Reviewerly/exHarmony huggingface dataset:
An exploration of inquiry-based authentic learning enabled by mobile technology for primary science

The rapid development of mobile technologies has been shown to assist in science learning, but more research is needed on the pedagogical integration of such technologies into authentic learning in a formal science curriculum. Thus, we conducted a study on exploring the impact of a learning platform nQuire-it together with its scientific mobile sensors in an authentic and inquiry-based learning environment on students' science learning. To examine the effectiveness of this learning platform and its mobile sensors, a mixed methods study was conducted for analysing students' performance in science inquiry, conceptual understanding and ability to connect content knowledge with daily life experiences. The results suggested that the learning platform together with scientific mobile sensors supported students' authentic science learning in following dimensions: academic performance, learning motivation, interest in learning and ability to link knowledge gained in and outside the classroom.
AUDIO BOOKS AS TEACHING MEDIA TO BLIND STUDENTS IN LEARNING EFL

The focus of this study on the implementation of audio books as teaching media for blind students in learning English. The purpose of this study was to determine the implementation of audio books as teaching media for blind students in learning English and to find out audio books used in learning EFL. The study was conducted at SLB YAPTI Makassar with a number of samples taken were 20 students conducted randomly. The instruments in this study were tests, observations, and questionnaires. The results of the study show that the implementation of audio books as teaching media for blind students in learning English can help students in the learning process. The implementation of the audio book also increases student motivation in learning because it makes it easier for students to study anywhere and anytime.
Conceptual Model for Connected Vehicles Safety and Security using Big Data Analytics

The capability of Connected Vehicles (CVs) connecting to a nearby vehicle, surrounding infrastructure and cyberspace presents a high risk in the aspect of safety and security of the CV and others.Data volume generated from the sensors and infrastructure in CVs environment are enormous.Thus, CVs implementations require a real-time big data processing and analytics to detect any anomaly in the CVs's environment which are physical layer, network layer and application layer.CVs are exposed to various vulnerabilities associated with exploitations or malfunctions of the components in each layer that could result in various safety and security event such as congestion and collision.The safety and security risks added an extra layer of required protection for the CVs implementation that need to be studied and refined.To address this gap, this research aims to determine the basic components of safety and security for CVs implementation and propose a conceptual model for safety and security in CVs by applying the machine learning and deep learning techniques.The proposed model is highly correlated to safety and security and could be applied in congestion and collision prediction.
Universal location referencing and homomorphic evaluation of geospatial query

Location data reveals users’ trajectories, yet it is often shared to enable many location-based services (LBS). In this paper, we propose a privacy-preserving geospatial query system with geo-hashing and somewhat homomorphic encryption. We geo-hash locations using space-filling curves for locality-preserving dimension reduction, which allows the users to specify granularity preference of their location and is agnostic to specific maps or precoded location models. Our system features three homomorphic algorithms to compute geospatial queries on encrypted location data and encrypted privacy preferences. Comparing with previous work, one of our algorithms reduces the multiplicative depth of a basic homomorphic computation approach by more than half, which significantly speeds it up. We then present an optimized prototype and experimentally demonstrates its utility in spatial cloaking.
An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction

The base price of Airbnb properties prediction is still a new area of prediction research, especially with the Extreme Learning Machine (ELM).The previous studies had several suggestions for the advantages of ELM, such as good generalization performance, fast learning speed, and high prediction accuracy.This paper proposes how the ELM approach is used as a prediction model for Air BnB base price.Generally, the steps are setting hidden neuron numbers, randomly assigning input weight and hidden layer biases, calculating the output layer; and the entire learning measure finished through one numerical change without iteration.The performance of the model is estimated utilizing mean squared error, mean absolute percentage error, and root mean squared error.Experiment with Airbnb dataset in London with twentyone features as input generates a faster learning speed and better accuracy than the existing model.
Improving Students’ 21st Century Skills Through Creative Writing as A Creative Media

&lt;p class="8Abstractcontent"&gt;The ability to improve students’ 21&lt;sup&gt;st &lt;/sup&gt;century skills using creative writing is truly one of the most revolutionary developments in modern creative English education. This type of education, however, is not without its challenges, the way of using creative projects as the creative media to improve writing skills experiences showed the students and the teachers as well that to improve ‘21&lt;sup&gt;st&lt;/sup&gt; century skills’ through creative writing is imaginative, and creative way. This study conducted an observation of design students at the University of Indraprasta PGRI Jakarta, especially their projects and literature study. The qualitative method with an integrated creative approach of 4C’s is used to analyze the creative writing elements of students’ creative projects as a creative media. The results of this study are useful to be used as a reference in improving students’ 21st century skills and creative writing skills as well by using those Creative Writing projects as a creative media. Furthermore, the results show most of the students have improved their 4C’s skills which is called 21&lt;sup&gt;st&lt;/sup&gt; century skills, grow their language skills by writing the projects creatively and innovatively.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; 21&lt;sup&gt;st&lt;/sup&gt; century skill, creative writing, media
Education During a Pandemic: Prospects and Challenges of Digital Learning

The study focuses on the impact of the COVID-19 pandemic on education systems around the world. The article reveals similarities and differences in decisions made by the responsible bodies in order to ensure the work of educational institutions during the current crisis caused by the new coronavirus pandemic. The author analyses the tools and facilities selected for education process and argues that in most cases digital technologies help students to continue their education even in the face of serious social shocks. At the same time, the downside of digital learning is also discussed. In a significant number of cases, the inability to attend schools and universities has a painful effect on the overall physical (such as food support), psychosocial (stress) and economic (additional costs associated with the need to use equipment and communication facilities) condition of both students and their families. The author emphasizes the pro and contra of digitalization as a vector for the development of education: new technologies not only contribute to solving the “old” issues of the industry, but also provoke the emergence of new challenges in this area. It is noted that the current situation in education can radically change not only the set of familiar tools for transferring knowledge, but also its content. Decision makers, heads of educational institutions, students and their parents face the challenge of finding the optimal ratio of “new” (digital) and “old” (classical) in the emerging model of education of the XXI century. The research is based on data published by international organizations (UN, ITU, UNESCO et al.), educational institutions of various countries of the world, and the author's personal teaching experience during the pandemic.
The Right Sentiment Analysis Method of Indonesian Tourism in Social Media Twitter

The growth of social media is changing the way humans communicate with each other, many people use social media such as Twitter to express opinions, experiences and other things that concern them, where things like this are often referred to as sentiments. The concept of social media is now the focus of business people to find out people's sentiments about a product or place that will become a business. Sentiment Analysis or often also called opinion mining is a computational study of people's opinions, appraisal, and emotions through entities, events and attributes owned. Sentiment analysis itself has recently become a popular topic for research because sentiment analysis can be applied in many industrial sectors, one of which is the tourism industry in Indonesia. To be able to do a sentiment analysis requires mastery of several techniques such as techniques for doing text mining, machine learning and natural language processing (NLP) to be able to process large and unstructured data coming from social media. Some methods that are often used include Naive Bayes, Neural Networks, K-Nearest Neighbor, Support Vector Machines, and Decision Tree. Because of this, this research will compare these four algorithms so that an algorithm can be used to analyze people's sentiments towards the city of Bali.
TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing

This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection (Schlechtweg et al., 2020).We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold.We also propose Gamma Quantile Threshold to distinguish between changed and stable words.Based on our system, we conduct a comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based methods.Evaluation results show that Skip-gram with Temporal Referencing achieves the best performance of 66.5% classification accuracy and 51.8% Spearman's Ranking Correlation.
On Locating Paths in Compressed Tries

In this paper, we consider the problem of compressing a trie while supporting the powerful locate queries: to return the pre-order identifiers of all nodes reached by a path labeled with a given query pattern. Our result builds on top of the XBW tree transform of Ferragina et al. [FOCS 2005] and generalizes the r-index locate machinery of Gagie et al. [SODA 2018, JACM 2020] based on the run-length encoded Burrows-Wheeler transform (BWT). Our first contribution is to propose a suitable generalization of the run-length BWT to tries. We show that this natural generalization enjoys several of the useful properties of its counterpart on strings: in particular, the transform natively supports counting occurrences of a query pattern on the trie's paths and its size r captures the trie's repetitiveness and lower-bounds a natural notion of trie entropy. Our main contribution is a much deeper insight into the combinatorial structure of this object. In detail, we show that a data structure of O(r log n) + 2n + o(n) bits, where n is the number of nodes, allows locating the occ occurrences of a pattern of length m in nearly-optimal O(m log σ + occ) time, where σ is the alphabet's size. Our solution consists in sampling O(r) nodes that can be used as “anchor points” during the locate process. Once obtained the pre-order identifier of the first pattern occurrence (in co-lexicographic order), we show that a constant number of constant-time jumps between those anchor points lead to the identifier of the next pattern occurrence, thus enabling locating in optimal O(1) time per occurrence.
Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity

Word similarity is widely used in machine learning applications like searching engine and recommendation.Measuring the changing meaning of the same word between two different sentences is not only a way to handle complex features in word usage (such as sentence syntax and semantics), but also an important method for different word polysemy modeling.In this paper, we present the methodology proposed by team Ferryman.Our system is based on the Bidirectional Encoder Representations from Transformers (BERT) model combined with term frequency-inverse document frequency (TF-IDF), applying the method on the provided datasets called CoSimLex, which covers four different languages including English, Croatian, Slovene, and Finnish.Our team
Automated Prediction of Examinee Proficiency from Short-Answer Questions

This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs).While previous approaches train on manually labeled data to predict the human ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does not require manually labeled data to train on.We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs).First, the latent trait of examinee proficiency is measured using the scored MCQs and then a model is trained on the experimental SAQ responses as input, aiming to predict proficiency as its target variable.The predicted value is then used as a "score" for the SAQ response and evaluated in terms of its contribution to the precision of proficiency estimation.
Comparison of Geometric Features and Color Features for Face Recognition

Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%.
SLAMMP Framework for Cloud Resource Management and Its Impact on Healthcare Computational Techniques

Technology such as cloud computing(CC) is constantly evolving and being adopted by the industries to manage their data and tasks. CC provides the resources for managing the tasks of the cloud users. The acceptance of the CC in healthcare industries is proven to be more cost-effective and convenient. CC manager has to manage the resources to provide services to the end-users of the healthcare sector. The SLAMMP framework discussed here shows how the resources are managed by using the concept of reinforcement learning (RL) and LSTM (long short-term memory) for monitoring and prediction of the cloud resources for healthcare organizations. The task(s) pattern and anti-pattern scenarios have been observed using HMM (hidden Markov model). These patterns will tune the SLA parameters (service level agreement) using blockchain-based smart contracts (SC). The result discussed here indicates that the variations in the cloud resource demand will be handled carefully using the SLAMMP framework. From the result obtained, it is identified that SLAMMP performs well with the parameter used here.
Improved Lane Detection Method Based on Convolutional Neural Network Using Self-attention Distillation

With the rapid development of autopilot technology and various types of sensor, highprecision maps containing a large amount of information for assisting driving have been proposed.The standard lane line detection algorithm relies on the robust estimation of visible lane line markers from a camera image using vision and image processing algorithms.Although the recognition and detection technology for road marking lines is relatively mature, some problems still exist, such as poor detection accuracy and unsatisfactory realtime performance.To solve the problems of the poor robustness and low running speed of the current lane detection methods in complex environments, in this study, we improve current lane detection methods from the perspective of semantic segmentation and propose a DC-VGG-SAD network (VGG: visual geometry group), in which dilated convolution (DC) is used to reduce the complexity of the network to ensure detection accuracy.Furthermore, adding self-attention distillation (SAD) makes the information transmission faster.The proposed network was experimentally evaluated using two large-scale datasets.It was found that when dealing with lane lines in complex environments, the network offers higher detection accuracy and detection speed than most current mainstream networks.
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction

We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction.Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluation of the system output because there is no dataset of system output with manual evaluation.This study manually evaluates the output of grammatical error correction systems to optimize the metrics.Experimental results show that the proposed metric improves the correlation with manual evaluation in both systemand sentence-level meta-evaluation.Our dataset and metric will be made publicly available. 1
Concentration bounds for almost <i>k</i>-wise independence with applications to non-uniform security

We prove a few concentration inequalities for the sum of n binary random variables under weaker conditions than k-wise independence.Namely, we consider two standard conditions that are satisfied in many applications: (a) direct product conditions (b) the XOR condition.Both conditions are weaker than mutual independence and both imply strong concentration bounds (similar to Chernoff-Hoeffding) on the tail probability of the sum of bounded random variables ([Impagliazzo and Kabanets, APPROX-RANDOM 10], [Unger, FOCS 09]).Our inequalities can be stated as the implication of threshold direct product theorems from either k-wise direct product conditions, or the k-wise XOR condition.By proving optimality of our inequalities, we show a clear separation for k n between k-wise product conditions and XOR condition as well as a stark contrast between k-wise and n-wise product theorems.We use these bounds in the cryptographic application that provides provable security against algorithms with S-bit advice.Namely, we show how the problem reduces to proving S-wise direct product theorems or Swise XOR lemmas for certain ranges of parameters.Finally, we derive a new S-wise XOR lemma, which yields a tight non-uniform bound for length increasing pseudorandom generators, resolving a 10-year-old open problem from [De, Trevisan, and Tulsiani, CRYPTO 10].
BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text

Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population.In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets.While the CNN component helps in the classification of positive and negative tweets, the self-attention based LSTM, helps in the classification of neutral tweets, because of its ability to identify correct sentiment among multiple sentiment bearing units.We achieved F1 scores of 0.707 (ranked 5 th ) and 0.725 (ranked 13 th ) on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively.The submissions for Hinglish and Spanglish tasks were made under the usernames ayushk and harsh 6 respectively.
Self-Progressing Robust Training

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods such as adversarial training explicitly uses an ``attack'' (e.g., l_infty-norm bounded perturbation) to generate adversarial examples during model training for improving adversarial robustness. In this paper, we take a different perspective and propose a new framework SPROUT, self-progressing robust training. During model training, SPROUT progressively adjusts training label distribution via our proposed parametrized label smoothing technique, making training free of attack generation and more scalable. We also motivate SPROUT using a general formulation based on vicinity risk minimization, which includes many robust training methods as special cases. Compared with state-of-the-art adversarial training methods (PGD-l_infty and TRADES) under l_infty-norm bounded attacks and various invariance tests, SPROUT consistently attains superior performance and is more scalable to large neural networks. Our results shed new light on scalable, effective and attack-independent robust training methods.