Reviewerly/exHarmony huggingface dataset:
Computational validity of neuroimaging software: the case of population receptive fields

Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e. public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but cannot guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the same input data. We describe a framework for validating and sharing software implementations. We apply the framework to an application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework and methods can be extended to other critical neuroimaging algorithms. In assessing validity across four implementations, we found and reported five coding errors. Most importantly, our results showed imperfect parameter recovery, with variation in ground truth values of one parameter influencing recovery of other parameters. This effect was present in all implementations. The computational validity framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. Having validation frameworks help (a) developers to build new software, (b) research scientists to verify the software’s accuracy, and (c) reviewers to evaluate the methods used in publications and grants.
Affinity-Preserving Integer Projected Fixed Point Under Spectral Technique for Graph Matching

Establishing the pairwise geometric correspondence relationship between two feature point sets from the reference and the query graph is one of the vital technique in image processing for identifying and matching the target. In this paper, we introduce a combination variant of graph matching method using the candidate assignment affinity-preserving algorithm and integer projected fixed point algorithm improved by spectral technique to realize one to one correspondence. Extensive comparison experiments show that it is superior to some of the image matching algorithms in the case of deformation noise and outliers.
Designing a Copy Cataloguing Interface using Z39.50 for Libraries

Copy cataloguing and Z39.50 are well known concepts in modern library environment for executing library functions and activities. Data import is possible from different library OPACs using the Z39.50 server. Online Computer Library Centre (OCLC) and Library of Congress (LoC) are among the most comprehensive OPAC databases. This paper attempts to design a framework for comparison of Z39.50 search available in public domain and implementation of information retrieval protocol facilities in open source ILSs. The framework is mainly based on recommendations given by ILS-DI and IFLA Working Group on the area under consideration. It considers how to import the records from the other databases or OPAC through Z39.50 server or SRU/W towards next generation automation. Two open source ILSs (from the matured block of ILSs) namely NewGenLib and Koha are compared against a set of search parameters related with Z39.50 and drawn from global recommendations.
Predicting the functional organization of human visual cortex from anatomy using geometric deep learning

Human visual cortex is comprised of a number of areas, nearly all of which are organized retinotopically. This retinotopic mapping is similar across individuals; however, considerable inter-subject variation does exist, and this variation has been linked to cortical folding and other anatomical features. It was our aim to develop a neural network capable of learning the complex relationship between the functional organization of visual cortex and the underlying anatomy. Importantly, we used a geometric approach able to directly interact with data represented on cortical surface models, as many properties only make sense considering their location with respect to the various sulci and gyri. To build our network, we used the most comprehensive retinotopy dataset openly available – that from the Human Connectome Project. This dataset includes 7T fMRI data from 181 participants. The data serving as input to our network included curvature and myelin values as well as the connectivity among vertices forming the cortical surface and their spatial disposition. The output of the network was the retinotopic mapping value for each vertex of the surface model. Our final network included 12 spline-based convolution layers, interleaved by batch normalization and dropout. Our neural network accurately predicted the main features of both polar angle and eccentricity maps. More impressive yet, we show that the network was able to predict nuanced variations in the retinotopic maps across individuals. We further showed how disruption of the spatial organization of the input features increases the error and reduces the individual variability of the predicted maps. In conclusion, we were able to predict the detailed functional organization of visual cortex from anatomical properties alone using geometric deep learning. Although we demonstrate its utility for visual neuroscience, geometric deep learning is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.
Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm

Internal fraud in the financial sector are difficult to detect since fraudulent transactions are indistinguishable from ordinary transactions, and standard checkpoints, in the form of transaction documentation and authorization, are skillfully avoided.Well-designed software has available and machine-readable application logs that can be analyzed to detect anomalies in application usage.This paper presents a data preparation technique using path analysis and Kohonen SOM clustering algorithm that can help better profile users of an application to reduce the number of cases that will be further investigated.
Impact of the COVID-19 pandemic on a pediatric medical residency program in the city of Rio de Janeiro

INTRODUCTION: In March 2020, the COVID-19 pandemic - a disease caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) was decreed. Since then, there has been a need for adaptations in the pediatric medical residency program (MRP) at a federal hospital in the city of Rio de Janeiro. METHODS: Retrospective observational study whose objective was to describe the modifications in the pediatric MRP at a federal hospital resulting from the COVID-19 pandemic. RESULTS: Our pediatric MRP implemented adaptations such as: postponement of outpatient consultations; reduction in the number of residents allocated to the wards; temporary suspension of external rotations except for maternity rotation; training for proper dressing and undressing procedures; removal of residents with chronic diseases, pregnancy and those with suspected COVID-19; implementation of remote consultation and educational activities in a virtual environment. Data from March, April, May, and June 2020 were analyzed. During this period, 4 residents from pediatrics were removed due to chronic diseases or pregnancy. Of the 37 remaining residents of the PRM, 27 (73%) were removed due to issues related to COVID-19 (suspected COVID-19 or psychological stress). Of these, coronavirus infection was confirmed in 15 residents (40.5%). CONCLUSION: The pandemic caused the removal of many residents on suspicion of COVID-19 and alteration of the services routines. There was a significant reduction in outpatient consultations and suspension of face-to-face theoretical activities. In any case, the adoption of digital platforms for remote consultations and teaching activities allowed the maintenance of patient care and continued medical education.
The efficiency of the on-grid solar power plant in the Chechen Republic

Abstract The paper presents the analysis’ results of the generation and economic efficiency of a solar power station functioning in the private sector in conjunction with a centralized power grid. Based on data analysis of the generation of a test solar power plant with a capacity of 1.86 kW, a comparative analysis of the calculated and actual characteristics in real functioning conditions is carried out. The estimated power generation capacity of the station during 5 months (December 2019 - April 2020) was 680.1 kWh, and the actual generation for the same period was 525.86 kWh. Using data on the solar radiation’s level on the Chechen Republic territory, as well as data on electricity tariffs in the region, the economic effect of using a grid solar power plant is predicted. Taking into account the electricity tariff for private consumers, the estimated payback period of the system was 25 years. At a rate for commercial consumers - 12 years. Based on the analysis, it was concluded that the use of grid solar power plants is economically feasible only for electricity consumers in the Chechen Republic who purchase electricity at a commercial rate.
Geoecological zoning of modern geological objects composed of technogenic soils. Distribution of Zn, Cu, Pb, Fe, Hg and As in technogenic dispersed soils and bottom sediments

Abstract The article presents a new methodology for the internal differentiation of technogenic objects (ash and slag dumps) based on ecological and geological studies of soils, bottom sediments, surface and underground waters, based on the allocation of three zones with various permissible types of nature management in their territory. The first zone (I) is the territory directly connected with the technogenic object (the bottom of the dump); the allocation of borders of the first kind is justified by the data of the cadastral division of land and the results of route surveys. The second zone (II) is the territory of the active manifestation of the impact of the object, which has a visual manifestation (the area of wind separation, the area of accumulation of solid surface runoff, the zone of drying or inhibition of the natural stand and growth), at the initial stages of the process of natural assimilation of exposure and/or at the initial stages of regression, first of all, phytocenosis. The third zone (III) represents the territory of active natural assimilation (general dispersion and/or concentration on natural geochemical barriers) of ash and slag material products (primarily, scattering flows of heavy metals and other pollutants). An analysis of the results of this work made it possible to specify the types of environmental protection measures that are carried out depending on the identified geochemical zones, the use of which—within the framework of projects of improvement and environmental monitoring of the sanitary protection zone—will significantly reduce the costs of nature users. The rationalization of the economic and legal components is associated with a decrease in the areas for a specific type of work, and all the activities of the nature user in the controlled territory.
Construction of Fault Diagnosis Model of Metro Wheel Speed Box System Based on Convolution Neural Network

Abstract This research focuses on a fault detection method apply on the subway wheelsets. This fault diagnosis method is mainly aimed at the vibration signal to distinguish the fault and abnormal situation. At the same time, the vibration signal incorporated with the deep learning method to establish the diagnosis mechanism. Reliable online data of subway in a city of China and CNN (Convolutional Neural Network) are applied in the web training process. The degradation of vibration signal of rolling functional unit (wheelset) was summarized, and the difference between normal and fault signals of rolling functional unit (wheelset) was studied. The feedback learning mechanism makes it possible to update the neural network in real time.
Pengaruh Pengetahuan Etika Terhadap Persepsi Etis Mahasiswa Akuntansi Dengan Love Of Money Sebagai Variabel Intervening (Studi Kasus Mahasiswa Program Studi Akuntansi di Lima Perguruan Tinggi Daerah Istimewa Yogyakarta)

This study aims to examine the effect of ethical knowledge, internship experience, and economic status on the ethical perceptions of accounting students with love of money as an intervening variable. The data used are primary data. The method used is multiple analysis. The sampling technique used in this study was convenience sampling and 90 data samples were obtained. The results of this study indicate that the ethical knowledge variable has a positive effect on the ethical perceptions of accounting students, while ethical knowledge has a negative effect on love of money. love of money has a negative effect on the ethical perceptions of accounting students. Ethical knowledge negatively affects the ethical perceptions of accounting students through love of money as an intervening variable.&#x0D; Keywords : Ethical Knowledge, Ethical Perception, Accounting Student, Love of Money
Visualizer of Dataset Similarity Using Knowledge Graph

Many institutions choose to make their datasets available as Open Data. Open Data datasets are described by publisher-provided metadata and are registered in catalogs such as the European Data Portal. In spite of that, findability still remain a major issue. One of the main reasons is that metadata is captured in different contexts and with different background knowledge, so that keyword-based search provided by the catalogs is insufficient. A solution is to use an enriched querying that employs a dataset similarity model built on a shared context represented by a knowledge graph. However, the “black-box” dataset similarity may not fit well the user needs. If an explainable similarity model is used, then the issue can be tackled by providing users with a visualisation of the dataset similarity. This paper introduces a web-based tool for dataset similarity visualisation called ODIN (Open Dataset INspector). ODIN visualises knowledge graph-based dataset similarity, offering thus an explanation to the user. To understand the similarity, users can discover additional datasets that match their needs or reformulate the query to better reflect the knowledge graph. Last but not least, the user can analyze and/or design the similarity model itself.
Exploratory Data Analysis: Frequencies, Descriptive Statistics, Histograms, and Boxplots

To clearly present findings to a target audience, researchers must utilize exploratory data techniques and create appropriate graphs and figures. By understanding data, researchers can determine if outliers exist, data are missing, and statistical assumptions will be upheld. Additionally, it is essential to comprehend these data when describing them in conclusions of a paper, in a meeting with colleagues invested in the findings, or while reading others’ work.
Hands-on Cybersecurity Curriculum Using a Modular Training Kit

There is an exponential growth in the number of cyber-attack incidents resulting in significant financial loss and national security concerns.Secure cyberspace has been designated as one of the National Academy of Engineering (NAE) Grand Challenges in engineering.Broadly, the security threats are targeted on software programs, operating system and network with the intention to launch confidentiality, integrity and availability violations.Existing undergraduate and graduate-level cybersecurity education curriculum rely primarily on didactic teaching methods with little focus on student centered, inquiry-based teaching, known to improve student learning.With growing number of security incidents taking place, it is of utmost importance to prepare a workforce equipped with knowledge of the threat space and existing state-of-the-art solutions.Such comprehensive understanding is only possible by a dedicated hands-on course on cybersecurity where students can learn the key concepts by editing the hardware, software and OS, and, network policies.Unfortunately, such extensive and deep flexibilities are not provided in current cybersecurity curriculum.In this paper, we introduce a hands-on and modular self-learning Cybersecurity Training (CST) Kit to advance cybersecurity education.Students can promptly apply newly acquired knowledge on the CST Kit as part of the learning process.This Kit accompanies Do-It-Yourself (DIY) training modules that is used to model and investigate cybersecurity issues and their prevention to all levels of the cybersecurity workforce, including undergraduate and graduate students and K-12 science and technology teachers.The Kit also covers various aspects of cybersecurity issues including, hardware, software, operating system and network security.A coursework has been developed on hardware security for senior undergraduate and graduate students using the Kit.A preliminary survey conducted among students who were introduced to the modular board to implement hardware security threats such as, side-channel attack shows an 120% improvement in their understanding after the CST Kit based activities.The components of the CST Kit have also been used in a 4-day summer workshop for K-12 teachers.Teachers took preand post-concept inventories to assess their learning of content throughout the workshop and the results indicated improvement of 58%.These assessments focused on vulnerabilities and specific types of attacks, system security, data transmission and encryption, permutations and combinatorics, and binary numbers.
Knowledge Representation Framework for Software Requirement Specification

The need to extract correct information has become one of the main issues when analyzing the software requirement specification (SRS) documentation. The amount of gathered knowledge depends on the size of the information. However, the complexity of software systems is continuously increasing. As software systems change to more complicated systems, the information from the SRS documents may not be easily comprehended. For example, each annotation requirements tasks target the different types of information, and these tasks require the availability of experts specialized in the field. Large scale annotation tasks require multiple experts and very costly. If the number of experts is limited, annotation tasks may overwhelm the experts. The organization would not complete their objectives if they failed to manage their data because poor knowledge management affects many operations within the organization. To extract such vast information and turn it to useful knowledge, a company needs top quality software. This technology should able to input, store, and access systematically. This paper will discuss a framework based on the knowledge-based method, an attempt to improve knowledge representation. In this approach, WordNet 2.1 would be used as the knowledge source used to identify concepts represented by each word in a text from the SRS document.
Weak integration of form and motion in two-stream CNNs for action recognition

Human-level performance in action recognition has been achieved by the two-stream convolutional neural networks (CNN), which include a spatial CNN for analyzing appearance information in images, a temporal CNN for analyzing optical flow information in body movements, and a fusion module to integrate the two processes. We examine the contributions of the three modules to the recognition of actions in point-light and skeletal displays. In simulation 1, we trained the two-stream CNNs with raw videos and skeletal displays of human actions and tested whether the model can recognize actions in the point-light display. We found that the final recognition from the fusion module showed worse performance than did the temporal CNN alone, suggesting the model overweights appearance features extracted from the spatial CNN than motion features from the temporal CNN. Simulation 2 used walking actions to examine whether walking directions impact on facing direction discrimination. The temporal CNN showed better discrimination for in-place walking than for backward walking and moonwalk. In contrast, the final decision from the fusion module did not show this effect. Simulation 3 trained the two-stream CNNs to discriminate three types of walking actions, i.e., forward walking, backward walking, and moonwalk. The temporal CNN of motion processing achieved higher accuracy (.82) than the final recognition from the fusion module (.6). Further generalization test showed that the temporal CNN is sensitive to the causal direction of limb movements and body displacements, but the final decisions from the fusion module fail to show the sensitivity. We conclude that two-stream CNNs extract important form and motion features from action stimuli, however, the integration of the two processes in the fusion module is suboptimal to account for human performance in action recognition and understanding.
Two decades of blackbox optimization applications

Abstract This article reviews blackbox optimization applications of direct search optimization methods over the past twenty years. Emphasis is placed on the Mesh Adaptive Direct Search ( Mads ) derivative-free optimization algorithm. The main focus is on applications in three specific fields: energy, materials science, and computational engineering design. Nevertheless, other applications in science and engineering, including patents, are also considered. The breadth of applications demonstrates the versatility of Mads and highlights the evolution of its accompanying software NOMAD as a standard tool for blackbox optimization.
Decentralized Vehicle Registry on Blockchain

&lt;p&gt;The main aim of this project is to meliorate the experience of the customers by providing them with the complete authentic history of a vehicle – from the manufacturer till the time it reaches to scrap yard. In vehicle registration management system we store the details in a decentralized and distributed database with the help of which various departments can access vehicle details. Any renovation in the vehicle details will be reflected in every department. By using blockchain we can track the whole lifecycle of the vehicle like selling or buying because it provides transparent of the entire business. Blockchain provides us transparency and is very trustworthy in vehicle transactions, preventing all the disputes and also lowers the cost of service because of which many stakeholders, including car manufacturers, buyers, sellers, dealers, regulators and even garages ae benefited. By the help of blockchain we can track ownership, sale and accident history and helps in maintaining proper system for supply chains. It also ensures that the data remains consistent among various departments and also eliminate false data problem. The regional and local carmakers as a whole are also influenced.&lt;/p&gt;