduces the quantum bit error rate (QBER) and improves the secret key rate (SKR) as compared to the single-beam system even though the distance between the sender and receiver increases.
Quantifying Sensitivity and Performance Degradation of Virtual Machines Using Machine Learning

Virtualized data centers bring lot of benefits with respect to the reducing the high usage of physical hardware. But nowadays, as the usage of cloud infrastructures are rapidly increasing in all the fields to provide proper services on demand. In cloud data center, achieving efficient resource sharing between virtual machine and physical machines are very important. To achieve efficient resource sharing performance degradation of virtual machine and quantifying the sensitivity of virtual machine must be modeled, predicted correctly. In this work we use machine learning techniques like decision tree, K nearest neighbor and logistic regression to calculate the sensitivity of virtual machine. The dataset used for the experiment was collected using collected from open stack cloud environment. We execute two scenarios in this experiment to evaluate performance of the three mentioned classifiers based on precision, recall, sensitivity and specificity. We achieved good results using decision tree classifier with precision 88.8%, recall 80% and accuracy of 97.30%.
Visualization Analysis for Global Water Resources Based on Digital Earth

Qu, S.; Lv, J., and Liu, J., 2020. Visualization analysis for global water resources based on digital earth. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 47–50. Coconut Creek (Florida), ISSN 0749-0208.The rapid growth of population has placed continuous increasing pressure on water resources. It is of great significance for the sustainable development of water resources to make rational use of limited water resources and to give full play to the maximum use efficiency of water resources. To make rational use of water resources and to protect them, effective monitoring and management of water resources must be performed. To solve this problem, this paper proposes a new method of global water resources monitoring and management, that is, the visual analysis method of global water resources based on digital earth. Through the three-dimensional digital modeling of global water resources, the visual analysis of global water resources is realized, including the distribution of water resources, water area, water resources depth, pollution degree, and other visual analysis, which provides effective means and decision-making basis for water resources monitoring and management.
What BERT Sees: Cross-Modal Transfer for Visual Question Generation.

Pre-trained language models have recently contributed to significant advances in NLP tasks. Recently, multi-modal versions of BERT have been developed, using heavy pre-training relying on vast corpora of aligned textual and image data, primarily applied to classification tasks such as VQA. In this paper, we are interested in evaluating the visual capabilities of BERT out-of-the-box, by avoiding pre-training made on supplementary data. We choose to study Visual Question Generation, a task of great interest for grounded dialog, that enables to study the impact of each modality (as input can be visual and/or textual). Moreover, the generation aspect of the task requires an adaptation since BERT is primarily designed as an encoder. We introduce BERT-gen, a BERT-based architecture for text generation, able to leverage on either mono- or multi- modal representations. The results reported under different configurations indicate an innate capacity for BERT-gen to adapt to multi-modal data and text generation, even with few data available, avoiding expensive pre-training. The proposed model obtains substantial improvements over the state-of-the-art on two established VQG datasets.
MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources

Maintenance record logbooks are an emerging text type in NLP.An important part of them typically consist of free text with many domain specific technical terms, abbreviations, and nonstandard spelling and grammar.This poses difficulties for NLP pipelines trained on standard corpora.Analyzing and annotating such documents is of particular importance in the development of predictive maintenance systems, which aim to improve operational efficiency, reduce costs, prevent accidents, and save lives.In order to facilitate and encourage research in this area, we have developed MaintNet, a collaborative open-source library of technical and domain-specific language resources.MaintNet provides novel logbook data from the aviation, automotive, and facility maintenance domains along with tools to aid in their (pre-)processing and clustering.Furthermore, it provides a way to encourage discussion on and sharing of new datasets and tools for logbook data analysis.
Performance Evaluation XGBoost in Handling Missing Value on Classification of Hepatocellular Carcinoma Gene Expression Data

Missing values are a condition where there is no value in the observation, which results in loss of information. One of the steps in dealing with missing values is by deleting observations that have missing values. Still, for data that has small missing values, it can reduce important information from the data. This study analyzed the performance evaluation of the XGBoost method in dealing with missing values for classification problems in Hepatocellular Carcinoma gene expression data. This study used Hepatocellular Carcinoma gene expression data with 40 observations and 54675 features obtained from the National Center for Biotechnology Information website. The researchers randomly eliminated 5%, 10%, 15%, and 20% of the total data to compare the model's performance evaluation with the imputation method and without using Imputation. The imputation method used is the mean and k-nearest neighbor method. Measurement of model performance using cross-validation and confusion matrix evaluation procedures. In finding the best parameter, tuning hyperparameter using grid search. In general, the handling of missing values with the mean's Imputation is better in performance evaluation than the handling of missing values with the KNN imputation and without the imputation process for classifying Hepatocellular Carcinoma gene expression data. From the results of the above research, the value of missing 20% using the mean imputation method produces the highest evaluation performance value with 100% specificity, 100% sensitivity, 100% accuracy, 100% precision, and 100% MCC in training and testing data, and sensitivity 88%, 100% specificity, 100% precision, 94% accuracy, and 89% MCC. The XGBoost Machine learning model can handle missing values in a dataset without Imputation, but with the imputation method can improve performance evaluation on classification Hepatocellular Carcinoma Gene Expression Data.
Eye-tracking and learning experience: gaze trajectories to better understand the behavior of memorial visitors

Eye-tracking technology is increasingly introduced in museums to assess their role in learning and knowledge transfer. However, their use provide limited quantitative and/or qualitative measures such as viewing time and/or gaze trajectory on an isolated object or image (Region of Interest "ROI"). The aim of this work is to evaluate the potential of the mobile eye-tracking to quantify the students’ experience and behaviors through their visit of the "Genocide and mass violence" area of the Caen memorial. In this study, we collected eye-tracking data from 17 students during their visit to the memorial. In addition, all visitors filled out a questionnaire before the visit, and a focus group was conducted before and after the visit. The first results of this study allowed us to analyze the viewing time spent by each visitor in front of 19-selected ROIs, and some of their specific sub-parts. The other important result was the reconstruction of the gaze trajectory through these ROIs. Our global trajectory approach allowed to complete the information obtained from an isolated ROI, and to identify some behaviors such as avoidance. Clustering analysis revealed some typical trajectories performed by specific sub-groups. The eye-tracking results were consolidated by the participants' answers during the focus group.&#x0D;
NURSING CARE PROVIDED TO PEOPLE WITH COVID-19: CHALLENGES IN THE PERFORMANCE OF THE COFEN/CORENS SYSTEM

ABSTRACT Objective: to analyze the challenges of the Federal and Regional Nursing Councils regarding the performance of nursing in providing care to people with COVID-19. Method: a documentary study carried out on the websites of the Federal Nursing Council and respective Regional Councils of São Paulo, Rio de Janeiro, Ceará, Amazonas and Pernambuco. Data collection took place in May, encompassing bulletins published from March to April 2020. A total of 149 bulletin items were analyzed through Content Analysis. Results: most of the bulletins published by the Federal Nursing Council cited support for professionals, and health service inspection and task force creation stand out for the Regional Nursing Councils among the included units. The findings were organized into four categories: The nursing professional during the pandemic: the fight against the invisible enemy; Working conditions in providing care to people with COVID-19: barriers and challenges; Professional devaluation x technical responsibility: frontline scenario; Mental health of nursing professionals: living with fear and uncertainty. Conclusion: the challenges of the Federal Nursing Council and the Regional Nursing Councils regarding nursing performance in providing care to people with COVID-19 are directly linked to the supervision and support to the category in the daily exercise of the profession as evidenced by structural difficulties of the working conditions, professional devaluation due to their technical responsibility, inadequate dimensioning of the workforce, overload and problems related to mental health.
When an Image Tells a Story: The Role of Visual and Semantic Information for Generating Paragraph Descriptions

Generating multi-sentence image descriptions is a challenging task, which requires a good model to produce coherent and accurate paragraphs, describing salient objects in the image. We argue that multiple sources of information are beneficial when describing visual scenes with long sequences. These include (i) perceptual information and (ii) semantic (language) information about how to describe what is in the image. We also compare the effects of using two different pooling mechanisms on either a single modality or their combination. We demonstrate that the model which utilises both visual and language inputs can be used to generate accurate and diverse paragraphs when combined with a particular pooling mechanism. The results of our automatic and human evaluation show that learning to embed semantic information along with visual stimuli into the paragraph generation model is not trivial, raising a variety of proposals for future experiments.
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning

This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks.The challenge is to directly validate whether the system can recognize natural language statements that make sense from those that do not, and also require to generate reasonable explanation.Based on BERT architecture with multi-task setting, we propose an effective and interpretable "Explain, Reason and Predict" (ERP) system to solve the three sub-tasks about commonsense: (a) Validation, and (c) Explanation, (b) Reasoning, following the order of the competition.Inspired by cognitive studies of common sense, our system first generate a reason or understanding of the sentences and then choose which one statement makes sense, which is achieved by multi-task learning.The rational experiment validates our assumption and boost the performance.During the post-evaluation, our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in subtask B (rank 8), and BLEU score of 12.9 in subtask C (rank 9) 1 .
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values.

In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS). We examine how our combination of established ideas achieves competitive performance on semi-realistic and realistic time series data exhibiting common challenges in real-world Earth sciences data. In particular, we discuss a) a rationale for leveraging linear methods to identify causal links in non-linear systems, b) a simulation-backed explanation as to why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. For benchmark usage, we detail the algorithms here and provide implementations at this https URL . We propose the presented competition-proven methods for baseline benchmark comparisons to guide the development of novel algorithms for structure learning from time series.
Tohoku-AIP-NTT at WMT 2020 News Translation Task

In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT’20 news translation task. We participated in this task in two language pairs and four language directions: English German and English Japanese. Our system consists of techniques such as back-translation and fine-tuning, which are already widely adopted in translation tasks. We attempted to develop new methods for both synthetic data filtering and reranking. However, the methods turned out to be ineffective, and they provided us with no significant improvement over the baseline. We analyze these negative results to provide insights for future studies.
Asynchronous session subtyping as communicating automata refinement

Abstract We study the relationship between session types and behavioural contracts, representing Communicating Finite State Machines (CFSMs), under the assumption that processes communicate asynchronously. Session types represent a syntax-based approach for the description of communication protocols, while behavioural contracts, formally expressing CFSMs, follow an operational approach. We show the existence of a fully abstract interpretation of session types into a fragment of contracts that maps session subtyping into binary compliance-preserving CFSMs/behavioural contract refinement. In this way, on the one hand, we enrich the theory of session types with an operational characterization and, on the other hand, we use recent undecidability results for asynchronous session subtyping to obtain an original undecidability result for asynchronous CFSMs/behavioural contract refinement.
A Bibliometric Analysis of Dropbox on Scopus Publication

Having a considered that online storage and sharing has becoming an essential to organised, stay focused and get in sync contents for all team members to enlighten way to work. Dropbox is the world‟s first smart work space which bring content of all team members together whilst letting users use the tool they want. Dropbox was initiated in 2008. Based on the usefulness and benefits of Dropbox, there are many kinds of research has been conducted on this topic. Therefore, this paper aims to analyse the scientific literature and report various types of published documents related to the Dropbox based on the data obtained from the Scopus Database by using Perish software to combine the obtained data, VOS Viewer Software to visualize the obtained data and Microsoft Excel to analysis the obtained data analysis. As of 27thApril 2020, there are 506 documents were retrieved and analysed based on the „key words‟ search result thru database. By using standard bibliometric indicators, this paper reports the documents types, source types, publication years, language of publications, subject area, most active source title, keywords, distribution of publications by countries, authorship, text analysis, most active institutions and citation analysis. As the result show that 1) 81% of the articles were published in conferences proceedings and journals articles. 2) 91% of the articles were published in English. 3) There is an increased growth rate of literature on Dropbox since 1985. However, the growth rate is slightly lower from 2016 until 2018. 4) Computer Science is the most popular subject category with respect to the frequency of citations, Halevi, Harnik, Pinkas and Shulman-Peleg (2011)‟s article appears as the most cited paper with an average of 30.44 citations per year. 5) Keywords of the Digital Storage, Cloud Storages and Cloud Computing were the top three keywords used in the database which represented the main areas of about Dropbox. 6) An analysis by country, The United States (US) is first country published most articles about Dropbox with 138 (27.27%).Meanwhile, 6) a total of 446 (88.14%) articles were published as multi-authored with a mean index of 3.55 authors per paper. Therefore, this research reviews of Dropbox published articles and delivers details of growth of Drop box for these35 years. This may help in potential directions or reference for future research.
To the question of the notion of digital transformation of science

Based on the analysis of a vast massif of multi-lingual scientific literature in the broad context, the author examines development of the perception of the phenomenon of digital transformation of society as a whole and science in particular. Since digital transformation takes place &amp;ldquo;here and now&amp;rdquo;, objectively leading the process of scientific cognition in its essence and results, any current results of comprehension of this scale of multi-level process objectively carry private, intermediary character. However, clarification of the content of this concept and formation of analytical constructs that would unambiguously describe the specificity of the new social reality are necessary for the theory and management practice. It is noted that academic concept of digital transformation of science did not completely form and is dynamically changing in accord with the development of reality. Analysis of the scope of ideas pertaining to digital transformation and various accompanying concepts allows formulating perceptions on the essence of this phenomenon in the sphere of science. This work demonstrates the dual character of the impact of digitalization upon the scientific sphere and functionality of knowledge.
A Hybrid Threat: The Night Wolves Motorcycle Club

Abstract Hybrid threat actors are dynamic and often intentionally disguise their intentions. While the Russian motorcycle club Night Wolves dress comparably to outlaw motorcycle gangs, open source materials describe the club as posing a distinctly different national security threat. The Night Wolves’ ambiguity and covert activities make the club hard to analyze; however, this case study uses open source materials to provide insight into their activities and characteristics across an array of security domains. International incidents indicate members and associates have the capability and resources to engage in crime, corruption and politically-motivated violence both for personal interests, as well as the furtherance of Russian goals.