Reviewerly/exHarmony huggingface dataset:
Goodbye, Paley… Hello, Charles!: Marketing a Library Move

How do you prepare a campus for the closure of one main library and the opening of a brand-new building? Temple University Libraries faced this question in 2019. Their marketing team answered the call by creating a campaign that honored their original Paley Library while building excitement for the new Charles Library. As part of this campaign, library staff worked together to create their own “Mean Tweets” video, reading real tweets patrons had posted about Paley over the years. In this column, the team's director reflects on the process and how it changed the tone of their overall campaign.
Assessment of Readiness and Usability of Information Systems Use

The assessment of the use of information systems has been carried out by many researchers. This research was conducted in Private Universities in Indonesia, which currently involve many information systems in many ways, especially those related to the management of Higher Education, by measuring the readiness and usability of the use of information systems with models that I build from the integration of two models. The results of the measurement of this study were obtained from the distribution of questionnaires, there were 47% of respondents who filled 61-80% of the level of IS usage and 68% of respondents stated their readiness in the level of readiness to use IS. The stage consists of evaluating reflective measurement models and structural model assessments. Evaluating reflective measurement in evaluating internal consistency reliability using Composite Reliability, Reliability Indicator, Convergent Validity, and Discriminant Validity, finally concluded that the use of the Readiness and Usability integration model can be forwarded to a more complex research stage and can use the questionnaire.
Computer-Assisted Language Learning (Call): The Use of PRAAT with Babcock University

Computer-Assisted Language Learning (Call): The Use of PRAAT with Babcock University ‘Demola Jolayemi PhD, cum laude Oyinloye, Comfort Adebola PhD Abstract The use of the English language continues to be viable and veritable across the countries of the continent, even in the non-native environments such as Nigeria, Ghana, and India, where English is the official language. As an index of achievement within these non-native contexts, there is a great premium on learning the various aspects of English. The psycholinguistic circumstances of teaching and learning English in such contexts call for concerted pedagogical efforts to satisfy the expected learning outcomes. Thus, within the field of Computer-Assisted Language Learning (CALL), an objective of this paper is to identify the various technology that can assist the teaching, learning and improvement of the English pronunciation. The second objective is to illustrate with the PRAAT software the applicability of technology for the improvement of the English pronunciation. Structurally, the paper is divided into two parts: Part One identifies some technology generally applicable to language teaching, learning and improvement. It lists 35 types of technology that possess the propensity to improve the English pronunciation. This list is divided into four different categories, namely: Hardware (13: 1-13), Software (9, 14-22), Hardsoftware (5, 23-27), and Virtual Technology (9, 28-35). Some of these are summarised into four Plates (Plates 1-4). Part Two of the paper discusses PRAAT as one of the best and most current software for learning, teaching and improving oneself in the English pronunciation. It illustrates with 11 screen captures (Pictures 1-11) the viability and applicability of the software to language learning, using This is Babcock University, Ilishan. The paper recommends a simulation of the native-like environments offered by the Computer-Assisted Language Learning technology for the desired proficiency of language learning, at this instance, the English pronunciation. Full Text: PDF DOI: 10.15640/ijlc.v7n1a3
Academic Libraries for Commuter Students

"Academic Libraries for Commuter Students." Journal of Electronic Resources Librarianship, 31(3), p. 215
Expected Cost Analysis of Attack-Defense Trees

Attack-defense trees ( ) are an established formalism for assessing system security. We extend with costs and success probabilities of basic events. We design a framework to analyze the probability of a successful attack/defense, its expected cost, and its probability for a given maximum cost. On the conceptual level, we show that a proper analysis requires to model the problem using sequential decision making and non-tree structures, in contrast to classical analysis. On the technical level, we provide three algorithms: (i) reduction to PRISM-games, (ii) dedicated game solution utilizing the structure of the problem, and (iii) direct analysis of for certain settings. We demonstrate the framework and compare the solutions on several examples.
Linear Support Vector Machine (SVM) with Stochastic Gradient Descent (SGD) training and multinomial Nave Bayes (NB) in News Classification

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Benchmarking Author Recognition Systems for Forensic Application

This paper demonstrates how an author recognition system could be benchmarked, as a prerequisite for admission in court. The system used in the demonstration is the FEDERALES system, and the experimental data used were taken from the British National Corpus. The system was given several tasks, namely attributing a text sample to a specific text, verifying that a text sample was taken from a specific text, and verifying that a text sample was produced by a specific author. For the former two tasks, 1,099 texts with at least 10,000 words were used; for the latter 1,366 texts with known authors, which were verified against models for the 28 known authors for whom there were three or more texts. The experimental tasks were performed with different sampling methods (sequential samples or samples of concatenated random sentences), different sample sizes (1,000, 500, 250 or 125 words), varying amounts of training material (between 2 and 20 samples) and varying amounts of test material (1 or 3 samples). Under the best conditions, the system performed very well: with 7 training and 3 test samples of 1,000 words of randomly selected sentences, text attribution had an equal error rate of 0.06% and text verification an equal error rate of 1.3%; with 20 training and 3 test samples of 1,000 words of randomly selected sentences, author verification had an equal error rate of 7.5%. Under the worst conditions, with 2 training and 1 test sample of 125 words of sequential text, equal error rates for text attribution and text verification were 26.6% and 42.2%, and author verification did not perform better than chance. Furthermore, the quality degradation curves with slowly worsening conditions were not smooth, but contained steep drops. All in all, the results show the importance of having a benchmark which is as similar as possible to the actual court material for which the system is to be used, since the measured system quality differed greatly between evaluation scenarios and system degradation could not be predicted easily on the basis of the chosen scenario parameters.
Classifying Malaria Cell Images Dataset using Machine Learning Algorithms

In today's world, detecting malaria has become a very common phenomenon. The project we have selected is taken from the area of medical sector and Health organizations. A large number of people are affected by malaria. But all these people are not correctly predicted whether they are affected by malaria or not. Every year, we read about a number of cases where people died with malaria. The risk associated with making decision on predicting a person affected with malaria accurately is immense. So, the idea of this project is to gather data from multiple data sources and use machine learning algorithms on this data to extract important information and predict whether a person is affected with malaria or not.
A Network Delay Factor Model Based on the Hidden Markov Model and Latent Dirichlet Allocation

Network systems are widely used in today's digital communication systems and applications. Network delay is a very important factor affecting the efficiency and reliability of the system, and researchers often use a model to predict and understand the causes and patterns of delay. For our model, we propose beginning with the Hidden Markov Model (HMM) of the transmission network, which is used to analyze the network's operational situation and to predict the approximate state of the system into the future using the HMM prediction algorithm. Then, employing Latent Dirichlet Allocation (LDA), we put forward the Delay Factor Model (DFM) value for the delay. In the DFM, we need to map the delay's interval to an integer, designated as DII (Delay Interval Integer), and the factors returned define the hidden states of the HMM. From the view of the DFM, DII is generated from a factor and the previous DII randomly. We use the Gibbs Sampling approach to obtain an estimation of the DFM's parameters. By defining the HMM and DFM, we can forecast future delay with high accuracy, and the result can be shown to follow the peaks and troughs of the real operation of the network system's delay patterns.
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?

Natural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of them. The typical recipe involves: (i) training a deep, possibly bidirectional, neural network with an objective related to language modeling, for which training data is plentiful; and (ii) using the trained network to derive contextual representations that are far richer than standard linear word embeddings such as word2vec, and thus result in important gains. In this work, we wonder whether the opposite perspective is also true: can contextual representations trained for different NLP tasks improve language modeling itself? Since language models (LMs) are predominantly locally optimized, other NLP tasks may help them make better predictions based on the entire semantic fabric of a document. We test the performance of several types of pre-trained embeddings in neural LMs, and we investigate whether it is possible to make the LM more aware of global semantic information through embeddings pre-trained with a domain classification model. Initial experiments suggest that as long as the proper objective criterion is used during training, pre-trained embeddings are likely to be beneficial for neural language modeling.
Exploring Meta-analysis for Historical Corpus Linguistics Based on Linked Data

Empirical work on English historical corpus linguistics is plentiful but fragmented, and some of it is hard to come by. This paper proposes a solution for making it more accessible and reusable for meta-analysis. We present an online Language Change Database (LCD), which provides comparative, real-time baseline data from earlier corpus-based studies. LCD entries summarize the findings and include numerical data from the articles. We discuss the LCD from the perspective of database design and linked data management. Furthermore, we illustrate the reuse of LCD data through a meta-analysis of the history of English connectives. For this purpose, we have developed an application called the LCD Aggregated Data Analysis workbench (LADA). We show how researchers can use LADA to filter, refine and visualize LCD data. Thus we are paving the way for a future where both research results and research data are regularly available for verification, validation and re-use.
On the Use of Optimal Transportation Theory to Recode Variables and Application to Database Merging

Abstract Merging databases is a strategy of paramount interest especially in medical research. A common problem in this context comes from a variable which is not coded on the same scale in both databases we aim to merge. This paper considers the problem of finding a relevant way to recode the variable in order to merge these two databases. To address this issue, an algorithm, based on optimal transportation theory, is proposed. Optimal transportation theory gives us an application to map the measure associated with the variable in database A to the measure associated with the same variable in database B . To do so, a cost function has to be introduced and an allocation rule has to be defined. Such a function and such a rule is proposed involving the information contained in the covariates. In this paper, the method is compared to multiple imputation by chained equations and a statistical learning method and has demonstrated a better average accuracy in many situations. Applications on both simulated and real datasets show that the efficiency of the proposed merging algorithm depends on how the covariates are linked with the variable of interest.
Dewey Decimal Classification Trending Downward in U.S. Academic Libraries, but Unlikely to Disappear Completely

A Review of:&#x0D; Lund, B., &amp; Agbaji, D. (2018). Use of Dewey Decimal Classification by academic libraries in the United States. Cataloging and Classification Quarterly, 56(7), 653-661. https://doi.org/10.1080/01639374.2018.1517851 &#x0D; Abstract&#x0D; Objective – To determine the current use of Dewey Decimal Classification in academic libraries in the United States of America (U.S.).&#x0D; Design – Cross-sectional survey using a systematic sampling method.&#x0D; Setting – Online academic library catalogues in the U.S.&#x0D; Subjects – 3,973 academic library catalogues.&#x0D; Methods – The researchers identified 3,973 academic libraries affiliated with degree-granting post-secondary institutions in the U.S. The researchers searched each library’s online catalogue for 10 terms from a predetermined list. From the results of each search, the researchers selected at least five titles, noted the classification scheme used to classify each title, and coded the library as using Dewey Decimal Classification (DDC), Library of Congress Classification (LCC), both DDC and LCC, or other classification schemes.&#x0D; Based on the results of their data collection, the researchers calculated totals. The totals of this current study’s data collection were compared to statistics on DDC usage from two previous reports, one published in 1975 and one in 1996. The researchers performed statistical analyses to determine if there were any discernible trends from the earliest reported statistics through to the current study.&#x0D; Main Results – Collections classified using DDC were present in 717 libraries (18.9%). Adjusting for the increase in the number of academic libraries in the U.S. between 1975 and 2017, DDC usage in academic libraries has declined by 56% in that time frame. The number of libraries with only DDC in evidence is unreported.&#x0D; Conclusion – The previous four decades have seen a significant decrease in the use of DDC in U.S. academic libraries in favour of LCC; however, the rate at which DDC has disappeared from academic libraries has slowed dramatically since the 1960s. There is no clear indication that DDC will disappear from academic libraries completely.
Private Proximity Retrieval

A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance r from the user’s record x. The user’s privacy at each server is given by the fraction of the record x that is kept private. The distortion of a PPR scheme measures how accurately the user can calculate the identities of the desired files. We assume that each server stores a copy of the database. This paper studies protocols that offer trade-offs between perfect privacy and low computational complexity and storage.In this paper, this study is initiated. The work focuses on the case when the records are binary vectors together with the Hamming distance. In particular, for a given privacy level, we investigate the minimum number of servers that guarantee a prescribed distortion value. The collusions of pairs of servers as well as other distance measures are investigated.
EKSPLORASI BUKTI DIGITAL PADA SMART ROUTER MENGGUNAKAN METODE LIVE FORENSICS

Network devices as media file sharing and can be used as file servers have begun to appear, just as smart router devices can be used as file servers by adding USB Thumb drive as storage media. With the diversity of router devices, it becomes a challenge in digital forensic science when a case occurs by utilizing a smart router device related to file-sharing services. Then it is necessary to study the right method in investigating smart router devices. This paper discusses the use of the live forensics acquisition method in investigating smart router devices, against system log files related to file-sharing activities. In identifying the process of searching, recognizing, and documenting potential things as digital evidence of processing devices and digital media storage. The acquisition process uses two methods, namely the live acquisition method on the router device and physical acquisition on the device that is used as storage media on the smart router.
Explainable Similarity of Datasets Using Knowledge Graph

There is a large quantity of datasets available as Open Data on the Web. However, it is challenging for users to find datasets relevant to their needs, even though the datasets are registered in catalogs such as the European Data Portal. This is because the available metadata such as keywords or textual description is not descriptive enough. At the same time, datasets exist in various types of contexts not expressed in the metadata. These may include information about the dataset publisher, the legislation related to dataset publication, language and cultural specifics, etc. In this paper we introduce a similarity model for matching datasets. The model assumes an ontology/knowledge graph, such as Wikidata.org, that serves as a graph-based context to which individual datasets are mapped based on their metadata. A similarity of the datasets is then computed as an aggregation over paths among nodes in the graph. The proposed similarity aims at addressing the problem of explainability of similarity, i.e., providing the user a structured explanation of the match which, in a broader sense, is nowadays a hot topic in the field of artificial intelligence.
BlockChain Enabled Secure E-Voting System

E-VOTING is among the key public sectors that can be disrupted by blockchain technology. The idea in blockchainenabled e-voting (BEV) is simple. To utilise a digital-currency analogy, BEV issues each voter a "wallet" containing a user credential. A manual voting process is excessively time-consuming and takes a lot of steps to finish the vote. With rapid development of information technology, an electronic voting system (E-voting system) is more convenient to vote for someone from a list of candidates shown on a computer screen. However, security is a big concern for electronic voting. In this paper, we build a secure voting system using Paillier homomorphic encryption. Paillier homomorphic encryption is a kind of public key cryptography that has the homomorphic property that can be exploited to calculate the sum of votes without revealing to the system which vote is voted for which candidate. The voting system design has a simple interface that clients can use easily. Here, we contend that blockchain may address two of the most predominant worries in casting a ballot today: voter access and voter extortion. The thought is as per the following. Qualified voters cast a vote namelessly utilizing a PC or cell phone.BEV employs an encrypted key and tamperproof personal IDs. For instance, the mobile e-voting platform of the Boston-based startup Voatz employs smart biometrics and real-time ID verification. In this paper, we highlight some BEV implementations and the challenges.
Blockchain for Decentralized Data Analysis

Blockchain has established a following due to the strength of security it offers. There is a tradeoff in security but definitively establishing provenance of data in some cases can be considered more important. The research work described here is an overview of the early stage of work in the area of decentralized data analysis. In this work we describe the significant issues found in setting up the decentralized environment using blockchain to support data for subsequent analysis.
DIFFICULTIES ENCOUNTERED BY ENGLISH TEACHERS IN TEACHING VOCABULARIES

This study aims at identifying some difficulties encountered by English teachers in teaching vocabulary and finding out the solutions. The design used was qualitative descriptive using a semi-structured interview as the instrument. The study showed that common difficulties encountered in teaching vocabulary are students’ and teachers’ limited knowledge of the words, techniques, time constraint, and word selection. To overcome those difficulties, teachers may employ some techniques in teaching vocabulary such as using translation in L1, picture, real objects, gestures, guessing from the context, drilling, repetition, and using dictionary. Thus, it can be concluded that difficulties in teaching vocabulary lead English teachers to some ways they can take to overcome them. For future studies, it is suggested to employ observation to see the practice of vocabulary teaching and find out what the actual difficulties are encountered. Moreover, interview to students might be carried out to see their own difficulties in learning vocabulary.Keywords: English teaching, vocabulary, difficulties, techniques
The role of online written communication channels for reading-writing connection

This study developed an empirical model that could test reading-writing connection theory by examining the mediating role of online written communication channels. A set of hypotheses, drawn on a socio-cognitive theory of reading-writing connection, was developed to examine how reading-writing practices influenced literacy development through online written communication. Factor analysis was conducted to examine empirical relationships among the theoretical variables obtained from survey data. The results showed a significant role of online written communication in reinforcing the relationship between literacy practices and development. First, the individual and social practices of reading and writing had little impact on the development in both areas. Second, online written communication channels had a significant interaction effect on reading-writing practices both at the individual and social dimensions. Third, online written communication channels, such as SNS-based interaction using mobile devices, served as the mediating variable that empowered the relationship between reading-writing practices and development. The empirical results suggest that, to encourage L2 students toward a more integrative continuum of reading-writing practices, practitioners should incorporate online written communication channel into teaching practices. (Mokpo National University · Daegu University)