Reviewerly/exHarmony huggingface dataset:
The Brazilian scientific production on bibliographic studies: a diachronic analysis

It describes the theoretical conceptions and concrete activities related to the field of Bibliography in Brazil, considering its theoretical and applied development, from scientific production in journal articles indexed in Brapci. The research aims at identifying the production about the subject of bibliography by searching the analysis of scientific literature indexed in Brapci; outlining the profile of this production, favoring the categories of researchers, themes and theoretical influences from the Information Science field and contextualizing the conceptions and activities in the scenario in which it was highlighted. The research diachronically analyzes the production on the subject, to highlight the evolution of the concept of bibliography in scientific production in Brazil, highlighting the relevant concepts and activities. It is a study of diachronic and descriptive character, which turns to the content of the articles, based on categories of analysis and information raised in the considered scientific production, corresponding to the period between 1972 and 2017. It also observes that in the literature produced in Brazil on the subject of bibliography, the emphasis is on the techno-methodological dimension, implemented in Librarian Science and documentary, explaining and organizing work and study tools, following the socio-political dimension related to the interventions in various forms in the production of instruments to support knowledge and its achievements in favor of the interests covered by social policies and, less expressively, in the whole of production, presents the epistemological theoretical dimension, related to the foundations and rational and historical reconstruction of the art of bibliography and bibliographic science.
Exploring Differences Among Darknet and Surface Internet Hacking Communities

Cyber-threat intelligence (CTI) has matured into its own industry within recent years. CTI efforts frequently involve scrutinizing data within Darknet communities to understand emerging threats. Many hackers within the Darknet share knowledge and other information through a variety of formats, including video. At the same time, many hackers are also making use of the “surface” Internet and traditional video-sharing platforms to disseminate hacking knowledge. Gleaning intelligence from the Darknet can be a very laborious and costly task, raising the question of how meaningful and valuable are the hacker patterns that can be observed on the surface Internet. Extant research contains no studies that compare and contrast hacking videos uploaded to the Darknet versus those uploaded to traditional Internet communities. In this research-in-progress, a testbed of hacking videos is constructed by sourcing videos from a popular video-sharing website, as well as several Darknet forums. The testbed is scrutinized to understand differences in how the populations of users watching such videos respond to them, and whether there are any unique engagement patterns that emerge within the Darknet and surface Internet populations. The results of this work serve to justify further investigations into the hacker knowledge gap between the Darknet and the traditional Internet.
Computer Network Platform Construction of Mechatronics Online Course and Teaching Method based on the Internet

This paper focused on computer network platform construction of mechatronics online course using the internet technology and made the research on corresponding teaching methods by this platform. The teaching method using hybrid teaching mode online and offline caused the students studying without time and the space restrictions [1]. To improve the teaching efficiency, this paper integrated the physical classroom with online course organically and took full advantage of both of them.
Improved The Learning Achievement of Elementary School Students Through The

The purpose of this study is to find out the improvement of elementary school students' learning achievement through the medium of picture word cards. The problem in this study is still the low learning achievement of students seen from the initial test of learning achievement. This research took place in class I SDN Babatan IV/459 Surabaya. The school is located at Jalan Raya Menganti Babatan Wiyung District of Surabaya City. The research time was carried out in semester 1 (odd) in October of the 2020/2021 School Year. The subject of this study was a student of class I SDN Babatan IV /459 Surabaya which amounted to 28 students. This research design is class action research (PTK) through stage flow (planning, research action, data collection and data analysis) carried out with two cycles. The result of this study is an increase in student learning achievement in each lesson cycle with a minimum completion criteria (KKM) of 70, cycle I completed learning of 15 students or 60% of students who have achieved the minimum completion criteria with an average score of 72.95. While cycle II has increased, 28 students completed their studies or 100% of students have achieved the minimum completion criteria with an average score of 85.4.
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots.

We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about various topics can be represented as an answer set program. However, to advance the conversation, we need to solve the problem of finding relevant consistent concepts, i.e., find consistent knowledge in the neighborhood of the current topic being discussed that can be used to advance the conversation. Traditional ASP solvers will generate the whole answer set which is stripped of all the associations between the various atoms (concepts) and thus cannot be used to find relevant consistent concepts. Similarly, goal-directed implementations of ASP will only find concepts directly relevant to a query. We present the DiscASP system that will find the partial consistent model that is relevant to a given topic in a manner similar to how a human will find it. DiscASP is based on a novel graph-based algorithm for finding stable models of an answer set program. We present the DiscASP algorithm, its implementation, and its application to developing a conversational socialbot.
STGRN: A Spatial-Temporal Graph Resonance Network for Social Connection Inference.

Social Connection Inference (SCI), as one of the most important social network analysis techniques, has been a popular research direction for decades. A lot of studies have been utilizing spatial-temporal co-occurrence events for mining latent social relations. The previous studies mainly look into the characteristics of the point-to-point meetup events. Moreover, some methods employ the social neighborhood of two target objects for better prediction. However, we have rarely seen research utilizing the network structure formed by meetup events for better SCI performance. In this paper, we propose a graph neural network framework, namely Spatial-Temporal Graph Resonance Network (STGRN), for social connection inference enhancement. Firstly, we calculate co-occurrence events’ characteristics, including two novel features we designed: Geo-Spread and ActM-Rate. Secondly, we build a network embedding based on meetup relations. Thirdly, we combine the meetup events, social network, and co-occurrence network for the final prediction. In the end, we conduct extensive experiments on public datasets to prove our method’s effectiveness.
Research on real-time posture analysis of dance based on spatial data partition

research-article Share on Research on real-time posture analysis of dance based on spatial data partition Author: Bo Wang Hohhot Vocational College, China Hohhot Vocational College, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2288–2292https://doi.org/10.1145/3482632.3484147Online:22 November 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Simplifying Statistical Modeling for Infrastructure-as-a-Service Cloud Computing

Cloud computing is a multitenant system where multiple users share the resources provided by the service provider. It is important to adjudge the performance of the cloud servers to ensure the successful deployment and execution of software. The inherent nature of various data centers and the request rate of users make it challenging to evaluate the performance of the cloud centers mathematically. The current work proposes a novel queuing-based analytical model based on a general expansion method to assess the performance of cloud servers. The proposed work's main advantage is that the model can be applied irrespective of the type of distribution function assumed for inter-arrival times and service time. The queuing characteristic of both the single server system and multi-server are taken into consideration.The model determines the key performance indicators such as response time, waiting time, and other performance metrics. The model critically analyzes the relationship between the number of the server, service rate probabilistic distribution function of the servers, response time, waiting time, and the number of the buffers. It is observed that the system's performance having a general distribution service rate outperforms the system having an exponential service rate.
Application of Information Technology to Promote the Practice of ESP Teaching

Human beings spread technology through education, and information technology is now also rapidly promoting education. Combining with the information literacy standards of College students, we have reformed the teaching design of ESP and integrated information literacy into ESP teaching by using modern information technology. Training students to use modern tools for efficient reading and information retrieval, their ability to understand and summarize the original information, create and express new information has been significantly improved.
Contribution of vocal fold thickness to gender perception

Male vocal folds are often longer and thicker than female vocal folds. The length difference is generally considered responsible for the notable difference in fundamental frequency between men and women, which plays an important role in gender perception. The role of the thickness difference in gender perception is less clear, although our recent studies demonstrated an important role of thickness in the control of voice quality. The goal of this study is to investigate the contribution of changes in vocal fold thickness and stiffness to gender perception. Synthetic voices were generated using a three-dimensional vocal fold model with parametric variations in vocal fold geometry, stiffness, adduction, and subglottal pressure. The vocal tract was kept constant in order to focus on the contribution of the voice source. Listening subjects were asked to judge speaker gender in a multiple choice task. Preliminary results showed a significant effect of vocal fold thickness and transverse stiffness on gender perception, with voices produced by thicker vocal folds and lower transverse stiffnesses more likely to be judged as male. Acoustically, gender perception scores were correlated with the fundamental frequency and spectral shape. [Work supported by NIH.]
An Enhanced Homomorphic Encryption for preserving privacy in Cloud Communications

As huge data need to store on clouds for big data analytics and related features to analyze data computationally. Cloud computing serves many services to the client over their data such as data storage, analysis and different resources. To avail such services over clouds, the client needs to provide the access to cloud service providers (CSP’s) over their data which leaves data unprotected and confidentiality of their data is at stake [1]. Different techniques are proposed to preserve the privacy and confidentiality of client’s data. One of the most common is, cryptography, which converts the plaintext to unreadable form that is ciphertext. But almost all the existing cryptographic techniques are not up to mark and don’t have potential to perform the computation on the ciphertext. The recent trends in field of cryptography, the homomorphic encryption is emerged as a boom, which enhance the privacy of data to a large extent, without compromising the confidentiality of users’ data at cloud end. Homomorphic Encryption provides the ability to perform computation on encrypted data, even when data is in use. Most of the existing homomorphic encryption algorithms are having high computational and time complexity [2-3]. In this paper, an enhanced homomorphic encryption algorithm is proposed specifically for the cloud environments, may having better performance. The algorithm uses prime number-based matrix for encryption and decryption [4]. The algorithm may give better performance in terms of encryption, decryption and overall execution time.
Warning: it's attack season

Network SecurityVol. 2021, No. 10 RegularsWarning: it's attack seasonTom McVey, Menlo SecurityTom McVeySearch for more papers by this author, Menlo SecuritySearch for more papers by this authorTom McVey; Menlo SecurityPublished Online:11 Dec 2021AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 January 2021Volume 2021Issue 10ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 11 December 2021 Published in print 1 January 2021 Information© MA Business Ltd.PDF download
Self-assessment of the Pre-graduate Practice of Students in Social Pedagogy

The article presents the organization and the course of the undergraduate practice of students in the specialty “social pedagogy”. Insofar as the self-assessment skills are among the important soft-skills of the modern specialist, an attempt is made to establish the degree of his / her formation by comparing the self-assessments of two groups of students – trainees at school and trainees at a social institution. Conclusions are made about the need for purposeful formation of self-assessment skills in a specific practical environment.
Deep learning based hand written character recognition for manuscript documents

Handwritten manuscripts contain much ancient information related to astrology, medicines, grammar etc. They are of various forms such as palm leaves, paper, stones etc. These manuscripts are preserved by the method of digitization with noise introduced. By using proper filtering as well as denoising methods these noises are eliminated and the images are restored. It is finally required to recognize the handwritten characters automatically from the restored image enabling the researchers and enthusiasts for going through the document very easily.This proposed work deals with the creation of handwritten characters dataset for all the characters within a specific dimensional area and recognition of handwritten characters using deep learning method. First the handwritten dataset is created from different human handwritings in a specific format, scanned and each character with suitable dimension is obtained by labeling them as per sequence. Then various forms of convolution network are applied for the character recognition and the results are compared to obtain the suitable net for the Tamil character recognition from the handwritten document.
Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction

In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.
Использование технологий работы с большими данными в российской науке

A conceptual framework and a set of indicators have been developed for monitoring the use of big data technologies in science. The conceptual framework has three main sections: the level of maturity of technology use in scientific organizations, the level of technology use in science, and barriers to the use of big data technologies in science. An assessment of the level of big data technology use in science and a comparative analysis of the results obtained for Russia and other countries were made. It was found that, compared to the global average, Russia has a higher share of articles using big data technologies in the social sciences and humanities. A regularity has been revealed: publications that use big data are cited significantly more frequently on average than all publications of the country in the same fields of science.
Bifurcation Spiking Neural Network

Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune. Though it is demanded to have an adaptive control rate, it is a non-trivial task because the control rate and the connection weights learned during the training process are usually entangled. In this paper, we show that the firing rate is related to the eigenvalue of the spike generation function. Inspired by this insight, by enabling the spike generation function to have adaptable eigenvalues rather than parametric control rates, we develop the Bifurcation Spiking Neural Network (BSNN), which has an adaptive firing rate and is insensitive to the setting of control rates. Experiments validate the effectiveness of BSNN on a broad range of tasks, showing that BSNN achieves superior performance to existing SNNs and is robust to the setting of control rates.
APhL aligner: A neural network forced-alignment system

Forced alignment is increasingly used in phonetics to automatically produce boundaries between words and phones. These boundaries can have significant errors and are often only placed at some predetermined time interval, like every 10 ms. We discuss some potential remedies to these difficulties and test them in a new neural network-based forced alignment system called the APhL Aligner, trained on the TIMIT and Buckeye speech corpora. In part, errors incurred during forced alignment can be attributed to the acoustic models that attempt to separate phones from each other. Even state-of-the-art neural network models struggle to acoustically separate phones. We examine the effect of relaxing the requirement to separate phones by instead training separate detectors for each phone class. Resolving the 10 ms interval difficulty requires a different approach. As with most aligners, we perform a Viterbi-style alignment to align windows of audio spaced at 10 ms to the phone string given by a pronunciation dictionary. We add an additional step, however, and use linear interpolation to determine an intermediate point after the 10 ms interval to place the boundary. We compare the results of these manipulations to the results of the Montreal Forced Aligner, custom-trained on the same data.
Estimating Hazard Exposure in Tower Crane Lift Operations Using BIM and Path Planning Algorithm

Estimating Hazard Exposure in Tower Crane Lift Operations Using BIM and Path Planning Algorithm Songbo Hu, Yihai Fang and Robert Moehler Pages 576-582 (2021 Proceedings of the 38th ISARC, Dubai, UAE, ISBN 978-952-69524-1-3, ISSN 2413-5844) Abstract: Tower cranes play an essential role in the execution of most construction projects. Unfortunately, they are also a major source of fatalities and injuries in the industry, owing to their great mass and large footprint on the site. Aiming to proactively identify and mitigate safety hazards in the design and planning stage, Prevention through Design (PtD) has been proven effective in various construction scenarios. The advent of Building Information Modeling (BIM) further strengthens the power of PtD by providing early access to comprehensive and accurate project data. This study proposes a conceptual framework aiming to automatically identify and quantitatively estimate the exposure of hazards associated with the operation of tower cranes. A literature review on crane lift safety is first carried out to identify major hazards related to tower cranes. Based on the results from path planning algorithms, a quantitative approach is presented to estimate hazard exposure during the operation of tower cranes in any given period during the construction. Thirdly, BIM entities and attributes necessary to describe tower-crane-related hazards are defined. Lastly, an application scenario is discussed to demonstrate the benefits and limitations of the proposed method. Findings in this study are expected to expand the application of PtD in more dynamic and complex construction scenarios and facilitate its integration with emerging automation and information technologies. Keywords: Tower crane; lift safety; hazard exposure; path planning; building information modeling DOI: https://doi.org/10.22260/ISARC2021/0078 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley