Reviewerly/exHarmony huggingface dataset:
An epistemological assessment of learning nutritional information with augmented reality

Purpose This study aims to investigate whether epistemological assessment is a suitable approach to evaluate students’ learning of dietary knowledge via the use of an augmented reality (AR) information system. Students’ perceived dietary knowledge was compared before and after learning with the AR system. Two major questions were addressed: Did students improve their understanding of dietary knowledge after the use of AR information system? Did students gain more appropriate understanding of dietary knowledge after the use of AR information system? Design/methodology/approach A mixed-methods approach was used in the study. The mobile AR system was used among 65 volunteered non-nutrition-major college students recruited in campus. For promoting epistemological development of personal dietary knowledge, students practiced with life experiences to access daily dietary information. Pre- and post-tests of students’ understanding of dietary knowledge were compared. Interviews with 20 students were used for gathering in-depth research data to analyse students’ epistemological understanding of dietary knowledge. Findings The epistemological assessment indicated an improvement in learning after the use of the AR system. Students gradually gained awareness of dietary knowledge and changed their perceptions of their dietary behaviours. Epistemological approaches to the analysis of students’ conceptual change in dietary knowledge revealed a significant increase in the mean nutritional concepts ( p &lt; 0.01) and a decrease in their mean misconceptions ( p &lt; 0.001) after learning via the mobile nutrition monitoring system. Learning assessment of 65 students also indicated a significant increase from the post-test after learning with the system ( p &lt; 0.0001). Research limitations/implications This study might have its limitations, as it only assessed learning using a pretest-posttest design for a specific learning context over a short period of learning time. The use of interviews based on the epistemological approach might have its limitations in the interpretations of the phenomenon. Future implementations can also be extended to different populations to promote self-monitoring dietary behaviours. Originality/value The findings of this study will contribute to the application of AR in learning about dietary knowledge. The research involving in-depth observation of students’ learning relevant to personal nutritional information needs via mobile AR might provide potential contributions to dietitian professionals and health education.
USE OF SELECTED MOBILE TECHNOLOGIES AT LOWER-SECONDARY SCHOOL

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 4234-4238ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1060Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
DEVELOPMENT OF THE STUDENTS’ COMPUTATIONAL THINKING SKILLS WITH PROJECT-BASED LEARNING IN SCRATCH PROGRAMMING ENVIRONMENT

DEVELOPMENT OF THE STUDENTS’ COMPUTATIONAL THINKING SKILLS WITH PROJECT-BASED LEARNING IN SCRATCH PROGRAMMING ENVIRONMENT
Image Captioning with Two Cascaded Agents

Recent neural models on image captioning usually take a encoder-decoder fashion, where the decoder predicts a single word at one step recently with the encoder providing information. The encoder is a pretrained CNN model typically. Thus the decoder, the input to it, and the output from it become the most important parts of a model. We propose a pipelined image captioning framework consisting of two cascaded agents. The former is named as semantic adaptive agent which generates the input to the decoder by consulting the information from the current decoding process, and the latter as caption generating agent which select a single word of the vocabulary as the output of the decoder by taking consideration of the input and the current states of the decoder. For the framework of two cascaded agents, we design a multi-stage training procedure to train the two agents with different objectives by fully utilizing reinforcement learning. In experiments, we conduct quantitative and qualitative analysis on MS COCO dataset and our results can significantly outperform baseline methods in terms of several evaluation metrics.
The combination of Viola-Jones and Eigen faces Algorithm for Account Identification for Diploma

This study aims to identify the account of the diploma/certificate owner in way of face recognition. The process of detecting the face image of diploma/certificate owner is using the Viola-Jones Method. The process is comparing existing face images in database using the Eigen face Algorithm. The trial material in the form of test data is totaling 10 people to identify ownership of data from a diploma/certificate. The test result using the Viola-Jones method and Eigen face algorithm obtained an accuracy value of 70%. The system can identify and recognize facial image data with the provisions of the face position facing to front of the camera, evenly distributed light intensity, and the distance between the face and computer camera ± 35 cm.
INFRA-CAMBRIAN PLACER GOLD-URANIFEROUS PALEOZOIC SEDIMENTS, SOUTHWESTERN SINAI, EGYPT

The present paper studied the stratigraphic succession of Infra-Cambrian Taba Formation overlainby Sarbit El Khadim, Abu Hamata, Adedia and Um Bogma formations. The distribution of Taba Formationat the study area comprises, Khaboba, Gabal Adedia, El Ferah, El Marahil, Gabal Hemeiyir and Ma’einlocalities. Gold is recoded mineralogically and geochemically at Khaboba locality. Also the gold isrecorded at the sandstone of Sarabit El khadim (4.5ppm) and the dolostone of Um Bogma Formation(9.7ppm). The radiometric survey recorded thorium in Taba Formation up to 700 ppm. Also, thorium ispredominant in sandstone of Adedia Formation at Ras El Homara and Syniea. The chemical analysesrecorded uranium up to 1000 ppm in sandstone of Adedia Formation at Sarbit El Khadim locality(Temple) for the first time.
Real walking on a virtual campus

The rapid development of virtual reality (VR) provides a revolutionary way for multimedia data visualization and interaction. Instead of sitting on a chair and playing with a controller, real walking gives the most natural experience when exploring the virtual world. However, considering both physiology and psychology factors, there are several challenges to achieve high fidelity when walking in a VR world with massive multimedia data. That is, the isometrical mapping is not feasible in most cases, as the real space can be completely different from the virtual world (usually smaller). Moreover, the sound is usually not consistent with visual cues when considering the directions. Therefore in this paper, we present a VR walking system that contains a huge virtual campus with extensive multimedia data, which combines 3D models, animations and stereo sound. To this end, we adopt a VR redirected walking method for the purpose of perception manipulation, and HRTF-based sound system for audio-visual consistency. By wearing a head-mounted display and walking in a small room tracked by our indoor localization algorithm, the user is able to freely explore the whole VR world with high fidelity.
THE ROLE OF ILLUSTRATION IN ENHANCING READING COMPREHENSION OF COLLEGE STUDENTS

The low interest in reading makes the students have less reading frequency, thus the students are not much experienced in reading correctly. This problem leads them to have a low level of comprehension skill. This research is intended to check and find out whether the students can improve their level of comprehension skill after being given a certain teaching completed with some treatments. The research investigates the impact of using illustrations and its effects on students’ attitude. The data are in the form of scores and students’ opinions. Score data is obtained from the result of pretest and posttest which are calculated quantitatively, while questionnaire and interview are analyzed qualitatively to support the explanation. The results show that there is significant enhancement in student reading skills not only of the expert readers but also of the average readers and of poor readers.
Research progress of DSCM in sub-pixel level measurement

The digital speckle correlation method (DSCM) is a full-field and non-contact optical measurement method. This paper analyzes the research status of the sub-pixel algorithm in DSCM, and analyzes the research progress and data of the algorithm. The inverse compositional Gaussian-Newton algorithm (IC-GN) has obvious advantages. The conclusion is that the inverse compositional Gaussian-Newton algorithm has obvious comprehensive advantages over traditional sub-pixel algorithms in terms of measurement accuracy and computational speed.
DIGITAL STORYTELLING AND DIGITAL BOOK TRAILER APPLICATIONS FOR EDUCATIONAL PURPOSES IN BULGARIA

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 529-534ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0211Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
NEEDS ANALYSIS FOR ICT IN TEACHING AND LEARNING

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 8644-8649ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.2158Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Decentralized Finance on the Ethereum Blockchain

Decentralized finance can be an option of last resort for refugees escaping political and societal collapse. This option can safely store a person’s wealth in a way that accumulates interest while preventing governments and banks from seizing it. In addition, there are other applications available for stabilizing financial holdings, trading, borrowing, speculating, and hedging. Using these applications, attorneys could advise desperate clients on using decentralized financial strategies to circumvent the authority of dysfunctional governments and insolvent banks.These applications are not without their perils, needing smart regulation that allows for attorneys to make good-faith efforts to help clients, while preventing criminal money-laundering and tax-evasion schemes. With carefully crafted regulation, a new field of law could open up to U.S.-based attorneys. This field of law has the potential to provide hours of lucrative legal work helping clients maintain their savings.
Keyword Extraction Performance Analysis

This paper presents a survey-cum-evaluation of methods for the comprehensive comparison of the task of keyword extraction using datasets of various sizes, forms, and genre. We use four different datasets which includes Amazon product data - Automotive, SemEval 2010, TMDB and Stack Exchange. Moreover, a subset of 100 Amazon product reviews is annotated and utilized for evaluation in this paper, to our knowledge, for the first time. Datasets are evaluated by five Natural Language Processing approaches (3 unsupervised and 2 supervised), which include TF-IDF, RAKE, TextRank, LDA and Shallow Neural Network. We use a ten-fold cross-validation scheme and evaluate the performance of the aforementioned approaches using recall, precision and F-score. Our analysis and results provide guidelines on the proper approaches to use for different types of datasets. Furthermore, our results indicate that certain approaches achieve improved performance with certain datasets due to inherent characteristics of the data.
From wooden shoe to click of a button: the risk of disgruntled employees

As a society, we have greatly profited from the digitisation of our processes, the rise of the Internet and the Internet of Things (IoT). As companies, we are able to connect systems and data so that we can work efficiently and provide customers with the best service at the lowest cost. These benefits also bring a great amount of dependency on these systems and have subsequently opened a Pandora's Box of very real and ever-increasing cyber- and technology-related exposures. Many cyber incidents are the result of actions taken by someone within the organisation. And a special category that is getting increasing attention is the disgruntled employee. In recent years, we have seen examples of employees facilitating sabotage and fraud for the benefit of a new employer or to satiate their need for recognition. Whatever the motivation, insiders are perfectly placed to wreak considerable damage on the organisation, either through sabotage or data theft, as Sjaak Schouteren of JLT Specialty explains.
Learning DMEs from Positive and Negative Examples

The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a (valid) schema. Therefore, it is essential to devise algorithms to infer schemas from XML documents. The fundamental task in XML schema inference is learning regular expressions. In this paper we consider unordered XML, where the relative order among siblings is ignored, and focus on the subclass called disjunctive multiplicity expressions (DMEs) which are proposed for unordered XML. Previous work in this direction lacks inference algorithms that support for learning DME from both positive and negative examples. In this paper, we provide an algorithm to learn DMEs from both positive and negative examples based on genetic algorithms.
Data Mining for Statistical Evaluation of Summative and Competency-Based Assessments in Mathematics

Measuring student achievement and competencies in mathematics is important for the teacher and the educational system, as well as in view of improving the motivation to learn among students. In this study we aim to develop an assessment methodology based on data mining approach. Two data mining techniques – cluster analysis and classification and regression trees (CART) are applied to investigate the influence of assessment elements on the final grade in two core mathematical subjects - linear algebra and analytical geometry. In addition, the specialty, academic year of education, and the sex of the students, as well as competency-based test results on content covered by secondary education curriculum are included. Using hierarchical cluster analysis the variables of scale type are classified into two clusters. CART models are built to regress and predict the summative assessment results in dependence of examined variables. The obtained models fit well over 90% of the data. It was established the relative importance of used variables in the model. The obtained results help to measure directly the student achievements and competencies in mathematics.
Information Security Culture Assessment of Small and Medium-Sized Enterprises in Tanzania

This study explores the status of information security culture (ISC) of small and medium-sized enterprises (SMEs) in sub-Saharan Africa (SSA) using Tanzania as a case. To assess the ISC of SMEs, measurement criteria from organizational and environmental dimensions were compiled from the literature. A combination of quantitative and qualitative methods was employed to collect data. The ISC dimensions were assessed using surveys collected using both paper and online sources, from 39 SMEs in the roundtable and five focus group discussions. The findings indicated lack of information security policy, absence of security education, training and awareness (SETA) programs, lack of human resource, poor risk assessment, and management and lack of national information security culture initiatives. These findings show the immaturity of ISC in SMEs in Tanzania. The results and implications of these findings suggest further research and intervention is necessary to institutionalize ISC in the SME environment.
Crowdsourced Time-Sync Video Recommendation via Semantic-Aware Neural Collaborative Filtering

As an emerging type of video comments, time-sync comments (TSCs) enable viewers to make comments on video shots in a real-time manner. Such comments well reflect user interests in the frame level, which can be utilized to further improve the accuracy of video recommendation. In this paper, we make the first attempt in this direction and propose a new video recommendation algorithm called SACF by exploiting temporal relationship between time-sync comments and video frames. Our algorithm can extract a rich set of semantic features from crowdsourced time-sync comments, and combine latent semantic representations of users and videos by neural collaborative filtering. We conduct extensive experiments using real TSC datasets, and our results show that our proposed algorithm can improve the recommendation performance by 9.73% in HR@10 and 5.72% in NDCG@10 compared with other baseline solutions.
Anti-corruption Efforts in National ICT Policies

Many countries in Sub-Saharan Africa suffer from endemic corruption, which disrupts development efforts. Although ICTs is no magic wand, several studies indicate that ICT can serve as an important tool for combatting corruption by increasing transparency, and enable citizens to hold public officials accountable. Anti-corruption efforts are however dependent on strong political will and rule of law to be successful. A policy typically contains description of government intentions formulated into concrete objectives, as well as the rationale behind government targets. Furthermore, policies guide programmatic responses. The aim of this study is to explore how ICTs’ anti-corruption potential have been understood at the policy level over time in ICT policies from nine Sub-Saharan African countries between 2000 and 2018. The study concludes that although there are signs of a growing understanding of ICTs as a multi-purpose tool for anti-corruption towards the end of the period, most policies fail to produce a comprehensive and explicit narratives as well as statements affirming political will. The policy environment’s development trajectory, albeit positive, thus leaves room for improvement in terms highlighting ICTs potential contributions.
Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion

In this work we introduce Lifting Autoencoders, a generative 3D surface-based model of object categories. We bring together ideas from non-rigid structure from motion, image formation, and morphable models to learn a controllable, geometric model of 3D categories in an entirely unsupervised manner from an unstructured set of images. We exploit the 3D geometric nature of our model and use normal information to disentangle appearance into illumination, shading and albedo. We further use weak supervision to disentangle the non-rigid shape variability of human faces into identity and expression. We combine the 3D representation with a differentiable renderer to generate RGB images and append an adversarially trained refinement network to obtain sharp, photorealistic image reconstruction results. The learned generative model can be controlled in terms of interpretable geometry and appearance factors, allowing us to perform photorealistic image manipulation of identity, expression, 3D pose, and illumination properties.
Bloom Filter Based Privacy Preserving Deduplication System

Deduplication is a data reduction technique which eliminates uploading and storing redundant data. Therefore, it is widely adopted in cloud storage services to reduce communication and storage overhead. However, deduplication can be used as a side channel to learn the existence of a particular data in cloud storage thereby raising significant privacy issues. The existing solutions delay deduplication process to hide information regarding the presence of data. These solutions increase communication overhead as the client needs to send data even if it is present on storage. In this paper, we present a novel privacy preserving deduplication approach using bloom filter. In our approach, bloom filter (containing blocks of data) is used as a deduplication identity. When a client sends an upload request, the server responds by sending a genuine bloom filter (if data exists) along with some dummy filters. Now, that client who genuinely owns the data, can learn the existence information by computing bloom filter of the data. Further, client does not need to send the data if it exists on storage. Security analysis proves that our approach provides privacy to data at deduplication system. We implement the approach and demonstrate that communication overhead is significantly less in our approach than the existing approaches.
Introduction to JavaScript

JavaScript has changed a lot over the years. We are currently in a time where there is a JavaScript library for just about anything you would like to build. JavaScript lives both on the client and the server, on the desktop and on mobile devices.
An accelerated variance reducing stochastic method with Douglas-Rachford splitting

We consider the problem of minimizing the regularized empirical risk function which is represented as the average of a large number of convex loss functions plus a possibly non-smooth convex regularization term. In this paper, we propose a fast variance reducing (VR) stochastic method called Prox2-SAGA. Different from traditional VR stochastic methods, Prox2-SAGA replaces the stochastic gradient of the loss function with the corresponding gradient mapping. In addition, Prox2-SAGA also computes the gradient mapping of the regularization term. These two gradient mappings constitute a Douglas-Rachford splitting step. For strongly convex and smooth loss functions, we prove that Prox2-SAGA can achieve a linear convergence rate comparable to other accelerated VR stochastic methods. In addition, Prox2-SAGA is more practical as it involves only the stepsize to tune. When each loss function is smooth but non-strongly convex, we prove a convergence rate of $${\mathcal {O}}(1/k)$$ for the proposed Prox2-SAGA method, where k is the number of iterations. Moreover, experiments show that Prox2-SAGA is valid for non-smooth loss functions, and for strongly convex and smooth loss functions, Prox2-SAGA is prominently faster when loss functions are ill-conditioned.
Comparative Study of Energy Consumption between OTTV and Sefaira in a House

Education is one of the aspects that support the establishment of smart city, especially in the study of architecture. The study aims to provide easiness, anticipate the unexpected incident, and to manage resources efficiently while designing a building. In the process, assisted by the software, OTTV and Sefaira are different software in the calculations. OTTV counts manually while Sefaira counts digitally. Sefaira is easier in its calculations, but its accuracy rate needs to be examined. Therefore, this research will discuss how big a difference in calculation of the manual one by OTTV and the digital one by Sefaira. The object of the research was the building of the House, so that it is easier to understand. Expected results of this research are that we can get a selection of software that is easier, more efficient, and more accurate to be used in the learning process
Typed-based Relaxed Noninterference for Free.

Despite the clear need for specifying and enforcing information flow policies, existing tools and theories either fall short of practical languages, fail to encompass the declassification needed for practical requirements, or fail to provide provable guarantees. In this paper we make progress on provable guarantees encompassing declassification by leveraging type abstraction. We translate information flow policies, with declassification, into an interface for which an unmodified standard typechecker can be applied to a source program - if it typechecks, the program provably satisfies the policy. Our proof reduces security to the mathematical foundation of data abstraction, Reynolds' abstraction theorem. By proving this result for a large fragment of pure ML, we give evidence for the potential to build sound security tools using off the shelf language tools and their theories.