Reviewerly/exHarmony huggingface dataset:
Using Domain Knowledge for Feature Selection in Neural Network Solution of the Inverse Problem of Magnetotelluric Sounding

In the present study, using the inverse problem (IP) of magnetotelluric sounding (MTS) as an example, we consider the use of neural networks to solve high-dimensional coefficient inverse problems. To reduce the incorrectness, a complex approach is considered related to the use of narrow classes of geological models, with prior selection of the model class by solving the classification problem by MTS data. Within the framework of this approach, the actual direction of work is to reduce the volume of calculations when re-building the system for another set of geological models. This goal can be achieved by selecting the essential features. The present paper is devoted to the study of the applicability of various selection methods to the MTS IP. Also, in this paper we consider taking into account domain knowledge about the studied object in the process of selection of essential features using methods such as wrapper.
Leveraging Health Helplines to Combat COVID-19 Pandemic: A Descriptive Study of Helpline Utilization for COVID-19 in Eight States of India

During a health emergency such as the current COVID-19 (coronavirus disease 2019) pandemic, immediate access to validated information is of utmost importance. Unfortunately, unverified, unreliable, and speculative information flows at an unprecedented rate in today's world, leading to an infodemic. In such a scenario, Indian states have used telephone-based health helplines to provide an effective channel for the provision of validated, accurate, and up-to-date information. We analyzed the utilization of these helplines for providing COVID-19-related information and the profile of callers handled by these helplines across 8 states of India. These helplines received more than 6.4 million calls over a period of 6 months. The volume of calls mirrored key announcements and events taking place in India as a response to the pandemic, such as the nation-wide lockdown, extension of lockdown, and so on. Nearly three quarters of the callers belonged to the economically productive age group of 20 to 59 years. The majority of the callers were males across all 8 states. The proportion of female callers was lowest in Bihar and highest in the small, hill state of Sikkim.
A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement

Medical images may be corrupted by noise. This noise affects the image quality and can obscure important information required for accurate diagnosis. Effectively apply filtering techniques can facilitate diagnosis or reduce radiation exposure. In this paper, we introduce a parallel method designed to reduce mixed Gaussian-impulse noise from digital images. The method uses fuzzy logic and the fuzzy peer group concept. Implementations of the method on multi-core interface using the open multi-processing (OpenMP) and on graphics processing units (GPUs) using CUDA are presented. Efficiency is measured in terms of execution time and in terms of MAE, PSNR and SSIM over medical images from the mini-MIAS database and over computed radiography (CR) images generated at different exposure levels. These images have been contaminated with impulsive and/or Gaussian noise. Experiments show that the proposed method obtains good performance in terms of the above mentioned objective quality measures. After applying multi-core and GPUs optimization strategies, the observed time shows that the new filter allows to remove mixed Gaussian-impulse noise in real-time.
Blockchain in Construction Practice

The construction industry is one of the most important sectors of most economies. However, the sector has been plagued with many challenges, including low productivity, lack of collaboration, inadequate/insecure information sharing, and lack of trust between participants. To overcome some of these challenges, blockchain, one of the emerging technologies has been hailed as a solution for sharing and distributing information securely. While blockchain has been widely popularized in the financial fields through well-established cryptocurrencies such as Bitcoins and Ethereum, the same cannot be said of the construction industry. The application of blockchain in the construction sector is yet to be widely documented in academic literature. This study explores the application of blockchain technologies in the construction sector. Specifically, the operational principles, applications, associated benefits, and weaknesses of blockchain in construction practice are examined in this paper. The paper concludes that there is definitely a huge potential in the adoption of blockchain in different construction processes.
A Parallel Global TFIDF Feature Selection Using Hadoop for Big Data Text Classification

Feature selection (FS) is a fundamental process in machine learning. This process aims to remove irrelevant features, reduce the dimensionality of the feature space, and increase the accuracy of classification algorithms. For this, many feature selection techniques were proposed through several studies. However, as big data is still coming, the classical feature selection methods are enabled to store or process efficiently large datasets. Therefore, this work aims to improve the well-known term weighting method term frequency inverse document frequency (TFIDF) using MapReduce programming model on Hadoop framework. Our proposed approach, we called global TFIDF (GTFIDF), aims to introduce a simple and efficient parallel feature selection method which addresses the drawbacks of the classical TFIDF. The proposed approach includes two weighting schemes based on averaging and maximizing the GTFIDF. Experimental results on two well-known datasets prove that GTFIDF improves the performance of classification in term of the Micro-F1 and Macro-F1 measures.
Opportunistic Collective Experiences

Despite many available social technologies for connecting at a distance, we don't always find opportunities to actively engage in shared experiences and activities with friends and loved ones, even though this kind of interaction is associated with increased social closeness. To better support active engagement in shared experiences and activities while also making it convenient to find opportunities for interacting in this way, our work explores the design of Opportunistic Collective Experiences (OCEs), or social experiences powered by computer programs that identify opportune moments when users share situations across distance and structure shared activities in those situations. To support interacting with, programming, and executing OCEs, we developed Cerebro, a computational platform that consists of a mobile app that supports users? social interaction, an API for expressing the situations and activities that make up the interactional opportunity, and an opportunistic execution engine that checks for interactional opportunities and executes them when possible. Through a 20 day deployment study tested with groups of geographically-distributed college alumni (N=21), we found that OCEs promoted opportunities for active engagement; facilitated interactions that were socially connecting by structuring ways to engage in shared experiences and activities; and made actively engaging easier by identifying situations appropriate for interacting and structuring how to engage in activities in these situations. We contribute to CSCW (1) a novel interaction that facilitates engaging in shared experiences and activities at distance during coincidental moments; and (2) the design of systems to interact with, program, and execute these kinds of interactions.
Exploring the Power of Romanian BERT for Dialect Identification.

Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a task specifically created for challenging participants to solve the previously mentioned issue. More specifically, we introduce a series of neural systems based on Transformers, that combine a BERT model exclusively pre-trained on the Romanian language with techniques such as adversarial training or character-level embeddings. By using these approaches, we were able to obtain a 0.6475 macro F1 score on the test dataset, thus allowing us to be ranked 5th out of 8 participant teams.
Dog Breed Classification Based on Deep Learning

Deep learning is part of the field of artificial intelligence. It has powerful feature extraction and learning capabilities. Because of its various advantages, it has been applied in many fields. Object detection is an important technology in deep learning, and object detection based on deep learning has also been studied by many people. With the gradual improvement of people's living standards, pets have gradually received people's love, among which pet dogs occupy the majority. Different types of pet dogs will bring different problems. For example, large pets may be more aggressive and cause problems for city management. If dangerous pets can be distinguished in time, it can bring more security to people and avoid some people being bitten by pet dogs. In the deep learning algorithm, YOLOv3 has better object detection performance, but it only targets different species and objects, and the classification of different categories of specific species is not good enough. In daily life, the body of the pet dog is sometimes hidden by the complicated background, which makes it difficult to extract the overall characteristics of the pet dog. At this time, the facial features of the pet dog can be fully utilized to distinguish the pet dog. In order to solve this problem, this paper proposes an improved yolov3 model for face detection and categorization of pet dogs. This paper establishes a data set of 8 different kinds of pet dogs. The data set is divided into training set and a test set, and the training set is sent to the established model for training. Finally, we use the test set to verify the effect of the model. This paper establishes a data set of 8 different kinds of pet dogs. Pet dog types include Akita, Golden Retriever, Poodle, Pomeranian, Samoyed, Corg, Pug, and Husky. Experiments show that this paper can realize the detection and classification of pet dogs with high detection speed and accuracy, and mAP(mean Average Precision) can reach 94.91%.
Effective Design and Implementation of B.Tech (CSE) Curriculum with Industry Tie-Ups

The advent of globalization urges the Indian industries in diverse domains of engineering to adopt highly skilled manpower.The pattern of recruitment by the industries has been changing from recruitment-training model to recruitment-work model to meet the global needs and standards.Hence, to be aligned with the recruitmentwork model, it is an urgent requirement for the higher engineering educational institutions to adopt instituteindustry collaboration.The paper emphasizes the need of Institute-Industry( 2 )collaboration, and illustrates the collaboration of Kalasalingam Academy of Research and Education with IBM-ICE in designing the curriculum and its implementation.The curriculum considers tracks such as Data Analytics, AI&ML, Cyber Security & Cyber Forensics, IoT etc.For effective teaching and learning, the collaboration facilitated the training of the faculty members by IBM personnel, adoption of innovative assessment schemes such as project based evaluation, non-standard quizzes, open book test, peer evaluation and industry personnel evaluation.The series of industrial webinars and guest lectures provide continuous support on updating the getting jobs.At the outset, it is believed that the Institute-Industry collaborations will help in making the graduates readily employable and aid in societal development.
Libraries of the world during the pandemic: a new experience and the first conclusions

The main theme of 2020 for libraries around the world is organizing the work under the constraints associated with COVID-19, which was confirmed by the results of information searches for articles in the world’s largest databases (Google Scholar, Web of Science, Scopus, etc.), which discuss actual problems of libraries’ activity during this period. Their solution is achieved by developing common approaches to challenges at the global level, sharing best practices and methods of working in a pandemic. The purpose of the article is to present the key reports presented in the cycle of online meetings entitled “Life of the world’s libraries during a pandemic”, organized by the State Public Scientific and Technological Library of the Siberian Branch of Russian Academy of Science (SPSTL SB RAS). The article presents the experience of foreign and Russian libraries: Bibliothèque universitaire des langues et civilisations (France), the Libraries of New York University and Harvard Universities (USA), University of Tartu Library (Estonia), the National Library of the Republic of Turkey, the Scientific Library of the Belarusian National Technical University, Research Library of Tomsk State University and SPSTL SB RAS. The authors showed the activities of libraries to organize the work of employees and service users. As a result, it was determined that libraries choose different ways and methods of working with users, develop innovative services, expand the repertoire of information resources / products, take measures to ensure the safe work of employees, including remotely. It was emphasized that not all types of work could be transferred to a remote mode, and that required quick decisions on the redistribution of functions among employees and the мlaunch of new projects. It is concluded that librarians need to continue learning digital etiquette and gaining new skills and competencies for telecommuting.
Economic analysis of growth finance and liquid liabilities in Nigeria

Liquid liabilities are required to develop key sectors that drive the Nigerian economy by ensuring that loans are available for investment purposes. However, controversies concerning the effectiveness of growth finance in fostering liquid liabilities in Nigeria exist. Thus, this study examines the relationship between growth finance and liquid liabilities in Nigeria, with insight into Nigeria’s real sector. In achieving its objective, the study utilizes secondary data from the annual reports of the Central Bank of Nigeria (1980–2018). The study finds that gross domestic savings significantly drive liquid liabilities in the long run compared to other growth finance indicators, which include stock market development and remittance inflows. Therefore, the study recommends that to improve liquid liability, gross domestic savings, among other growth finance indicators, should be harnessed as a tool to efficiently influence liquid liabilities in the Nigerian economy. The study concludes that attention should be paid to development policies that drive all stakeholders’ gross domestic savings.
NFDI-Neuro: building a community for neuroscience research data management in Germany

Abstract Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.
Factors Affecting the Organizational Adoption of Blockchain Technology: An Australian Perspective

Blockchain Technology (BCT) is a novel innovation that has the potential to transform industries, for instance, supply chain, energy, finance, and healthcare.However, despite the potential and the wide range of benefits reported, organizational adoption of BCT is low in several countries including Australia.Some studies investigated the adoption of BCT in different countries, however, there is a lack of research that examines the organizational adoption of BCT in Australia.This study fills this gap by exploring the factors, which influence BCT adoption among Australian organizations.To achieve this, we used an interpretative qualitative research approach based on the Technology, Organization, and Environment (TOE) framework and the Institutional Theory.The findings show that organizational adoption of BCT in Australia is influenced by perceived novelty, complexity, cost, and disintermediation feature of BCT; top management knowledge and support; government support, customer pressure, trading partner readiness, and consensus among trading partners.
Deep Learning-Based Vein Localization on Embedded System

Venipuncture is a common process in medical treatment. In the fight against pandemic like COVID-19, it is often very difficult for medical staff to carry out venipuncture accurately, since the staff have to wear safety glasses and surgical gloves. In this work, we designed an embedded system which implements deep learning algorithm to localize veins from color skin images. The proposed method consists of a fully convolutional neural network (CNN) as encoder and feature extractor, a dilated convolution module, and a transposed convolution module as decoder. A synchronized RGB/Near Infrared (NIR) image database was constructed to provide the mapping information between the two image fields. A combined loss function which includes a per-pixel loss and a perceptual loss was presented to optimize the network parameters. To make the model adaptive to different images, a histogram specification scheme was adopted to transform the color style of an image. The model was then implemented on a NVIDIA Jetson TX2 development kit. Comprehensive experiments were conducted on different databases to evaluate the proposed method and the embedded system. Experimental results showed that the system has satisfactory performance and a promising perspective in daily medical treatment.
How to Train Your Energy-Based Model for Regression.

Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking. Training EBMs is however known to be challenging. While a variety of different techniques have been explored for generative modeling, the application of EBMs to regression is not a well-studied problem. How EBMs should be trained for best possible regression performance is thus currently unclear. We therefore accept the task of providing the first detailed study of this problem. To that end, we propose a simple yet highly effective extension of noise contrastive estimation, and carefully compare its performance to six popular methods from literature on the tasks of 1D regression and object detection. The results of this comparison suggest that our training method should be considered the go-to approach. We also apply our method to the visual tracking task, achieving state-of-the-art performance on five datasets. Notably, our tracker achieves 63.7% AUC on LaSOT and 78.7% Success on TrackingNet. Code is available at this https URL.
Designing a Pseudo-Random Bit Generator Using Generalized Cascade Fractal Function

A cascade function is designed by combining two seed maps that resultantly has more parameters, high complexity, randomness, and more unpredictable behavior. In the paper, a cascade fractal function, i.e. cascade-PLMS is proposed by considering the phoenix and lambda fractal functions. The constructed cascade-PLMS exhibits the required fractal features such as fractional dimension, self-similar structure, and covering entire phase space by the data sequence in addition to the chaotic properties. Due to the chaotic behavior, the proposed function is utilized to generate a pseudo-random number sequence in both integer and binary format. This is the result of an extreme scalability feature of a fractal function that can be implemented on a large scale. A sequence generator is designed by performing the linear function operation to the real and imaginary part of a cascade-PLMS, cascade-PLJS separately, and the iteration number at which the cascade-PLJS converges to the fixed point. The performance analysis results show that the given method has a large keyspace, fast key generation speed, high key sensitivity, and strong randomness. Therefore, the scheme can be efficiently used further to design a secure cryptosystem with the ability to withstand various attacks.
Scalable Authentication and Optimal Flooding in a Quantum Network

The global interest in quantum networks stems from the security guaranteed by the laws of physics. The deployment of quantum networks means facing the challenges of scaling up the physical hardware and, more importantly, of scaling up all other network layers and optimally utilizing network resources. Here, we consider two related protocols and their experimental demonstrations on an eight-user quantum network test bed, and discuss their usefulness with the aid of example use cases. First, we consider an authentication-transfer protocol to manage a fundamental limitation of quantum communication—the need for a preshared key between every pair of users linked together on the quantum network. By temporarily trusting some intermediary nodes for a short period of time (<35min in our network), we can generate and distribute these initial authentication keys with a very high level of security. Second, when end users quantify their trust in intermediary nodes, our flooding protocol can be used to improve both end-to-end communication speeds and increase security against malicious nodes.5 MoreReceived 2 April 2021Accepted 16 November 2021DOI:https://doi.org/10.1103/PRXQuantum.3.020311Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum networksQuantum Information