Reviewerly/exHarmony huggingface dataset:
Service Provisioning through High Level, Complexity Hiding Interfaces

Over the past decade, cyberinfrastructure community like XSEDE has substantially fostered and enriched knowledge discovery of scholars, researchers, and engineers from a variety of domains through enabling access to advanced computing systems, where continuing support for classic packages and parallel computing frameworks (e.g., MPI and OpenMP) has been well established. However, with the rise of "Big Data" era, an ever increasing demand from user community is the desire to run sophisticated, state-of-the-art distributed frameworks that handle various data related tasks. Examples include Hadoop and Spark for data processing and analytics, Cassandra and Redis for scalable on-disk and in-memory data stores, Apache Airflow for distributed workflow engine, just to name a few. Though by design such frameworks provision high-level, user friendly programming APIs for business logic composition, their deployment process oftentimes is both complex and complicated, requiring expertise well beyond what the majority of cyberinfrastructure users may have. To bridge the gap, in this paper we propose the concept of provisioning such frameworks through "cyberinfrastructure managed", system wide services, especially through leveraging "high level, complexity hiding interfaces" principle when designing interfaces that are exposed to users for framework setup and shutdown. In particular, we use Spark-as-a-Service at Indiana University as a concrete case study to illustrate how we applied the design principles. Furthermore, to demonstrate the generality of the design, we showcase Cassandra-as-a-Service, a work-in-progress prototype.
3D Object Classification, Visual Search from RGB-D Data

In this chapter, we consider the problem of creating a system for processing 3D models obtained using RGB-D sensors for the purpose of semiautomatic selection and classification of objects and their auto-completion based on visual search. We have proposed several heuristic preprocessing algorithms for selecting an object of interest on a scan that contains noise and extraneous objects. To implement the visual search algorithm, we obtained a modification of the ray casting 3D-shape feature extraction algorithm. To solve the classification problem, the possibility of using deep learning architectures based on convolution mechanisms on graphs is investigated. The information about the object class obtained during the classification stage is used for faster and more accurate auto-completion. The resulting system has been tested on real data.
Predicting escalations in customer support with gradient boosting at the IEEE BigData 2020 Cup

This paper is based on the 1st place solution to IEEE BigData 2020 Cup: Predicting Escalations in Customer Support. We have analysed the task of this competition, talked about various potential approaches to solving this task and our approach to solving it. In addition, we analysed those features that potentially have the greatest impact on the prediction of our model, making it possible to assess the impact of factors on escalation in this particular situation.
Introduction to Python's Syntax

This tutorial is the second in our series and covers basic syntax in Python with examples related to psychology.The aim is to teach programming beginners and experts alike the fundamentals required in order to smooth the learning curve and succeed with integrating Python with their research.It starts by covering basic built-in functions and variable creation.Next, different data types and data structures that you will encounter are covered detail, followed by comments and best commenting practices.Finally, indentation, logic, conditional statements, and loops are all explained with simple, illustrative examples.The tutorial ends with a comprehensive example of the same-different task from cognition that ties together everything learned.With this foundation, the reader will gain the confidence to begin practicing Python on their own and think of ways to incorporate it into their own research and daily lives.
Learning a No Reference Quality Assessment Metric for Encoded 4K-UHD Video

4K-UHD videos have become popular since they significantly improve user’s visual experience. As video coding, transmission and enhancement technology developing fast, existing quality assessing metrics are not suitable for 4K-UHD scenario because of the expanded resolution and lack of training data. In this paper, we present a no-reference video quality assessment model achieving high performance and suitability for 4K-UHD scenario by simulating full-reference metric VMAF. Our approach extract deep spatial features and optical flow based temporal features from cropped frame patches. Overall score for video clip is obtained from weighted average of patch results to fully reflect the content of high-resolution video frames. The model is trained on automatically generated HEVC encoded 4K-UHD dataset which is labeled by VMAF. The strategy of constructing dataset can be easily extended to other scenarios such as HD resolution and other distortion types by modifying dataset and adjusting network. With the absence of reference video, our proposed model achieves considerable accuracy on VMAF labels and high correlation with human rating, as well as relatively fast processing speed.
Reducing the Need for Manual Annotated Datasets in Aspect Sentiment Classification by Transfer Learning and Weak-Supervision

Users’ opinions can be greatly beneficial in developing and providing products and services and improving marketing techniques for customer recommendation and retention. For this reason, sentiment analysis algorithms that automatically extract sentiment information from customers’ reviews are receiving growing attention from the computer science community. Aspect-based sentiment analysis (ABSA) allows for a more detailed understanding of customer opinions because it enables extracting sentiment polarities along with the sentiment target from sentences. ABSA consists of two steps: Aspect Extraction (AE) that allows recognizing the target sentiment; Aspect Sentiment Classification (ASC) that enables to classify the sentiment polarity. Currently, most diffused sentiment analysis algorithms are based on deep learning. Such algorithms require large labeled datasets that are extremely expensive and time consuming to build. In this paper, we present two approaches based on transfer learning and weak supervision, respectively. Both have the goal of reducing the manual effort needed to build annotated datasets for the ASC problem. In the paper, we describe the two approaches and experimentally compare them.
Designing Blockchain enabled customer experiences for new digital services

This practice-based paper presents a case study of designing services utilising blockchain technology. Blockchain providesan extremely secure record of transactions and has the potential to simplify and automate many complex processes. Inthis example Blockchain is used to validate a person’s identity through a digital passport. In using Blockchain thecustomer journey becomes highly automated and has no points of user interaction, which user-research revealed hasboth benefits and disadvantages. The paper discusses the challenges and opportunities of highly automated processesand proposes a framework of design principles to be used when developing Blockchain and technology-based servicesthat produce invisible processes.
E-NOTICE ANDROID APPLICATION

E-notice app is a place for students to view notices and teachers to post them for easy and fast communication. The notice board has always been a place where students and teachers gather to get the latest release of notices. We aim to bring this board to a virtual location where staff/students will not only read notices but also respond to them immediately through their own desk. Students sometimes waste their time reading notices in which they are not interested. We come up with solutions to these problems. Students who are not interested in notices related to a particular cell will not be notied with notices related to that particular cell. This will also help to communicate notices/news not only in text form but in other formats also like image, video, etc.
Effects of Camera Lens Distortion on Particle Size and Shape Characterizations

Abstract The image-based methods have been widely used for particle size and shape characterizations. However, images captured by various cameras may contain a level of lens distortion. This study shows that even a small distortion that cannot be identified by visual observation could significantly alter image-based particle size and shape distortions. This study develops a self-rectification technique to rectify the distorted images. The key is to use a rectangular template as a reference system. An edge detection technique is used to identify the edges of the rectangular template, which becomes curves in the distorted images. Image processing techniques based on least square and Levernberg-Marquardt approaches are used to rectify the curved edges back to straight lines and determine distortion coefficients. The distortion coefficients are used to quantify the magnitude of image distortion and to rectify the image. This research demonstrates that it is unreliable to use visual observation to determine whether the image contains distortion. The rigorous analysis must be performed to ensure the accuracy of image-based particle size and shape characterizations.
An Expert System for Fuzzy Server Failure Data Warehouse Analysis

The instinctive use of natural terms when interrogating the data warehouse by decision-makers encourages to be integrated into the queries made by the decision-makers during the data warehouse analysis phase. In this paper, we present an overview of works that have studied the use of vague words in decisional queries. And we propose a rule-based system that uses fuzzy logic for querying the server failure data warehouse. Some perspectives emerge from our work, namely: the enrichment of the current system with new terms and new rules.
Efficient Algorithm to Compute Odd-Degree Isogenies Between Montgomery Curves for CSIDH

Isogeny-based cryptography, such as commutative supersingular isogeny Diffie-Hellman (CSIDH), have been shown to be promising candidates for post-quantum cryptography. However, their speeds have remained unremarkable. This study focuses on computing odd-degree isogeny between Montgomery curves, which is a dominant computation in CSIDH. Our proposed “2-ADD-Skip method” technique reduces the required number of points to be computed during isogeny computation. A novel algorithm for isogeny computation is also proposed to efficiently utilize the 2-ADD-Skip method. Our proposed algorithm with the optimized parameter reduces computational cost by approximately 12% compared with the algorithm proposed by Meyer and Reith. Further, individual experiments for each degree of isogeny ℓ show that the proposed algorithm is the fastest for 19≤ℓ≤373 among previous studies focusing on isogeny computation including the Õ(√ℓ) algorithm proposed by Bernstein et al. The experimental results also show that the proposed algorithm achieves the fastest on CSIDH-512. For CSIDH-1024, the proposed algorithm is faster than the algorithm by Meyer and Reith although it is slower than the algorithm by Bernstein et al.
José Honório Rodrigues: interlocutor brasileiro para a institucionalização da Arquivologia no Brasil a partir das contribuições francesas

This paper aims at presenting the contributions of Jose Honorio Rodrigues as one of the interlocutors of international relations between Brazil and France about Archival Science, from the analysis of his documents, custodied by three Brazilian institutions. This analysis takes up studies that consider the historian’s contributions, as an internationalist intellectual, to History, archives and Archival Science. The results point out to French contacts established by Rodrigues, whose unfoldings culminate in the arrival of a French archivist to Brazil. Boullier de Branche teaches courses, collaborates in the organization and description of fonds custodied by the Arquivo Nacional and recommends the offer of a regular course for the training of archivists in Brazil, the “CPA”, the first of 16 Brazilian graduation courses in Archival Science.
Informational Teaching Concept in the Reform of College English Education

English education is an important and unusual basic subject from early childhood to higher education. The concept of informatization teaching has a positive effect on improving English education and teaching reform. College English (CE) teaching must strengthen the construction of software and hardware information, strengthen the cultivation of English teachers' information literacy, and improve the English teaching information level. The trend of higher education informatization (EI) is more obvious. In addition to the semi-social nature of colleges and universities (CAU), it is also one of the manifestations of information modernization. The traditional English teaching model has a single form, and the content is more tedious than the information model. The purpose of this article is to study the practical role of informatized teaching concepts in CE reform. This article uses a novel and effective teaching guidance method to optimize the application process of informatization teaching. Experiments show that improving the level of informatization teaching has a positive effect on English teaching in CAU.
Report of a Cooperative High School and University Class using High-speed Network

In December 2019, The Ministry of Education, Culture, Sports, Science and Technology of Japan announced the GIGA (Global and Innovation Gateway for All) school concept. The aim is to optimize education for each and every child by providing a terminal and a high-speed, large-capacity communication network. To achieve this goal there have been various initiatives across Japan. A demonstration experiment of remote classes using a high-speed network was conducted between Kyoto Institute of Technology (KIT) and Kyoto Prefectural Toba High School. This paper reports the effects and problems of the demonstration experiment.
Learning Latent Variable Models with Discriminant Regularization

In many machine learning applications, data are often described by a large number of features or attributes. However, too many features can result in overfitting. This is often the case when the number of examples is smaller than the number of features. The problem can be mitigated by learning latent variable models where the data can be described by a fewer number of latent dimensions. There are many techniques for learning latent variable models in the literature. Most of these techniques can be grouped into two classes: techniques that are informative, represented by principal component analysis (PCA), and techniques that are discriminant, represented by linear discriminant analysis (LDA). Each class of the techniques has its advantages. In this work, we introduce a technique for learning latent variable models with discriminant regularization that combines the characteristics of both classes. Empirical evaluation using a variety of data sets is presented to verify the performance of the proposed technique.
Enhancing Steady-State Entanglement Generated by a Nondegenerate Three-Level Laser with Thermal Reservoir

We analyze a nondegenerate three-level cascade laser with an open cavity and coupled to a two-mode thermal reservoir employing the stochastic differential equations for atomic operators associated with the normal ordering. Applying the large-time approximation scheme, we obtain the solutions for the corresponding equations of evolution of the expectation values of atomic operators. Furthermore, employing the resulting solutions, we studied the photon as well as cavity atomic-state entanglement amplification of the cavity radiation.
SoK: Transparent Dishonesty: Front-Running Attacks on Blockchain

We consider front-running to be a course of action where an entity benefits from prior access to privileged market information about upcoming transactions and trades. Front-running has been an issue in financial instrument markets since the 1970s. With the advent of the blockchain technology, front-running has resurfaced in new forms we explore here, instigated by blockchain's decentralized and transparent nature.In this paper, we draw from a scattered body of knowledge and instances of front-running across the top 25 most active decentral applications (DApps) deployed on Ethereum blockchain. Additionally, we carry out a detailed analysis of Status.im initial coin offering (ICO) and show evidence of abnormal miner's behavior indicative of front-running token purchases. Finally, we map the proposed solutions to front-running into useful categories.
Supervised depth estimation for visual perception on low-end platforms

Depth estimation is a very useful task in robotics and mostly computervision applications as obstacle avoidance, navigation and localization in GPS denied environments. Stereovision aproaches are commonly used in the state-of-the-art and dominated with methods from the classic Euclidean geometryand computational photography. However, most of these methods are computationally expensive which limits the applicability on high end setups and platforms. In the light of the recent bloom in Deep Learning and especially CNNs, we present in this paper a lightweight CNN-based encoder-decoder model for the task of monocular depth estimation from single RGB images. The model is designed to be runnable on low-cost platforms. In particular, we explore the performance of the most recent version of the MobileNet family as an encoder part, and afterwards we gradually build up the depth map back using the optimized UpConv block for decoding.
Making media for learning musical instruments using the Scratch application

Abstract The challenge for music educators in distance learning is the inability to practice teaching musical instruments directly to students. This article aims to bridge music educators so that they can still teach musical instruments to students and the students can continue to learn music even though they are learning from home. The solution offered is learning music using the Scratch application on a laptop or desktop computer as the controller. There are five steps to make music learning media using the Scratch application: (1) downloading and installing the Scratch Desktop application, (2) making designs or icons for each music scale, (3) recording or inputting sound musical instruments, (4) setting the buttons for each musical scale, and (5) playing the musical compositions. The Scratch application was effective in making learning media for learning musical instruments, learning to recognize the sounds of musical instruments, and learning to memorize song melodies. In conclusion, the Scratch application can be a solution for creating computer-based learning media for musical instruments.
Light Field Image Depth Estimation Method Based on Super-Resolution Cooperative Reconstruction

In order to improve image resolution in depth estimation, this paper proposes a method of simultaneous super-resolution reconstruction and light field image depth estimation. In this paper, the SRCNN super-resolution algorithm was used for the super-resolution reconstruction of two intermediate images in the depth estimation process, to improve the resolution of the final output image. In the proposed method, the SRCNN algorithm is used in the depth estimation map f(x) of the input picture pixels firstly, and then the algorithm is used for the second time in the confidence reduction process of the occlusion boundary area, so that the super-resolution reconstruction and the depth of the image proceed simultaneously. Over above two steps processing, the observable improvement of the detail information of the enlarged image can be noted through the senses, and the objective evaluation index—PSNR is also significantly improved.
Semantic Shifts Reveal the Multipurpose Use of Potential COVID-19 Treatments

When the world is rushing to test potential COVID-19 treatments, the limited resources and the overwhelming information demand the fast understanding of the promising drugs. This paper examines the multipurpose use of potential COVID-19 treatments by mining scientific papers on COVID-19 and related historical coronavirus research. Semantic shifts of the treatment-related entities are recognized to present their various applications in practice. The results identify 10 multipurpose entities. For selected entities, a detailed interpretation is given via text mining analysis about their possible use and rationale in different situations.
Penetration testing of Surveillance Data Distribution System

The objective of this paper is to introduce a comprehensive status of cyber security challenges in Surveillance Data Distribution System (SDDS). The cyber security is one of the essential issues of current Air Traffic Systems. It has been mandated by European Union law as well as national law. Therefore, EUROCONTROL as the provider of several tools or services (SDDS, IMPACT, EAD, ARTAS etc.), is regularly conducting a penetration testing in order to identify security vulnerabilities of each system. For this purpose, a grey-box assessment was conducted by EUROCONTROL's security experts on the SDDS system in a development environment of EUROCONTROL's data centre. The numerous vulnerabilities were found, mainly due to older version of operation system used by the system. They are currently mitigated by the fact that SDDS systems allows the network connectivity only to trusted partner. The next version of SDDS application will be addressing the most critical issues.
Identification the Control Object by Convergence of the Results Spectral Analysis of the Emitted Acoustic Noise

The article deals with the actual problem of improving the quality to object identification of adaptive control systems. It is proposed to apply the decision classification rules synthesized on the basis of the convergence of the results analysis the spectral characteristics of the acoustic noise emitted by the object. In particular, methodologically different algorithms for studying the bispectrum of the emitted signal and correlation matrices between indicator variables of the Fourier spectrum in previously defined informative frequency ranges were used. As an illustration of the proposed method, the results of diagnostics for defective states of an internal combustion engine the car are given. A distinctive feature of the proposed approach is noted - the analysis in various clusters not of the spectral characteristics of the signal from the object, but of their specific functional connections. This allows you to observe the changes in the response of the internal autonomous control system occurring during the control process. It is shown that the use of convergent decision rules made it possible to improve the quality identification by 6–10%.