Reviewerly/exHarmony huggingface dataset:
50 Years of ITAL/JLA

Over five decades, Information Technology and Libraries (and its predecessor, the Journal of Library Automation) has influenced research and practice in the library and information science technology. From its inception on, the journal has been consistently ranked as one of the superior publications in the profession and a trendsetter for all types of librarians and researchers. This research examines ITAL using a citation analysis of all 878 peer-reviewed feature articles published over the journal’s 51 volumes. Impactful authors, articles, publications, and themes from the journal’s history are identified. The findings of this study provide insight into the history of ITAL and potential topics of interest to ITAL authors and readership.
The Use of Biometrics in Multifactor Authentication (MFA) for Cloud Computing Data Storage

Cloud Computing can be defined as a technology that allows access to computing resources and services in a flexible, scalable and highly available manner over the internet.It has been gaining momentum over the years with several organizations targeting cloud migration from the conventional on-premise approach for their data centers.Cloud Service Model offerings such as Platform as a Service (PaaS), Software as a Service (SaaS) and Infrastructure as a Service (IaaS) has grown in adoption with cloud data storage being one of the popularly used services in the public cloud.With the widespread growth of cloud computing services, new security and privacy issues have arisen, posing an open challenge to adoption of the cloud computing paradigm.Authentication stands to play an important role in mitigating the security and privacy issues foreseen.There is a need to enhance the current authentication schemes in use which are known to be prone to security threats and attacks.Multi-factor authentication allows the use of more than one authentication factor; knowledge, possession and inherence with combinations of the first two currently being used by most cloud service providers however still facing security breaches.In this paper, the author discusses security threats in cloud computing, the current multifactor authentication mechanisms in use and how to enhance multifactor authentication using fingerprint biometrics in cloud computing data storage to assure confidentiality and integrity of user data.
Influence Factors of College Students&amp;apos; Creativity: The Effect of Self-Esteem and Motivation

This study attempted to examine the influence of self-esteem and learning motivation for college students' creativity.Data was obtained from 530 students in 11 schools of 7 cities in China.From the results: (1) self-esteem (self-competence and self-liking) has a significantly positive influence on creativity; (2) IM (internal motivation) and SEM (synergistic external motivation) have significantly positive influences on creativity; (3) IM mediates the relationship between SEM and creativity, IM mediates the relationship between self-competence and creativity; (4) N-SEM (non-synergistic external motivation) moderated the relationship between self-esteem and creativity.When the level of individual self-esteem is higher, the higher N-SEM has a stronger promoting effect on creativity.Thus, self-esteem and motivation both impact on creativity, and constitute a dynamic cycle system of creativity which improving constantly.Implications of this study are further discussed.
Multi-parameter quantum magnetometry with spin states in coarsened measurement reference

We investigate the simultaneous estimation of the intensity and the orientation of a magnetic field by the multi-parameter quantum Fisher information matrix. A general expression is achieved for the simultaneous estimation precision of the intensity and the orientation, which is better than the independent estimation precision for the given number of spin states. Moreover, we consider an imperfect measurement device, coarsened measurement reference. For the case of the measurement reference rotating around the y-axis randomly, the simultaneous estimation always performs better than the independent estimation. For all other cases, the simultaneous estimation precision will not perform better than the independent estimation when the coarsened degree is larger than a certain value.
HIGH CAPACITY AND OPTIMIZED IMAGE STEGANOGRAPHY TECHNIQUE BASED ON ANT COLONY OPTIMIZATION ALGORITHM

The tremendous development of digital technology, it is mandatory to address the security while transmitting information over network in a way that observer couldn’t depict it. Measures to be taken to provide the security by establishing hidden communication using steganography principle which is help to camouflage the secret information in some carrier file such as text, image, audio and video. In this era of hidden data communication, image becoming an effective tool on account of their frequency, capability and accuracy. Image steganography uses an image as a carrier medium to hide the secret data. The main motive of this article is that the uses the combination of frequency domain and optimization method inorder to increasing in robustness. In this article, Integer Wavelet transform is performed into the host image and coefficients have been transformed. ACO optimization algorithm is used to find the optimal coefficients where to hide the data. Furthermore, sample images and information having been demonstrated which proved the increased robustness as well as high level of data embedding capacity.
Conditions of formation of professional competence in pedagogical practice of future history teachers

The author in the article analyzes the basic theoretical provisions of the conditions for the formation of professional competence among future teachers of history and presents the main approaches on this issue, which have developed in modern pedagogical and psychological literature. At the same time, attention was drawn to the differences between the two interrelated concepts of "competence" and "competence", as well as the three-component structure of the competence system in education, which includes key or basic, general-sectoral and vocational-specialized (subject) competencies acquired while studying one or another discipline and defining the term "professional competence". In the article, the main features of professional competence are highlighted: the need for knowledge formation, provided they are aware of their significance for future professional activities, a set of operational skills; the development of an algorithm for the implementation of professional exercises and the ability to creativity in the performance of professional tasks. This made it possible to determine the conditions for the formation of professional competence for future teachers in the course of their continuous and systematic activities if they have the appropriate professional qualities that contribute to this activity and general abilities based on knowledge, experience, values acquired through learning, upbringing, integration into the space of social and cultural relations. In addition, the psychological aspects of the formation of students of institutions of higher education of the indicated competence, which allows to significantly improve the effectiveness of the educational process during the training of future specialists, which accompanies the process of assimilation of theoretical foundations, means and methods of fulfilling professional tasks, can be considerably improved; ability to analyze, predict and independently choose means and means of action in certain specific situations; the ability to self-development and self-realization in the context of mastering modern scientific achievements and their implementation and positive attitude to their future professional activities. Key words: competence approach, history, competences, professional competence, future teachers, professional training, professional educational qualifications.
Digital Evidence Acquisition System on IAAS Cloud Computing Model using Live Forensic Method

Cloud Computing is a technological development that has been warmly discussed in recent years and has seen significant increases in usage, especially on the IAAS Cloud Computing model. The high rate of development of IAAS Cloud Computing model is in line with the high number of crimes involving IAAS Cloud Computing model on server virtualization. When a computer crime occurs and a digital forensic investigation will be carried out to uncover the case, it raises issues related to the acquisition of digital evidence. Because the acquisition model in general, it is done only to one operating system, while in virtualization there is more than one operating system, so the acquisition technique in general cannot be used because it takes only one operating system involved crime, and cannot acquire the whole data server related privacy data in other virtual operating systems. Therefore, research to make the acquisition system of server virtualization is needed. The focus in this research is to make system acquisition in server virtualization Proxmox using the live forensic method to produce a system that can acquire virtualization without disrupting the overall data server and in accordance with the principle of digital forensics. The resulting acquisition system can be a reference for investigators to investigate the IAAS Cloud Computing model on Proxmox virtualization and facilitate the investigator's work in the use of the system because the investigator simply chooses which virtual operating system to acquire, after which the system will work on its own the acquisition.
Efficient Hardware Implementation of Modular Arithmetic and Group Operation Over Prime Field

The need for secure communication over the network has increased drastically over recent years, and Elliptic Curve Cryptography (ECC) carries out a significant role in moving secured information.In this work, a hardware implementation of modular arithmetic and group operations over the prime field for an Elliptic Curve Cryptography Processor (ECP) for an efficient security system is proposed.The modular addition or subtraction operation takes only one clock cycle and the modular multiplication, which is designed using the interleaved modular multiplication method, requires 257 clock cycles.For elliptic curve group operation separate point doubling (PD) and point addition (PA) architectures are implemented in Jacobean coordinates.These new architectures are simulated in a Xilinx ISE 14.7.After that, the architectures are implemented in Xilinx Virtex-7 field-programmable gate array (FPGA) with the VHDL language.Proposed modular arithmetic and group operations can be utilized to design an Elliptic Curve Point Multiplication (ECPM).
Analysis of how the properties of structured data can influence the way these data are processed

AnALYSIS OF hOw the PROPeRtIeS OF StRUCtUReD DAtACAn InFLUenCe the wAY theSe DAtA ARe PROCeSSeDPurpose.The purpose of the article is to develop mathematical methods for processing "big data".This is based on the system analysis of properties for their structural organization.These methods allow us to optimize the basic characteristics of "big data".This includes increasing the search speed to process large volumes of fast incoming data while preserving their relevance.methodology.We suggested mathematical methods to work with a data structure "mtuples based on ordered sets of arbitrary cardinality (OSAC)".We determined pairwise combinations of Boolean elements as operands of the op erations investigated.The foregoing is based on the analysis of the data structure properties.We also calculated the dynamics of changes in the constituent pairwise combinations of the Boolean elements depending on the basis set cardinality for different groups of the given data structure.Findings.We estimated the time needed to execute methods of working with the OSAC data structure as func tional dependencies of the amount of data O( f (n)).We also determined the component of combinations for Boolean elements.For these elements, the execution of algorithms that implement the operation investigated is not required as the desired result is defined in the data structure property.Originality.We further developed a mathematical method which allows us to forecast the result of performing certain operations on elements of ordered data structure.This takes into account the position of the elements in the structure without using the computational algorithm.For the first time, we obtained an analytical dependency to determine the component number for Boolean elements of length m 2 .This includes an element represented by a tuple of smaller length m 1 in relation to the total number of Boolean elements of length m 2 .For the first time we also ob tained an analytical dependency to determine the minimum maxima of the functional dependency described above.Practical value.The results obtained in this paper can be used to minimize the time and computational resources needed to process "big data" represented by mtuples based on OSAC.
Diagnostics as a Reasoning Process: From Logic Structure to Software Design

Diagnostic tests are used to determine anomalies in complex systems such as organisms or built structures. Once a set of tests is performed, the experts interpret their results and make decisions based on them. This process is named diagnostic reasoning . In diagnostic reasoning a decision is established by using both rules and general knowledge on the tests and the domain. The artificial intelligence community has focused on devising and automating different methods of diagnosis for medicine and engineering, but, to the best of our knowledge, the decision process in logical terms has not yet been investigated thoroughly. The automation of the diagnostic process would be helpful in a number of contexts, in particular when the number of test sets to make decision is too wide to be dealt with manually. To tackle such challenges, we shall study logical frameworks for diagnostic reasoning, automation methods and their computational properties and technologies implementing these methods. In this paper, we present the formalization of a hybrid reasoning framework T L that hosts tests and deduction rules on tests, and an algorithm that transforms a T L theory into defeasible logic, for which an implemented automated deduction technology (called Spindle) exists. We evaluate the methodology by means of a real-world example related to the Open Web Application Security Project requisites. The full diagnostic process is driven from the definition of the issue to the decision.
Iterative Bayesian denoising based on variance stabilization using Contourlet Transform with Sharp Frequency Localization: application to EFTEM images

Due to the presence of high noise level in tomographic series of energy filtered transmission electron microscopy (EFTEM) images, alignment and 3D reconstruction steps become so difficult. To improve the alignment process which will in turn allow a more accurate and better three dimensional tomography reconstructions, a preprocessing step should be applied to the EFTEM data series. Experiments with real EFTEM data series at low SNR, show the feasibility and the accuracy of the proposed denoising approach being competitive with the best existing methods for Poisson image denoising. The effectiveness of the proposed denoising approach is thanks to the use of a nonparametric Bayesian estimation in the Contourlet Transform with Sharp Frequency Localization Domain (CTSD) and variance stabilizing transformation (VST). Furthermore, the optimal inverse Anscome transformation to obtain the final estimate of the denoised images, has allowed an accurate tomography reconstruction. The proposed approach provides qualitative information on the 3D distribution of individual chemical elements on the considered sample.
Using Named Graphs and Knowledge Graph Template Patterns for Efficiently Organizing FAIR Anatomy Data and Metadata

Currently, morphological data and metadata are still mostly published as unstructured free texts, which lack semantic transparency, cannot be parsed by computers, and do not comply with the FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. (2016) data principles, thus hampering their reuse by non-experts and their integration across many fields in the life sciences. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data and metadata can be recorded, stored, and communicated through the Web in the form of Resource Description Framework (RDF) triple statements that use the ‘Subject – Property – Object’ syntax of RDF and URIs of ontology classes and properties as well as URIs for individual entities as terminology. Since a given URI can take the ‘Subject’ position in one and the ‘Object’ position in another RDF statement, several triples can be linked to form a highly formalized and structured directed graph (semantic graph). After introducing an instance-based approach of recording morphological descriptions and their accompanying metadata as semantic knowledge graphs (i.e. Anatomy Knowledge Graphs ), we propose a knowledge graph template pattern for each type of anatomical observation and a pattern for documenting metadata. The use of template patterns for knowledge graphs provides Interoperability and Reusability of comparable anatomical observations and of their accompanying metadata and a means to meaningfully visualize information contained in semantic graphs in a user-friendly HTML representation. Stored in a tuple store, Anatomy Knowledge Graphs become Findable and Accessible through the store’s SPARQL endpoint. As a consequence, anatomy data and metadata documented as Anatomy Knowledge Graphs in a tuple store are FAIR. Finally, we suggest a general scheme of how to efficiently organize Anatomy Knowledge Graphs in a tuple store framework based on instances of named graphs, with each individual named graph instantiating an ontology class that relates to a particular type of observation (e.g., weight measurement named graph class). A named graph is a fourth element in an RDF statement ( ‘Subject – Property – Object – Named Graph’ ), turning the triple into a quadruple. All RDF statements that share the same URI in the ‘Named Graph’ position belong to the same named graph. The use of named graph resources allows meaningful fragmentation of the contents of an Anatomy Knowledge Graph (Fig. 1), which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data and metadata. This scheme has been implemented in the description module of the prototype for semantic Morph∙D∙Base.
Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.
A Cross-Sectional Study of Textese in Academic Writing: Magnitude of Penetration, Impacts, and Perceptions

This cross-sectional study examined the distribution of electronic texting patterns in academic writing and effects of textese on EFL learners’ writing performance. It also explored teachers’ perspectives on this phenomenon. Data were gleaned from 60 undergraduates enrolled for a license degree in English language and literature and 10 of their professors who were familiar with writing skills of this body of informants. A corpus of texts derived from exam scripts, assignments, and lecture notes was analyzed, and the teachers were interviewed. The results revealed that learners tend to transfer some patterns of instant messaging (IM) into their writing. However, this remains rather limited to morphosyntactic features and some other paralinguistic features and local-based contents. The phenomenon was pervasive across the sample in a descending order; learners at the entry level e-texted more than their seniors did. Besides, heavy texters used this deformed variety of English to compensate low writing proficiency in terms of spelling, word selection, and sentence structures. Additionally, texting was apparently evident in note-taking more than in answer sheets and assignments. The study concludes that textese is not a surrogate for the Standard English but an addition to it with a variance of scope and purpose of usage.
Hubungan Kemampuan Berpikir Kritis dengan Kemampuan Menulis Teks Editorial Siswa Kelas XII

&lt;div align="center"&gt;&lt;table width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This research was conducted using quantitative methods. Data analysis of this study was carried out using correlation techniques. The data collection process is done using critical thinking test instruments and test instruments writing editorial texts. The relationship between variables was analyzed using Pearson Product Moment correlation analysis techniques. The results of this study indicate that there is a positive and significant relationship between critical thinking skills and the ability to write editorial texts at a significance level of 0,000. That is, one of the determinants of the success of writing editorial texts is that students must have high critical thinking skills.&lt;/p&gt;&lt;p class="Default"&gt;&lt;strong&gt;A&lt;/strong&gt;&lt;strong&gt;bstrak:&lt;/strong&gt; Penelitian ini dilakukan dengan menggunakan metode kuantitatif. Analisis data penelitan ini dilakukan dengan menggunakan teknik korelasi. Adapun proses pengambilan data dilakukan dengan menggunakan instrumen tes berpikir kritis dan instrumen tes menulis teks editorial. Hubungan antar variabel dianalisis menggunakan teknik analisis korelasi &lt;em&gt;Pearson P&lt;/em&gt;&lt;em&gt;roduct &lt;/em&gt;&lt;em&gt;M&lt;/em&gt;&lt;em&gt;oment.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;Hasil penelitian ini menunjukkan&lt;em&gt; &lt;/em&gt;bahwa terdapat hubungan positif dan signifikan antara kemampuan berpikir kritis dan kemampuan menulis teks editorial pada taraf signifikansi 0,000. Artinya, salah satu faktor penentu keberhasilan menulis teks editorial adalah siswa harus memiliki kemampuan berpikir kritis yang tinggi.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;