Reviewerly/exHarmony huggingface dataset:
Identifying Creative Content at the Page Level in the HathiTrust Digital Library Using Machine Learning Methods on Text and Image Features

Front-matter pages in a digitized book typically consist of mostly factual content that is not subject to copyright, and thus could potentially be opened to the public, even if the book itself is protected under copyright. However, the boundary of what is considered to be “front matter” is rather arbitrary, and some copyright-protected creative content can be found in the initial pages of a copyrighted volume. In this work, we conduct empirical research to evaluate machine learning approaches to detect creative content in the first 20 pages in a large sample of HathiTrust volumes. We start by analyzing different machine learning methods to distinguish creative from factual content using the statistically-expressed textual features from the HathiTrust Research Center’s Extracted Features dataset. From this experiment, we found that the random forest model had the best performance compared with logistic regression, support vector machine (SVM), or stochastic gradient descent (SGD) models. This experiment also reveals that textual data is not sufficient to reliably identify pages containing some kinds of creative content, e.g., images. Thus, we further trained an image detection model using YOLO-v3 to detect page types, thus creating an ensemble of textual and image features. Our findings show a promising result for the random-forest model trained on a combination of text and image features, increasing the accuracy from 85% to 89% compared with the model trained only on textual data.
High Visual-Quality Scenes in Low-Cost Virtual Reality With Collisions and Irregular Surfaces

Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics. Image-based rendering techniques arise as an alternative to solve this problem, but usually, the support of collisions and irregular surfaces (i.e., any surface that is not flat or even) represents a challenge. In this work, we present a technique suitable for both virtual and real-world environments that handle collisions and irregular surfaces for an image-based rendering technique in low-cost virtual reality. We also conducted a user evaluation for finding the distance between images that presents a realistic and natural experience by maximizing the perceived virtual presence and minimizing the cybersickness effects. The results prove the benefits of our technique for both virtual and real-world environments.
A Three-Year Analysis of Engineering Students’ Readiness for Remote Learning and Its Relevance to COVID-19

UNESCO is concerned about the impact of the COVID-19 pandemic on 1.5 billion learners worldwide. Future movers and shakers of our world are losing their valuable time due to the disruptions caused by this pandemic. Cannot they continue their learning activities from wherever they are? Technology provides teachers and students with several means to access a variety of educational resources. There has also been considerable growth in the adoption of technology within educational institutions in recent years. The world is standing on the threshold of the fourth industrial revolution, which is based on information and communication technologies. In keeping with this modern trend, data collection was done through the distribution of an online questionnaire among first-year undergraduates of the Faculty of Engineering at the University of Moratuwa (UoM) in Sri Lanka during 2017, 2018 and 2019. When preparing the online questionnaire, questions were taken from the Kirkwood & Price TEL Handbook. Results indicated that all the respondents were computer literate and of them, 80% owned computers at home. Therefore, higher educational institutions can arrange to take advantage of these capabilities to enhance the students' learning by encouraging them to adopt collaborative learning methods. Further, this study shows that more than 95% of them access the Internet by using a smartphone, and as such, higher education institutions can arrange to exploit mobile technologies for engineering education, thereby delivering the maximum benefits to students. Based on the findings of this study, it does make sense to conclude that adopting meaningful technological interventions could result in significant and positive changes in the teaching and learning practices at UoM.
A robust approach for people counting in dense crowd images using deep neural networks

Abstract People counting in dense crowd images with deep neural networks are proved to be effective. The models like RCNN are able to predict the crowd count by head detection, using the CNN and selective search algorithm, but these approaches are very slow, as they involve computing convolutional operation for 2k regional proposals, involving no shared computations, besides the selective search algorithm itself is slow. In this approach a Faster R-CNN for head detection which uses a Regional Proposal Network (RPN) has been used. The region proposal network is a Fully Convolutional Neural Networks that generates region proposals, these regional proposal were fed in to RoI pooling layer and subsequently classified and localized, Thus Faster-RCNN reduces computation cost of convolutional operations by passing a image only once, sharing the convolutional operations and also using RPN for regional Proposals. Using the Faster R-CNN MAE and MSE are reduced compared to R-CNN.
Improving Thai Word Segmentation using HMM: A Case Study of Sentiment Analysis

Word segmentation is a basic problem in the natural language processing of non-boundary delimiters language, especially for the Thai language. The ambiguity of the boundaries of the words in the sentence is one of the significant problems that can cause an unknown word and affects the word segmentation accuracy. This paper presents an improving Thai word segmentation using Hidden Markov Model to cope with an unknown word problem. The five-state of left-to-right HMMs are built according to the classes of the unknown word by applied the parts of speech of the Thai language as the observation symbols of the model. To determine the unknown word in the sentence, the String Matching algorithm is first implemented to find overlapping words and unknown words. The unknown words that unidentified by the lexical dictionary are classified according to their classes by the HMMs. Then the word combining rules are applied to determine the proper word boundary and to merge possible characters into words. In addition, the sentiment analysis task of polarity detection was selected as a case study to verify the accuracy of the proposed method. The precision, recall, and F-measure are used for evaluating the efficiency of the proposed method. The empirical results show that both segmented words and polarity classification results obtained by the proposed method tend to outperform the existing methods.
Arabic Sentiment Analysis Problems and Challenges

Arabic Sentiment Analysis (ASA) has obtained a great interest from the research community with the increasing interest in social media. This is thanks to a large variety of helpful applications that can be derived from analyzing sentiments. Although there are various tools that were created to select opinions, they can be only utilized for other languages. This presents the necessity for providing more attention to ASA research domain. For that reason, this detailed study tries to explore the problems and challenges of analyzing Arabic sentiments contents. This detailed study can be considered as a valuable step toward defining great solutions for ASA research field. In addition, presenting these problems and challenges may encourage researchers to participate more in this research field, make more improvements and gain the great results in various domains especially in the field of education.
Ensemble Analysis of the Students Length of Study at University of Klabat Manado Indonesia

Abstract The purpose of this study is to classify the student’s length of study based on the status of graduating on time or not on time based on several independent variables observed, namely gender, Grade Point Average (GPA), place of residence, type of parents occupation and school origin. The statistics used in this study is non-parametric statistics with a classification analysis method. The classification analysis is to find a training set model of the training set that distinguishes records into appropriate categories or classes. The method used is classification using ensemble techniques. The basic principle of the ensemble method is to develop a set of models from training data and combine a set of models to determine the final classification. The final classification is based on the largest collection of votes from a combination of a set of models. To get the best combination of models, the ensemble method enables the use of several different classification models. The ensemble method used in this study is Bagging and Boosting.
Prediction the Probability of Purchases Recommended Items

This paper discusses various methods for improving recommendation systems. A comparative analysis of two models for solving classification problems is performed: random forest and CatBoostClassifier. The research was performed on the data of the purchase history of Ozon customers. Standard methods that are often used in recommendation systems were used. We implemented collaborative filtering methods, cosine similarity of products from customer views per site visit, and similarity of text data. To evaluate the results, we used special metrics that evaluate the quality of predictions of the first k objects from the recommendations: Mean average precision (map@K) and Recall at K (recall@k). When generating additional features based on various methods that reveal the similarity of objects, an increase in the quality of model forecasts is noted. The CatBoostClassifier model showed the best results.
LigADOS: Interlinking Datasets in Open Data Portal Platforms on the Semantic Web

The fostering of data opening has been largely motivated by sets of laws on access to information, which establish the need to make data related to governmental activities available to citizens and society in general, as well as results from business processes or scientific research, also for accountability and transparency. There are several ways of making data available to the public, from a simple website to sophisticated applications for accessing the data. In this context, one of the options is the construction of an open data portal using platforms for data repositories and catalogs. In the last few years, there has been a rapid proliferation of this type of portals, with domain or organization specific datasets being widely disseminated in platforms like CKAN. In these platforms, datasets are organized in thematic groups and described by keywords and other attributes assigned by the publisher. Usually described by metadata with poor semantics, these datasets very often remain as “data silos”, with no explicit connection or data integration mechanism, making it difficult for the users to locate and interrelate relevant data sources. In contrast, the Semantic Web focuses on a way of modeling and representing data in an easier manner to establish interrelationships between data, accompanied by richer descriptors. Based on this scenario, this paper proposes LigADOS, an approach to create interconnections between datasets considering their content and related metadata. LigADOS is based on the principles of the Semantic Web and associated linked data solutions and technologies, to support rich access strategies to RDF data published using portal platforms like CKAN and others.
Inclusive Genetic Programming

The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima. This paper aims to further investigate and prove the efficacy of a GP heuristic proposed in a previous work: the Inclusive Genetic Programming (IGP). Such heuristic can be classified as a niching technique, which performs the evolutionary operations like crossover, mutation and selection by considering the individuals belonging to different niches in order to maintain and exploit a certain degree of diversity in the population, instead of evolving the niches separately to find different local optima. A comparison between a standard formulation of GP and the IGP is carried out on nine different benchmarks coming from synthetic and real world data. The obtained results highlight how the greater diversity in the population, measured in terms of entropy, leads to better results on both training and test data, showing that an improvement on the generalization capabilities is also achieved.
A Novel Deep Learning Character-Level Solution to Detect Language and Printing Style from a Bilingual Scanned Document

Bangla is one of the world's most widely-spoken languages, but few languages (or "script") automation solutions have been reported for it. To build an OCR system, it is very important to detect the language and type of printing style to run specific character recognition and segmentation modules. This paper presents a novel solution to automatically detect the language (Bangla vs English in terms of the script), and printing style (printed vs handwritten) from any given bilingual scanned document using multiple deep learning models.
The Study of Graph Convolutional Network for Activity Recognition

Abstract GCN is an excellent single-modal machine learning model for processing non-Euclidean data, such as skeleton data. Conventional GCN only uses single-modal data as its input. When we have multiple modal data, the application of GCN will be limited. To utilize multi-modal data advantages, we propose a method to combine GCN and LSTM to extract skeleton feature and acceleration feature, respectively. To test our method, we did a lot of experiments on Cooking Activity Dataset.
Differentially Private Frequency Sketches for Intermittent Queries on Large Data Streams

We propose novel and differentially private versions of Count Sketch, particularly suited for dynamic, intermittent queries for observed frequencies of elements in a universal set. Our algorithms are designed for scenarios where the queries are made intermittently, that is, at different times during the course of the data stream. We explore several approaches, all based on the Laplace mechanism, and ultimately propose an algorithm that is robust and efficiently handles multiple queries at multiple times while keeping its utility at reasonable levels. We demonstrate the performance of the proposed algorithm in various scenarios with a numerical example.
Pro-ISIS Tweets Analysis Using Machine Learning Techniques

The spread of violent extremism and propaganda is a critical threat both nationally and globally. With the ever-increasing popularity and use of social media, spreading this extremism has never been easier for terrorist organizations and their followers. One terrorist organization, in particular, ISIS (the Islamic State of Iraq and Syria), uses Twitter for the vast majority of their social media interaction. It is crucial to have cyber analytics tools developed to combat these extremists' online presence and influence on social media platforms, such as Twitter. In this research, we apply machine learning algorithms to understand popular ISIS supporters' behavior and techniques and their possible influence on other users. We collected and analyzed a dataset containing over seventeen thousand tweets posted by pro-ISIS Twitterers. We utilized three machine learning algorithms with several models/settings in an attempt to classify and predict whether the top 4 pro-ISIS Twitter users (the most followed and tweeted users) authored a specific tweet. The algorithms applied in this work include sequential neural networks, random forests, and XGBoost. The models were ensembled, timed, and one model was simplified to attempt to improve performance and runtime.
Linking Author Information: EconBiz Author Profiles

Author name ambiguity represents a real obstacle in the world of digital library (DL) information retrieval. The search with an author’s name almost always casts doubt on whether all publications in the result list belong to that author or another author sharing the same name. In several other cases, the scholar is interested in having additional information about a selected author, such as a short biography, affiliations, metrics, or co-relations with other authors. The main purpose of this work is the integration and usage of diverse data, based on Linked Data approaches and authority records, to create a comprehensive author profile inside a DL. We are proposing and deploying an approach that provides such author profiles by using the available data, on-the-fly, i.e., harvesting the available sources for this purpose. The proposed approach - developed as a fully functional prototype - has been introduced for evaluation to a group of authors, scholars, and librarians. The results indicate acceptance of such an approach, underlining the benefits and limitations that come with it.
Toward Context-Relevant Library Makerspaces: Understanding the Goals, Approaches, and Resources of Small-Town and Rural Libraries

While best practices for developing makerspaces in public libraries exist, there is scarce literature that describes how they apply to small-town and rural libraries in alignment with the libraries’ existing assets, practices, and constraints. This paper aims to explore the small-town and rural libraries’ goals, approaches, and existing resources towards establishing a future makerspace and investigate the extent to which these elements support or hinder the design of the makerspace or maker programming. From the qualitative analysis of cultural probes and interview data with nine librarians, this paper demonstrates two ways that small-town and rural libraries differed from the best practices in the field: a) focusing on attendance and equipping the materials within the makerspace over community building, b) lack of transfer of existing assets and practices to maker programming. Study findings suggest small-town and rural librarians’ lack of STEM competencies and knowledge around makerspaces as a critical barrier for applying their existing assets and practices to a new area of maker programming. Our study proposes context-specific recommendations and directions for small-town and rural libraries to design and develop makerspaces.
Expanding Language Development In Preschool Age Children

Abstract. Important language knowledge taproot during school. Preschool Classroom curriculum can help with language development through various exploration activities, activities that are guided by the teacher, and routine activities. Teachers ' understanding of the potential to expand language development in all the preschool curriculum priests provides many opportunities for children to see the language being used. Specific teaching strategies that are effective in supporting language development include asking questions, buffering language, mediation, and conflict resolution. Activities that combine books, poetry and songs encourage children to develop receptive and expressive knowledge in written language and to develop their receptive and expressive knowledge in spoken language. Keywords: Development; language; preschoolers
Producing Web Content Within Platform/Infrastructure Hybrids

While web content management systems (CMSs) play important roles in shaping web content, they have received very little attention from scholars working in information studies or related fields concerned with the intersection of society and technology. This paper first situates CMSs within an emerging discussion of platform/infrastructure hybrids. It then presents a limited case study of how the concept of structured content is implemented within the social and technical constraints of the widely used Wordpress CMS, indicating ways that the study of CMSs can contribute to theory on platform/infrastructure hybrids.
Low Cost Autonomous Driving Architecture using Ultrasonic Sensors and Camera

Abstract The existing autonomous vehicles cost are very high due to inclusive of various range of LIDAR, RADAR and multiple cameras for autonomous features. The proposed autonomous vehicles can operate at low speed with limited sensors which will cut the cost by half. The purpose of this research is to make an auto driven platform, which can be ported in any electric vehicles. The prototype also has features including a safety system, control panel, route recognition, android device assistance, Wi-Fi, &amp; battery updates. Our study findings are carefully analysed. This leads to less emissions of the electric vehicle powered by man.
Method for Product Selection Support Based on Reliability of Features in Reviews

When users selecting products on e-commerce sites, they always use reviews to guide their purchasing decisions. However, if several reviews are posted below one product, it might become difficult for users to select products efficiently. In this study, we propose two methods that use natural language processing technology, such as Word2vec and TF-IDF, to help users judge the reliability of features described in reviews effectively and efficiently. One method uses word embeddings and another method uses sentence embeddings. Finally, we evaluate the results of the two methods and find that the sentence embeddings method seams have a better performance.
Multiple comparisons: a tutorial. Part 2: Understanding multiple comparisons

Data sharing not applicable to this article as no datasets were generated or analysed during the current study. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Possible Instant Messaging Malware Attack Using Unicode Right-To-Left Override

The right-to-left special Unicode character has a legitimate use for languages that are transcribed in a right-to-left direction or in an environment that combines both right-to-left and left-to-right languages, like web pages, emails, desktop documents and text messages. These writing systems include right-to-left languages such as Persian, Arabic and Hebrew. The “right-to-left” attacks have been used for many years for malicious purposes, mostly in email communications. Early in 2018, Kaspersky Lab published an article describing a vulnerability in the Windows client of the popular instant messenger Telegram. This vulnerability uses the Unicode “right-to-left” character to obfuscate the name of the malware file. In our paper, we shall describe a possible attack that we discovered and that uses a combination of the “right-to-left” override attack and instant messaging malware attack and presents a realistic threat for another widely used messenger—Microsoft’s Skype for Linux. Our purpose for conducting this research was to describe an exploit that we discovered and to warn the people who use this communication application about it, as well as to appeal to the producer for fixing it. Additionally, it is important to emphasize that the attack scenario developed by us also impacts other applications that allow file transfer (e.g. e-mail clients) and run on Linux systems with Wine installed.