Reviewerly/exHarmony huggingface dataset:
Integration of Science learning Apps based on Inquiry Based Science Education (IBSE) in enhancing Students Science Process Skills (SPS)

The teaching and learning process no longer has its focus on the classroom. There are various teaching methods which teachers can use these days. However, the traditional, passive and teacher-oriented methods of teaching Science are still being widely used and this has led to the deterioration of students’ interest in Science. Thus, this study was conducted to examine the effectiveness of Inquiry Based Science Education (IBSE) learning through apps in improving the level of students' mastery and achievement in the Science Process Skills (SPS). The study involved a total of 30 Year-6 students from a school in the district of Johor Bahru selected based on the their achievement in the Primary School Achievement Test. It took 5 weeks to complete the study which used quasi-experimental design of time series design followed by multiple repetitions of assessments before the post-test was given to the students. The results of the paired-samples t-test were found to be significant (t = - 11.119, df = 29, p &amp;lt;.05) which showed that, there was a difference in the level of the students’ performance in the pre- and post-tests. The quasi-experimental study of this series of time design used recurring measurements carried out on dependent variables and one-way ANOVA tests for repeated measures were used for the data analysis. The test results of Mauchly's Test of Sphericity showed that, there were significant differences in the three test scores F(1.63,47.4)=344, p&amp;lt;.05. The mean score values for test 2, test 3 and test 4 outweighed the mean of test 1. In addition, the graphical shape of plots profile shows that, teaching activities through apps enhanced students’ Science Process Skills (SPS). Therefore, it can be concluded that, the integration of Inquiry-Based Science Education (IBSE) in Science learning through Apps is capable of improving students’ mastery and achievement in their Science Process Skills (SPS).
The Impact of Englishgame-Based Mobile Application on Students’ Reading Achievement and Learning Motivation

This study is aimed at finding the impact of Englishgame-based Mobile Application (EBMA) on reading achievement and learning motivation of the students.It is an experimental research which uses test and questionnaire to gather the data.119 samples (58 students of experimental group and 61 students of control group) are selected from eighth grade students of junior high school spread into urban and rural areas in the Jepara Municipality of Central Java Province under the Ministry of Education and Culture Indonesia.By using multivariate analysis of variance (Manova), it was found that the use of EBMA in learning could increase the students' achievement in reading.The students who used EBMA as a medium of learning had better achievement in reading than those who did not (71.12> 65.52).Then, students' motivation in learning differed significantly after the implementation of EBMA.It was higher since the activities engaged and motivated them to learn.Therefore, it indicates that the use of EBMA have great impact on students' reading achievement and learning motivation.
A Development of Augmented Reality Mobile Application to Promote the Traditional Indonesian Food

Traditional food is a representation of the culture and identity of certain regions and is a symbol of cultural heritage. Traditional Indonesian food must be preserved and introduced to the community, especially the easy generation as the next generation of the nation as a form of love for the culture that has been passed down from generation to generation. It aims to avoid the influence of food or foreign cuisine that enters Indonesia, which from time to time makes the preservation of traditional Indonesian food increasingly eroded. One form of an effective cultural approach is by introducing to the easy generation and also to the wider community. Therefore the use of technology is very important to maintain the preservation of traditional Indonesian food and convey it to the community, especially the easy generation. This study aims to develop a mobile application for augmented reality as a promotional medium to introduce traditional Indonesian food. Mobile augmented reality in applications built with the Unity3D program is done in different ways in presenting information to other media. This method allows users to view the media as a presentation of their information in the form of 2D and or 3D image modelling by using tracking images through a smartphone device's camera. The mobile augmented reality application that will be developed can provide an interesting and interactive information experience so that it can be fun for every user so that knowledge of traditional Indonesian food can be distributed and channelled so that the preservation of traditional Indonesian food is maintained.
Distributed subkey-relay-tree-based secure multicast scheme in quantum data center networks

In data center networks (DCNs), secure multicast provisioning services are obtained from data centers and transported to multiple users in a multicast style, which requires a high demand for security. Moreover, these services are usually stored and maintained in multiple geographically dispersed data centers for more reliable and efficient access. Quantum key distribution (QKD) is a state-of-the-art technology employed to unconditionally distribute security keys, based on the principles of quantum physics. Therefore, one should pay more attention to how to efficiently distribute global quantum keys in quantum DCNs in the near future. We investigate the problem of global QKD while leveraging multicast service backups among multiple geographically distributed datacenters for secure service provisioning. A distributed subkey-relay-tree-based, secure multicast scheme is proposed and used as a basis to develop a distributed subkey-relay-tree-based secure multicast-routing and key assignment algorithm. Numerical results show that the latter can achieve higher security probability and lower consumed keys compared with the traditional routing and key assignment scheme and single-key-relay-tree-based secure multicast scheme.
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing

This paper presents our enhanced dependency parsing approach using transformer encoders, coupled with a simple yet powerful ensemble algorithm that takes advantage of both tree and graph dependency parsing.Two types of transformer encoders are compared, a multilingual encoder and language-specific encoders.Our dependency tree parsing (DTP) approach generates only primary dependencies to form trees whereas our dependency graph parsing (DGP) approach handles both primary and secondary dependencies to form graphs. Since DGP does not guarantee the generated graphs are acyclic, the ensemble algorithm is designed to add secondary arcs predicted by DGP to primary arcs predicted by DTP.Our results show that models using the multilingual encoder outperform ones using the language specific encoders for most languages.Moreover, the ensemble models generally show higher labeled attachment score on enhanced dependencies (ELAS) than the DTP and DGP models.As the result, our best parsing models rank the third place on the macro-average ELAS over 17 languages.1 1 1 1 ✕ ✕
Study on students' experiences about online teaching during COVID-19 Outbreak

The online teaching in colleges and universities during the COVID-19 outbreak is one of the challenges faced by faculty and students during this period especially for colleges and universities that meet the quality assurance standards and under the accreditation process. One of the main requirements of quality standards is to carry out a variety of opinion surveys at different stages among different levels of study, analyze, and then provide recommended solutions based on survey findings. Although many researchers have been carried out online teaching, there is no consensus on the impact of a sudden transition from face-to-face learning to online learning especially in community colleges in Saudi Arabia. The purpose of this paper is to present the outcomes of the study on students' experiences about online teaching during COVID-19 Outbreak. Smart PLS program is used for testing the model and to make sure that the variables are appropriate and the outcomes are valid.
Towards Low-Energy Leakage-Resistant Authenticated Encryption from the Duplex Sponge Construction

The ongoing NIST lightweight cryptography standardization process highlights the importance of resistance to side-channel attacks, which has renewed the interest for Authenticated Encryption schemes (AEs) with light(er)-weight sidechannel secure implementations. To address this challenge, our first contribution is to investigate the leakage-resistance of a generic duplex-based stream cipher. When the capacity of the duplex is of c bits, we prove the classical bound, i.e., ≈ 2c/2, under an assumption of non-invertible leakage. Based on this, we propose a new 1-pass AE mode TETSponge, which carefully combines a tweakable block cipher that must have strong protections against side-channel attacks and is scarcely used, and a duplex-style permutation that only needs weak side-channel protections and is used to frugally process the message and associated data. It offers: (i) provable integrity (resp. confidentiality) guarantees in the presence of leakage during both encryption and decryption (resp. encryption only), (ii) some level of nonce misuse robustness. We conclude that TETSponge is an appealing option for the implementation of low-energy AE in settings where side-channel attacks are a concern. We also provides the first rigorous methodology for the leakage-resistance of sponge/duplex-based AEs based on a minimal non-invertibility assumption on leakages, which leads to various insights on designs and implementations.
SIMHAR - Smart Distributed Web Crawler for the Hidden Web Using SIM+Hash and Redis Server

Developing a distributed web crawler obliges major engineering challenges, all of which are eventually associated to scale. To retain corpus of search engine and a reasonable state of freshness, the crawler must be distributed over multiple computers. In distributed crawling, crawling agents are given a task to fetch and download web pages. The number and heterogeneous structure of web pages are increasing rapidly. This made the performance a serious challenge to web crawler systems. In this paper, a distributed web crawler for the hidden web is proposed and implemented. It combines and integrates, scrapy framework and Redis server. Crawling is split into three stages-adaption, relevant source selection and underlying content extraction. The crawler accurately detects and submit the searchable forms. Duplication detection is based on hybrid technology using hash-maps of Redis and Sim+Hash. Redis server is also acting as a data store for a massive amount of web data so that the growth of hidden web databases is handled ensuring scalability.
TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions

As more and more people are expected to work with complex AI-systems, it becomes more important than ever that such systems provide intuitive explanations for their decisions. A prerequisite for holding such explanatory dialogue is the ability of the systems to present their proposed decisions to the user in an easy-to-understand form. Unfortunately, such dialogues could become hard to facilitate in real-world problems where the system may be planning for multiple eventualities in stochastic environments. This means for the system to be effective, it needs to be able to present the policy at a high-level of abstraction and delve into details as required. Towards this end, we investigate the utility of temporal abstractions derived through analytically computed landmarks and their relative ordering to build a summarization of policies for Stochastic Shortest Path Problems. We formalize the concept of policy landmarks and show how it can be used to provide a high level overview of a given policy. Additionally, we establish the connections between the type of hierarchy we generate and previous works in temporal abstractions, specifically MaxQ hierarchies. Our approach is evaluated through user studies as well as empirical metrics that establish that people tend to choose landmarks facts as subgoals to summarize policies and demonstrates the performance of our approach on standard benchmarks.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

Successfully training a deep neural network demands a huge corpus of labeled data.However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort.In this work, we introduce LEAN-LIFE 1 , a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task, but also enables LearnIng From Explanations for each labeling decision.Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data.On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances.Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks--thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.
COVID-19: digital contact tracing technologies and ethical challenges

Digital contact tracing technologies (DCTT) are used in public health surveillance to support rapid reporting, data management and analysis with the intention to improve the efficacy of the health system. One form DCTT that has been receiving attention in many countries facing COVID-19 epidemics is proximity tracking. Globally 47 contact tracing apps are available and for maximally effective for contact identification it should be adopted by 60-75% of a country’s population. But no country could achieve this in near future. Even with no proven efficacy for controlling the present pandemic and it has been deployed in several countries at unprecedented swiftness and in an unregulated environment. From a public health perspective, the essentiality of DCTT can be approved only if it is proved to be necessary, proportionate and sufficiently effective. Any public health measure is ethically correct, if it provides sufficient public health benefit to justify the burdens associated with it. In this context global health experts like WHO, Johns Hopikins university and Oxford university released recommendations on ethics and governance on the use of DCTT. Based on this principle a public health ethical review was done using available literature. Currently, there are no established methods for assessing the effectiveness of digital proximity tracking. More research to evaluate their effectiveness is needed. Governments, developers must ensure that COVID-19 contact- tracing apps satisfactorily address the ethical questions and must ensure the necessary but least intrusive measures for disease surveillance.
Hybrid Low-Order and Higher-Order Graph Convolutional Networks

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.
Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study

Background Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent. Objective Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology. Methods We enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants’ medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain. Results A total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with “good,” indicating fair digital literacy. In the second survey, only 44 participants—those who logged into the app more than once and used the app for more than 28 days—were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability. Conclusions Patients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment.
Using Chinese Glyphs for Named Entity Recognition (Student Abstract)

Most Named Entity Recognition (NER) systems use additional features like part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Adding these external features to NER systems have been shown to have a positive impact. However, creating gazetteers or taggers can take a lot of time and may require extensive data cleaning. In this work instead of using these traditional features we use lexicographic features of Chinese characters. Chinese characters are composed of graphical components called radicals and these components often have some semantic indicators. We propose CNN based models that incorporate this semantic information and use them for NER. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. We present one of the first studies on Chinese OntoNotes v5.0 and show an improvement of + .64 F1 score over the baseline. We present a state-of-the-art (SOTA) F1 score of 71.81 on the Weibo dataset, show a competitive improvement of + 0.72 over baseline on the ResumeNER dataset, and a SOTA F1 score of 96.49 on the MSRA dataset.
Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.
3-D Gaze-Estimation Method Using a Multi-Camera-Multi-Light-Source System

The image of the center of a 3-D circular target is not the center of its imaging ellipse due to the imaging distortion of the spatial circular targets; therefore, traditional 3-D gaze-estimation methods with the multi-camera-multi-light-source (MCMLS) systems generally replace the projection of the pupil center with the virtual image of the pupil center for 3-D gaze estimation. However, this introduces a large gaze-estimation error when the oblique angle between the optical axis of the eye and the camera optical axis is large. To eliminate the error caused by using the virtual image of the pupil center, a 3-D gaze-estimation method using an MCMLS system is developed in this study. We first estimate the cornea center and then determine the matching points of the pupil imaging ellipse using the special polar plane. After the cornea radius and the kappa angle are calibrated, the optical axis of the eye is reconstructed by the refraction planes constructed by the edge points of the pupil imaging ellipses. Thus, the 3-D gaze is estimated by the real-time calculated transformation matrix represented by the kappa angle. The feasibility and performance of the method have been analyzed by the simulations and the experiments. Since the estimation of the spatial pupil center or the construction of the refraction plane using the virtual image of the pupil center is not required, the proposed method mitigates the inherent errors caused by optical axis reconstruction in the traditional methods and simplifies the algorithm for gaze estimation, which has a practical value.
NH-TTC: A gradient-based framework for generalized anticipatory collision avoidance

We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots with arbitrary equations of motions.Our approach exploits implicit differentiation and subgradient descent to locally optimize the non-convex and non-smooth cost functions that arise from planning over the anticipated future positions of nearby obstacles.The result is a flexible framework capable of supporting highquality, collision-free navigation with a wide variety of robot motion models in various challenging scenarios.We show results for different navigating tasks, with various numbers of agents (with and without reciprocity), on both physical differential drive robots, and simulated robots with different motion models and kinematic and dynamic constraints, including accelerationcontrolled agents, differential-drive agents, and smooth car-like agents.The resulting paths are high quality and collision-free, while needing only a few milliseconds of computation as part of an integrated sense-plan-act navigation loop.For a video of further results and reference code,