Reviewerly/exHarmony huggingface dataset:
Modeling Students’ Perceived Self-efficacy and Importance toward Core Ideas and Work and Life Skills in Science Education

Developing student self-efficacy and conceptualising the importance of core ideas are seen as important learning areas in science education. Such core ideas can be elucidated with respect to the science discipline as well as interdisciplinary learning. &nbsp;Schools can be expected to promote high levels of students’ self-efficacy in acquiring major disciplinary ideas and guiding students towards recognising the importance of gaining work and life skills. This research sought to develop models describing students’ gains in perceived self-efficacy and the importance of work and life skills as well as indicating the relationship with both disciplinary and interdisciplinary core ideas in science education. Using a validated instrument, data was collected from grade 8 (N=218) (14-15 years old) and grade 11 (N=95) (17-18 years old) students. Models were created based on confirmatory factor analysis and parallel analysis. This research shows that there is need to make changes in secondary school studies so as to more clearly recognise a framework for both disciplinary and interdisciplinary core ideas, shown to be important for enabling students to prepare for their future life and careers. Key words: disciplinary core ideas, interdisciplinary core ideas, modelling, perceived importance, perceived self-efficacy, work and life skills
MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types

Heterogeneous information network (HIN) embedding is to encode network structure into node representations with the heterogeneous semantics of different node and edge types considered. However, since each HIN may have a unique nature, e.g., a unique set of node and edge types, a model designed for one type of networks may not be applicable to or effective on another type. In this article, we thus attempt to propose a framework for HINs with arbitrary number of node and edge types. The proposed framework constructs a novel mixture-split representation of an HIN, and hence is named as MixSp. The mixture sub-representation and the split sub-representation serve as two different views of the network. Compared with existing models which only learn from the original view, MixSp thus may exploit more comprehensive information. Node representations in each view are learned by embedding the respective network structure. Moreover, the node representations are further refined through cross-view co-regularization. The framework is instantiated in three models which differ from each other in the co-regularization. Extensive experiments on three real-world datasets show MixSp outperforms several recent models in both node classification and link prediction tasks even though MixSp is not designed for a particular type of HINs.
Coherence measures based on sandwiched Rényi relative entropy*

Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory. Baumgratz, Cramer and Plenio established a rigorous framework (BCP framework) for quantifying coherence [Baumgratz T, Cramer M and Plenio M B Phys. Rev. Lett. 113 140401 (2014)]. In the present paper, under the BCP framework we provide two classes of coherence measures based on the sandwiched Rényi relative entropy. We also prove that we cannot get a new coherence measure f ( C (·)) by a function f acting on a given coherence measure C .
Incremental Association Rule Mining Based on Matrix Compression for Edge Computing

A growing amount of data is being generated, communicated and processed at the edge nodes of cloud systems; this has the potential to improve response times and thus reduce communication bandwidth. We found that traditional static association rule mining cannot solve certain real-world problems with dynamically changing data. Incremental association rule mining algorithms have been studied. This paper combines the fast update pruning (FUP) algorithm with a compressed Boolean matrix and proposes a new incremental association rule mining algorithm, named the FUP algorithm based on a compression matrix (FBCM). This algorithm requires only a single scan of both the database and incremental databases, establishes two compressible Boolean matrices, and applies association rule mining to those matrices. The FBCM algorithm effectively improves the computational efficiency of incremental association rule mining and hence is suitable for knowledge discovery in the edge nodes of cloud systems.
Pitfalls Analyzer: Quality Control for Model-Driven Data Science Pipelines

Data science pipelines are a sequence of data processing steps that aim to derive knowledge and insights from raw data. Data science pipeline tools simplify the creation and automation of data science pipelines by providing reusable building blocks that users can drag and drop into their pipelines. Such a graphical, model-driven approach enables users with limited data science expertise to create complex pipelines. However, recent studies show that there exist several data science pitfalls that can yield spurious results and, consequently, misleading insights. Yet, none of the popular pipeline tools have built-in quality control measures to detect these pitfalls. Therefore, in this paper, we propose an approach called Pitfalls Analyzer to detect common pitfalls in data science pipelines. As a proof-of-concept, we implemented a prototype of the Pitfalls Analyzer for KNIME, which is one of the most popular data science pipeline tools. Our prototype is model-driven, since the detection of pitfalls is accomplished using pipelines that were created with KNIME building blocks. To showcase the effectiveness of our approach, we run our prototype on 11 pipelines that were created by KNIME experts for 3 Internet-of-Things (IoT) projects. The results indicate that our prototype flags all and only those instances of the pitfalls that we were able to flag while manually inspecting the pipelines.
Using Syntax to Resolve NPE in English

This paper describes a novel, syntax-based system for automatic detection and resolution of Noun Phrase Ellipsis (NPE) in English.The system takes in free input English text, detects the site of nominal elision, and if present, selects potential antecedent candidates.The rules are built using the syntactic information on ellipsis and its antecedent discussed in previous theoretical linguistics literature on NPE.Additionally, we prepare a curated dataset of 337 sentences from wellknown, reliable sources, containing positive and negative samples of NPE.We split this dataset into two parts, and use one part to refine our rules and the other to test the performance of our final system.We get an F1-score of 76.47% for detection and 70.27% for NPE resolution on the testset.To the best of our knowledge, ours is the first system that detects and resolves NPE in English.The curated dataset used for this task, albeit small, covers a wide variety of NPE cases and will be made public for future work.
Advanced Gravitational Search Algorithm with Modified Exploitation Strategy

Gravitational search algorithm (GSA) is a novel technique as compared to other heuristic methods and depends pon the gravitational forces between masses. It showed better performance in terms of convergence but has slow exploitation ability due to the fitness function effect on masses; they are getting heavier after every iteration. Therefore, masses are getting closer to each other and nullify the gravitational forces on each other avoiding them from swiftly exploiting the optimum. In order to solve this problem in this paper, an advanced gravitational search algorithm (AGSA) with modified exploitation strategy is proposed. The reason for the modification is that the agents will reach the optimum point swiftly and the convergence is much faster as compared to the standard and other improved versions of GSA available in the literature. AGSA is also compared with the standard and modified Particle Swarm optimization algorithm in this paper. Five benchmark functions have been implemented to assess the efficiency of the presented algorithm. In addition, a standard, constrained, design problem of a pressure vessel design is also used to examine the efficiency of the proposed technique. Simulation results empirically validated that the presented algorithm has remarkably better results in accordance with convergence and solution stability when compared to the other methods.
Feasibility of quantum key distribution with macroscopically bright coherent light

We address feasibility of continuous-variable quantum key distribution using bright multimode coherent states of light and homodyne detection. We experimentally verify the possibility to properly select signal modes by matching them with the local oscillator and this way to decrease the quadrature noise concerned with unmatched bright modes. We apply the results to theoretically predict the performance of continuous-variable quantum key distribution scheme using multimode coherent states in scenarios where modulation is applied either to all the modes or only to the matched ones, and confirm that the protocol is feasible at high overall brightness. Our results open the pathway towards full-scale implementation of quantum key distribution using bright light, thus bringing quantum communication closer to classical optics.
Author Correction: Modeling of systematic errors in stereo-digital image correlation due to camera self-heating

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Forward and Backward Knowledge Transfer for Sentiment Classification

This paper studies the problem of learning a sequence of sentiment classification tasks. The learned knowledge from each task is retained and used to help future or subsequent task learning. This learning paradigm is called Lifelong Learning (LL). However, existing LL methods either only transfer knowledge forward to help future learning and do not go back to improve the model of a previous task or require the training data of the previous task to retrain its model to exploit backward/reverse knowledge transfer. This paper studies reverse knowledge transfer of LL in the context of naive Bayesian (NB) classification. It aims to improve the model of a previous task by leveraging future knowledge without retraining using its training data. This is done by exploiting a key characteristic of the generative model of NB. That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks. Experimental results show that the proposed method markedly outperforms existing LL baselines.
Searching for Fiction for Pleasure Reading: Current Research and Recommendations

One of the major tasks library patrons are faced with is searching for a good book to read, particularly when they have no specific book in mind. As such, people faced with this task search in places such as online catalogs, looking at existing book reviews and descriptions, and they also ask librarians and other readers’ advisors for assistance in finding book recommendations. This paper attempts to review the research on how people conduct searches for pleasure reading, as well as research for online catalog design in order to inform the search process, leading to recommendations for future work in catalog design and readers’ advisory driven by the application of human factors principles in the search process.
Young People, Smartphones, and Invisible Illiteracies

There has been considerable euphoria surrounding smartphones and their ability to make mobile internet access a reality for previously disconnected communities, offering them exciting possibilities in terms of communication, education, health and consumer services. However, is this optimism warranted or is there a chasm between the potentiality inherent in smartphones and the actual ways in which they are deployed by individual adopters? In this research on underprivileged youths in Singapore, a country with affordable and widespread internet access, we find growing evidence of young people who are ostensibly connected to the internet, but whose online repertoire is severely limited. Eschewing laptops and computers in favor of smartphones, these young people's internet use is largely confined to communication apps such as WhatsApp, Snapchat and Instagram, and entertainment apps such as YouTube, Dubsmash and Musical.ly, with minimal exploration of the World Wide Web’s rich offerings. While statistical measures would classify these youths as internet users, their app-centric navigation of the online space results in a narrow use of the internet that does not fully optimize the medium’s affordances for learning, participating, creating and produsing. To reverse this trend of invisible illiteracies, media literacy education in mobile-only or mobile-centric media environments needs to be urgently refined to better prepare young people for the full complement of online opportunities.
Sentiment analysis and relationship between social media and stock market: pantip.com and SET

Abstract This research constructs a process flow of social media sentiment analysis and explains relationship between comments on social media and stock. One of the popular social media for Stock Exchange of Thailand investors is Pantip.com, a website providing service as a webboard with tagging feature. In this research, all posts tagged by ‘Stock’ on this website were crawled to files. Then comments were scraped and cleaned. For model training, some comments were labelled into three classes; positive, negative and neutral. Sentiment analysis model was constructed by Naive Bayes Classification technique. In evaluation, the model shown that it performed 74% accuracy. This model was utilised to classify comments into sentiments. When all comments were completely classed, sentiment types were counted by date. Finally, correlation matrices were constructed to find relationship between number of sentiments and stock. The research found that number of sentiments from social media relate to ADVANC and CPALL stock volumes. Moreover, the correlation always reaches to the peak on trading day then it gradually declines with the magnitude depending on the day length after trading day.
ANNOTATING DATA WITH MULTIDIMENSIONAL PROPERTIES

The evolution of web technologies and the data we are manipulating announce profound changes on Business Intelligence (BI) systems and open up important researches and innovations particularly in multidimensional data modeling and data integration. The emergence of the semantic Web highlights the need of including external data sources in the BI system. The semantic web came with Resource Description Framework (RDF) model to describe data over the Web by annotating resources with semantics and properties and consequently establishing reasoning mechanisms. However, integrating and/or analyzing information from Wide World Sources still a very challenging process because of their “unpredictability” and heterogeneity. Consequently, the transition to an open BI/SW system is required to handle automatic alteration on structures and enabling discovery of multidimensional entities over multiple Web sources. In this paper, we introduce our prospective approach and architecture for including external data sources in an open BI/SW system and we provide an automatic method aimed to define multidimensional entities and properties over different sources for data acquisition and data analysis requests.
Kinect-based human finger tracking method for natural haptic rendering

In the multi-modal natural human-computer interaction (HCI), real-time finger position detection with high accuracy becomes an important basis for interactive modeling of the virtual environment. In this paper, a novel fingertip auto-positioning (FAP) detection method is proposed for tracking finger position with a Microsoft Kinect sensor. Based on the finger skeleton point obtained through the Kinect SDK (Software Development Kit), skeleton point correction and fingertip auto-positioning are realized with the pixels circle which covers the fingertip area within a certain threshold. The experimental results show that the average AMPE (Absolute Mean Percentage Error) of the proposed finger position detection method are 1.47%, 1.62% and 0.80% respectively in the direction of X, Y and Z axes while its execution rate is 23 Hz. The proposed method can be applied to haptic based virtual reality applications and provide required position information for haptic modeling.
Scale specified single shot multibox detector

Detecting objects at vastly different scales is a fundamental challenge in computer vision. To solve this, some approaches (e.g. TridentNet) investigate the effect of receptive fields, whereas other approaches (e.g. SNIP, SNIPER) are based on the image pyramid strategy. In this study, a novel single‐shot based detector, called scale specified single‐shot multibox detector (4SD) is proposed. It aims to predict objects of a specific scale range separately by using feature maps of different sizes. First, a parallel multi‐branch architecture with feature maps of different sizes is generated by scale specific inference module. Then, the authors propose a scale specific training scheme to specialise each branch by sampling object instances of proper scales for training. Results are shown on both PASCAL VOC and COCO detection. The proposed method can achieve a mean average precision of 83.1% on PASCAL VOC 2007, and 36.9% on MS‐COCO at a speed of 28 frames per second, which is superior to most single‐stage detectors.
The Role of Virtual Reality in Enhancing Students’ Learning

The globalization, innovation, and rapid spread of advanced technologies in different fields, including education, are a recent trend. Virtual reality (VR) is one emerging technology that has been used in recent years in medical training, military training, and other fields. It also presents several opportunities in the educational sector to support teaching and learning processes and thus deserves greater attention from educators and policy makers. The present paper reviews the huge role of Virtual Reality in supporting and enhancing students’ learning in four main areas: collaborative/cooperative learning, creative learning, problem-based learning, and role playing. Different types and categories of VR are also explored. Finally, some of the limitations associated with the implementation of VR, including its high cost, and possible solutions are discussed. Overall, VR presents several unique opportunities for enhancing teaching and students’ learning.
A Proposed Course Recommender Model based on Collaborative Filtering for Course Registration

Students face issues and challenges in making decisions for course registration.Traditionally, students rely on suggestions from academic advisers prior to course registration.Therefore, students spend a considerable amount of time waiting for advisers to help them register for the right subjects.However, the number of students rises yearly, thereby increasing the responsibilities of lecturers.Moreover, academic advisers experience constraints in analysing data during consultations for course registration.Therefore, this study proposes a course recommender model based on collaborative filtering.Collaborative filtering is adopted because it provides recommendations based on students' performance in previous subjects.A dataset from the Information & Communication Technology Centre (ICT) of the University Malaysia Pahang is used to evaluate the proposed model.The evaluation is conducted based on two experiments.The first experiment is performed by calculating the difference between actual and predicted scores to verify prediction accuracy.Results show that the average of the mean absolute error of the proposed model is 0.319, which is highly accurate.The second experiment is conducted by comparing the recommendations of the proposed model with those of experts to validate the course recommendation accuracy of the proposed model.Results of the second experiment show that the proposed model has a 91.06% accuracy rate with an error rate of 8.94%.In addition, average precision is 0.68 and recall is 0.724, which are considered accurate.Therefore, the proposed model can play a vital role in assisting students and academic advisers to recommend the right courses during registration, thereby overcoming the limitations of academic advising.
A modified Matrices Approach in Advanced Encryption Standard Algorithm

The cryptographic algorithms became the main proceeding for protection of very important data from unauthorized access.There are several cryptographic algorithms to ensure the data, but algorithms must be selected according to speed, strength and the implementation.Thus, choosing the advance encryption standard (AES) for encryption and decryption data because its speed and strength of encryption, flexible, complex processing and its resistance to Brute-force attack.This paper presents enhancement of the AES algorithm to increase the security of the encrypted documents by using different sizes data matrices based on multiple irreducible polynomials with order 2, 4, and 8.The proposed modifications results tested and provide a high randomness.
The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm

Graduating on time is one element of higher education accreditation assessment. In the Strata 1 level, students are declared to graduate on time if they can complete their studies &lt;= eight semesters or four years. BAN-PT sets a timely graduation standard of &gt;= 50%. If the standard is not met, it will reduce the value of accreditation. These problems encourage the Universitas Simalungun Pematangsiantar to conduct evaluations and strategic steps in an effort to increase student graduation rates so that the targets of BAN-PT can be achieved. For this reason it is necessary to know in advance the pattern of students who tend not to graduate on time. In this study, C4.5 Algorithm is proposed to predict student graduation. This algorithm will process student profile datasets totaling 150 data. This dataset has a graduation status label. The value of the label is categorical, that is, right and late. The features or attributes used, namely the name of the student, gender, student status, GPA. The results of the C4.5 algorithm are in the form of a decision tree model that is very easy to analyze. In fact, even by ordinary people. This model will map the patterns of students who have the potential to graduate on time and late.