Reviewerly/exHarmony huggingface dataset:
Foundations in Linked Data for Serialists

This report summarizes an all-day workshop on linked data, with emphasis on its application to the description and discovery of serials. The presenters reviewed the history, lexicon, and technological underpinnings of linked data. They discussed the basics of ontologies, including an exercise with Protégé ontology-building software. They introduced Resource Description Framework (RDF), the basic structure for linked data, and discussed various syntaxes used with it, with an emphasis on Turtle (Terse RDF Triple Language). They examined individual Resource Description and Access elements for serials expressed in the Bibliographic Framework Transition Initiative (BIBFRAME) 2.0 ontology, and gave a brief overview of BIBFRAME testing and experimentation projects by the Library of Congress, Cooperative Online Serials, and the Linked Data for Production project.
Dehazing of Images using Minimum White Balance Optimization

The quality of image captured in presence of fog and haze is degraded due to atmospheric scattering. In order to restore such images, several dehazing algorithms have been proposed. These algorithms sometimes, results in either a contrast distorted dehazed image or a dehazed image that has influence of dense haze. In order to solve this problem, dynamic facsimile dehaze system built on minimum white balance optimization is proposed. This paper proposed a system that integrates some famous single image dehazing algorithms and enhance their outputs using histograms and adaptive histograms; then adaptively select the output with minimum white balance distortion in order to get the optimum output. Experimental results demonstrated that the presented system can attain better dehazing effect and further improves universality of dehazing methods. Also proposed system improves luminance and contrast of dehazed images to a certain extent.
Modified cluster-variational method of triangulate estimation under the conditions of uncertainty.

Triangulate estimation modified method, based on “increasing” conception of target location ordinary marks and their following “clusterization”, for expected uncertainty modification is developed. Various tests for detection optimality of questionable measuring channels and consequent estimate generation algorithms of target location are given. Comparison results of these algorithms in accuracy and operational efficiency are presented.
Enhanced Image Vision and Resolution during Low Light Conditions using GANs

Low light computer vision is an arduous task because of the low signal to noise ratio and less photon count. This means that the images captured in low light experience noise, which can result in blurring of the image. Although there are multiple techniques to overcome the noise and blur, their results are bounded in undue conditions as in there is a drop in the video imaging at night. This low light enhancement is a daring task as there are multiple factors like brightness, de-noising, de-blurring, contrast must be handled at the same time. Even the development of a CNN has proved to perform poorly on such data. This paper uses a technique to take care of this issue using GANs. Our technique gives a platform to enhance the image captured in low light and increase its resolution giving out an enhanced super resolute image. To support the low light image processing, we have used a dataset of low-light images. This method can give promising results on the dataset, and display a break for the future work.
Persepsi Mahasiswa Perbankan Syariah Semester II Tentang Penggunaan Aplikasi Google Classroom Selama Perkuliahan Daring pada Masa Pandemi Covid-19

This study aims to describe the perceptions of second semester Islamic banking students regarding the use of the Google Classroom application during online lectures during the COVID-19 pandemic. Online lectures are one of the terms used during the COVID-19 pandemic which means lectures that are carried out on a network and connected via the internet. The perceptions of students studied were limited to aspects of teaching and learning, lecturers' capabilities or abilities, and facilities and infrastructure during the implementation of online lectures through the Google Classroom application. This research is a descriptive research with a qualitative approach. The subjects of this study were all second semester Islamic banking students who had attended online lectures in the economic mathematics course at the Jam'iyah Mahmudiyah Islamic High School Tanjung Pura, Langkat Regency, for the 2019/2020 academic year. Data were collected using a questionnaire filled out online by students. The data analysis technique used in this research is qualitative analysis which consists of data collection, data reduction, data presentation, and drawing conclusions. The results showed that the second semester Islamic banking students' perceptions of using the Google Classroom application during online lectures during the COVID-19 pandemic were in the good category. However, students still prefer lectures to be held face-to-face.
Web Presence of Repositories of Indian Institutes of Technology: A Webometric Study

Web Presence of Repositories of Indian Institutes of Technology: A Webometric Study Dr. Babita Jaiswal, Richa Arya Abstract Links weave web documents together in a complex, structured hypertext corpus. Web links studies have been conducted by the scholars and information professionals all over the world since its introduction. The main objective of the study is to investigate the web presence of repositories of Indian Institutes of Technology (IITs) as a part of the World Wide Web. To investigate the web presence the few sub-objectives have been framed such as to identify IITs which are having their repositories; to find out the TLD, cc TLD and file formats use by the repositories; to find out total number of Web Pages, Link Web Pages, Self Link Web Pages, External Link Web Pages and Inlink Web Pages; to calculate Overall Web Impact Factor, Simple Web Impact Factor, Self Link Web Impact Factor, External Link Web Impact Factor and Inlink Web Impact Factor. Google covers more hyperlinks with advanced search facilities to count links possessed by the websites. Therefore, the present study has used Google to collect raw data, i.e., number of web pages, number of inlinks, self links and external links etc. Boolean operators are also used to form query syntaxes for retrieving data. SocSciBot web crawler is also employed. It is used to create network map of all the websites. URLs/websites of repositories are tested through command line textual queries like link, linkdomain, site, etc. Findings have been derived after analyzing the data. Full Text: PDF DOI: 10.15640/jlis.v8n1a10
Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

Aspect-oriented sentiment analysis is done in two phases like aspect term identification from review and determining related opinion. To carry out this analysis, features play an important role to determine the accuracy of the model. Feature extraction and feature selection techniques contribute to increase the classification accuracy. Feature selection strategies reduce computation time, improve prediction performance, and provides a higher understanding of the information in machine learning and pattern recognition applications etc. This work specifically focuses on aspect extraction from restaurant review dataset but can also be used for other datasets. In this system, we proposed a multivariate filter strategy of feature selection which works on lemma features. This method helps to select relevant features and avoid redundant ones. Initially, the extracted features undergo preprocessing and then the “term-frequency matrix” is generated which contains the occurrence count of features with respect to aspect category. In the next phase, different feature selection strategies are applied which includes selecting features based on correlation, weighted term frequency and weighted term frequency with the correlation coefficient. The performance of weighted term frequency with correlation coefficient approach is compared with the existing system and shows significant improvement in F1 score.
Transform Domain Block Based Watermarking using Spatial Frequency and SVD

Digital image watermarking has been proposed to protect the digital multimedia content. The main objectives of watermarking scheme are robustness, reliability, security against numerous attacks. To improve the imperceptibility, robustness and capacity of the watermarked image, this paper presents a transform domain watermarking method using spatial frequency and block SVD. The spatial frequency is used to select the appropriate blocks for embedding the watermark image by transforming the SVD coefficients of these blocks of the cover image. In this paper first we scramble the cover image by ZIG-ZAG sequencing and then rearranged. After that Shift Invariant Discrete Wavelet Transformed (SIDWT) cover image is partioned in to non-overlapping blocks. Then find out the spatial frequency of these blocks, those blocks which spatial frequency value greater than threshold value are selected for embedding process. Now the watermark image directly embedded by modifying the SVD coefficient of these blocks and get watermarked image. Then inverse process is applied for extracting for watermark image form noisy image. Experimental outcomes show that the proposed scheme is higher imperceptible, robust against various image processing attacks and produce improved results as compared to previous presented schemes
On Semigroups of Relations with the Operation of Left and Right Rectangular Products

A set of binary relations closed with respect to some collection of operations on relations forms an algebra called an algebra of relations.The class of all algebras (partially ordered algebras) isomorphic to algebras (partially ordered by set-theoretic inclusion ⊆ algebras) of relations with operations from Ω is denoted by R{Ω} (R{Ω, ⊆}).An operation on relations is called primitive-positive if it can be defined by a formula of the first-order predicate calculus containing only existential quantifiers and conjunctions in its prenex normal form.We consider algebras of relations with associative primitive-positive operations * and ⋆, defined by the following formulas ρ * σ = {(u, v) : (∃s, t, w)(u, s) ∈ ρ ∧ (t, w) ∈ σ} and ρ ⋆ σ = {(u, v) : (∃s, t, w)(s, t) ∈ ρ ∧ (w, v) ∈ σ} respectively.The axiom systems for the classes R{ * }, R{ * , ⊆}, R{⋆}, R{⋆, ⊆}, and bases of quasi-identities and identities for quasi-varieties and varieties generated by these classes are found.
Collaborative Research: Center for Mobile Hands-On STEM

Abstract Collaborative Research: Center for Mobile Hands-On STEMMobile Hands-On STEM (MOHS) has been implemented and studied in three NSF fundedprojects: The Mobile Studio Project (RPI, Howard, Rose-Hulman, U Albany); Lab-in-a-Box(Virginia Tech); and TESSAL (Georgia Tech). Application of MOHS pedagogy has expanded atall partner institutions and been successfully transferred to other institutions in the US (e.g.Wisconsin-Madison, Boston University, Morgan State University, Virginia Western, CommunityCollege of Rhode Island, etc.) and in other countries (e.g. Ethiopia, Cameroon). In all cases,hands-on learning has been successfully implemented at low cost, with more engaged studentsand instructors, and hands-on learning implemented in courses that were traditionally only theorybased. Although the development and spread of this exciting new approach to STEM educationargue for broad application, the documented case for its adoption is not yet at the stage where allSTEM educators can fully appreciate its merit. This is due, in part, to the characteristics of theearly adopters who tend to be curious and innovative about how students learn within theircontent; at ease with the technology of electrical and computer engineering; well acquainted withSTEM educational research, etc. As a result, the most effective approach to STEM education isstill in question in the broader community and best practice methods of dissemination of MOHSpedagogy to the entire STEM community have not yet been identified.RPI, as the lead institution, along with Georgia Tech, Virginia Tech, University of Albany, Rose-Hulman Institute of Technology, Howard University, and Morgan State University are pursuingactivities that support the following goals: I. Gather strong evidence of the effectiveness of Mobile Hands-On STEM pedagogy on student learning II. Develop a pro-active dissemination strategy that will be effective with all of the STEM educational community.To achieve these goals, we are documenting the evidence already available on mobile hands-onlearning, identifying and standardizing the assessment tools utilized by the three main partners(RPI, Georgia Tech, Virginia Tech), developing and implementing new assessment tools thatmeasure student learning as well as ease of adoption by instructors, holding a practitioners’ bestpractices workshop for instructors who currently employ mobile hands-on education to build acommunity of users to pool expertise, holding focus groups among different constituencies toidentify the barriers for wide-spread adoption and how these might be overcome, holding a seriesof mini workshops to introduce mobile hands-on learning to instructors from these differentconstituencies, and will pilot a full-scale workshop for new instructors to mobile hands-onlearning near the end of the proposed program.
Geologic map of the southern flank of Mauna Loa Volcano, Island of Hawai‘i, Hawaii

First posted April 28, 2020 For additional information, contact: Contact HVOVolcano Science Center, Hawaiian Volcano ObservatoryU.S. Geological Survey On the Island of Hawaiʻi, Mauna Loa, the largest volcano on Earth, has erupted 33 times since written descriptions became available in 1832. Some eruptions began with only brief seismic unrest, whereas others followed several months to a year of increased seismicity. Once underway, its eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa has great volcanic-hazard potential for the Island of Hawai‘i. Volcanic hazards on Mauna Loa may be anticipated, and risk substantially mitigated, by documenting the past activity to refine our knowledge of the hazards and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.Although most Mauna Loa eruptions begin in the summit area at 12,000 feet (ft) elevation, the Southwest Rift Zone (SWRZ) was the source of at least 10 flank eruptions since 1843. The SWRZ extends from the summit towards Kalae (South Point) at sea level. The lowermost part of this rift zone, marked by Pu‘uʻoke‘oke‘o to the north at 6,874 ft elevation and extending to the sea, makes up the lower SWRZ. The community of Hawaiian Ocean View Estates, with a population of about 2,500, is the largest in the region. The subdivision is built entirely on flows erupted from southern Mauna Loa, and some source vents are located within the subdivision. Approximately 25 percent of the subdivision is within Hazard Zone 1.From east to west, the map covers the area from Punalu‘u to Miloli‘i and, from north to south, extends from north of Pu‘uʻoke‘oke‘o to Kalae (South Point). The map encompasses 1,163 square kilometers of the southwest flank of Mauna Loa, from 7,325 ft elevation to sea level. It shows the distribution of eruptive units (flows), which are separated into 16 age groups, ranging from more than 100,000 years before present to A.D. 1950.Lava erupted from the SWRZ typically flows to the west, east, or south (depending upon vent location relative to the rift crest) and generally produces narrow flow lobes. Both morphologic lava flow types—‘a‘ā and pāhoehoe—are present. In general, the northern part of the mapped area is dominated by flows from the middle SWRZ, whereas the southern part contains flows from the lower SWRZ and includes areas adjacent to, and downslope of, the rift zone. The exceptions are flows that originated from the upper SWRZ in the northeastern part of the Punaluu quadrangle.
An Investigation on various Text Information Extraction (TIE) Algorithms for Images

Automated text recognitions as of innate pictures has received a wide notice with latent application during picture recovery, robotic as well as intellectual transportation systems.Text data into innate scenery pictures are an vital source of several picture-based application like automatic geo-coding, assistive navigation, contents-based picture recovery as well as scene understanding.Conversely, segmenting texts as of a complicated backgrounds through millions of color are still date challenge to scientist.With the availability of digital cameras with higher resolution, image analysis has become a real-time possibility.Numerous approaches have been developed for extracting text information from camera-captured text pictures as well as the presence of commercialized optical character recognition (OCR) systems have supported a lot.This paper deals with an extensive survey on all the literatures that have been presented till date in this regard of extraction of text from real-time scenes.
Estimation of Hidden Neuron Requisite for Predictive Conversion from Sign Language to a Formal Language

Using sign language for communication has existed since millennia and has evolved over the ages to support the hearing impaired to a great extent. There have been numerous wonderful inventions and innovations aimed at improving the living conidtions of the hearing or speech impaired using technology which was contemporary. However, till date there exists no standard that can be shown as a single universal sign language translation system. This is partially due to the research happening in small pockets and lack of technology penetration in some countries. Largely the efforts to design translation are concentrated on Americal Sign Language (ASL) and Thai Sign Language (TSL). Other than the two, Indonasian Sign Language (SIBI) is the front runner in standardising a translation system. In our previous paper we had attempted to develop an ANN based translator for ASL. The system performed as per expectations. However, since the number of hidden neurons was taken as a variable, the estimation still remained a challenge.In this paper, we have developed a system to estimate the number of hidden neurons required for the accurate translation of ASL
Research on Digital Media Image Data Tampering Forensics Technology Based on Improved CNN Algorithm

At present, digital media has widely appeared in the modern Internet network, which has an extremely far-reaching impact on people's life. Due to the data characteristics of digital media images, it is easy to tamper with different purposes in the process of digital media communication. This research is based on CNN algorithm technology to study the methods of digital media image data tampering forensics, constructs the corresponding image tampering operation chain information theory model, and further uses the limited convolution CNN networks algorithm to identify the tampering behavior of pictures of different sizes. Through the comparative simulation analysis of CNN algorithm and SVM algorithm on digital media image data tampering forensics, it can be seen that CNN algorithm has higher recognition accuracy and recognition efficiency.
Secure Healthcare System using Blockchain Technology

Due to the popularity of crypto currencies, blockchain technology has gotten valuable for various areas.In the healthcare domain, blockchain became a key component that can drive information streams as significant and adaptable enough to enable continuous secure human services framework.So, this system is proposed as the secure healthcare system by using blockchain technology.To reduce the redundancy from each block of health data, this system proposes the modified mutual information (MMI) method as the contribution.MMI method identifies the quality of relationships between features of various natures without dispersion law limitation.And then, this system uses MD-5 (message-digest-5) hash algorithm for the hash value.Each block with hash value is transmitted through the blockchain.So, this system allows the healthcare providers to easily and securely share health data.
Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals

The significant objective of research in the distributed cloud networks is resource planning and allocation using voids in scheduled intervals, which grabbed several researchers’ attention in contemporary literature. Minimum failures of resource scheduling, completion of a robust task and fair usage of the resource were the important parameters of resource scheduling schemes. Therefore, this paper projected scalable-resource scheduling method for distributed CC environments, which aimed to attain the metrics of scheduling. The projected method is known as "Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals (RPA-DCN)," where resources are scheduled towards the corresponding task so that, the optimum using of idle time of resource is attained. The projected method performs scheduling in sequential order, and they were allocation of optimum resource when no single resource is identified to allocate, then optimum manifold idle resources are allocated with a considerable filling of schedule intervals. This paper discusses (a) pre-requisite of resource scheduling schemes, (b) current scheduling methods found in recent literature, (c) methods &amp; materials utilized and method of projected resource scheduling scheme (d) and its significance over other standard methods. The analysis of performance for the proposal conducted by metrics cross-validation such as load vs. loss, optimality of task completion &amp; process-overhead in the resource-scheduling. Here, the simulation outcomes exhibit that the projected method is robust and scalable under modified metrics
Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.