Reviewerly/exHarmony huggingface dataset:
A study of the development of an EAP program model: Focusing on college English classes.

The study aims to design an English for Academic Purposes (EAP) program model for college English classes (CECs) in Korea. To achieve this aim, two questions were raised: 1) how do foreign universities apply EAP into their CECs? and 2) what are differences in Y university students' satisfaction with the current CECs system and their future needs? In order to answer the first research question, several cases of English programs in foreign universities were investigated through Internet queries. For the second question, we collected surveys from a total of 673 undergraduates. The results indicated that foreign universities primarily target English for general academic purposes, prefer task-based instruction in their EAP classes, and emphasize academic writing and reading skills. In addition, the survey results displayed that the satisfaction with current CECs and future needs are different among students in different disciplines, most students hope to take EAP classes prior to their 4th semester, and students prefer a class size less than 20 students, and an after-class workload less than 4 hours a week. Those results were integrated to ultimately establish a dual model for EAP. More detailed accounts of the model are discussed in the paper.
Sequential A/B Testing

In this paper sequential analysis to conversion rate optimization is considered. Sequential testing as the technique of decision making during A/B testing by sequentially gathering and processing the data is described. Sequential A/B testing results are presented descriptively and graphically.
Web Based Ontology Implementation for Information Search System

Currently, search engine websites are often used by people in searching for information about the profession. Such information can help job seekers to develop training strategies to master the competence of job skills, so that job seekers get jobs that match their competencies. Thus, this research develops a website that can be used to search professional information based on the name of the profession or competence in the field of Information Technology in the domain of the National Occupational Map of ICT mapped with ontology. Ontology is a hierarchical structure that describes a domain used as a representation of a knowledge base. Ontology design method used in this research is Seven Step method. Implementation of ontology using Protege 5.2 tool as an ontology editor, while for the implementation of ontology into the web used XML. Search on the website is done based on keywords that are input by the user, the keyword can be a profession or competence. Based on the results of trials conducted, the website created able to provide information about the profession in the field of Information Technology, the use of ontology also makes the information submitted to be more structured.
Traffic Sign Recognition by Integrating Convolutional Neural Network and Support Vector Machine

This paper represents a combined model of convolutional neural network (CNN) and support vector machine (SVM) for traffic sign recognition. This model was built by training a CNN model. Once the CNN model is fully trained the output from the later layers of CNN can be used as features. These features were then fed into SVM for classification purpose. Three different models of CNN: modified version of LeNet, AlexNet and ResNet-50 were considered to build three CNN-SVM models. The integrated model of Resnet50 and SVM seems to perform better than ResNet-50 while the other two merged models of Lenet and Alexnet performed worse than their corresponding CNN models. One reason of this can be ResNet-50 having a shallow classification part consisting of only one fully connected layer while modified version of LeNet and AlexNet have 3 and 4 fully connected layers respectively. This combined approach provides for a good comparison between SVM and CNN as classifiers since the features used in both these classifiers are same. So a comparative analysis among three different CNN models and their corresponding integrated models is shown. In our analysis, we considered different measurement metrices like accuracy, precision, recall and F1 score. We used German Traffic Sign Detection Benchmark (GTSRB) dataset. This dataset gives access to a wide range of traffic sign images.
Pocket E-Li: Listening Assessment Made Easy

Listening skills should be given more attention as listening takes precedence over anything else when it comes to acquiring a language (Putriani, Sukirlan &amp; Supriyadi, 2013). Even with the booming of various technology to facilitate teaching and learning of listening skills in class, the assessment conducted to identify students’ level of understanding of certain topic is still not up-to-date and not parallel with the advancement of technology. The current studies show that the use of mobile apps for listening purpose is proven to be effective in reducing students’ anxiety (Rahimi &amp; Soleymani, 2015), sustaining students’ motivation (Read &amp; Kukulska-Hulme, 2015), and improving students’ linguistic competencies (Ramos &amp; Valderruten, 2017). This study is aimed to test the effectiveness of mobile application in assessing students’ listening skills. Diploma students from various faculties in UiTM Shah Alam were randomly chosen to answer listening comprehension questions via the prototype developed, named Pocket E-Li. The results demonstrate that the majority of the students provided positive response towards the implementation of mobile application for listening assessment. Almost all respondents agreed that listening assessment should be conducted via mobile application in the future. It can be concluded that listening assessment via mobile application is beneficial to students since it meets the students’ demands and needs which is equivalent with the use of current technology.
Sentiment Analysis System for Myanmar News Using Support Vector Machine and Naïve Bayes

With the growth of web technology, there are huge amount of information in the web for the internet users. Users not only use that information but also provide opinions for decision making process. Sentiment analysis or opinion mining is one of text categorization techniques that extract opinion expressed in a piece of text. This system created sentiment annotated corpus. Feature extraction and selection are needed in sentiment analysis to get high performance. N-grams are used for feature selection and TF-IDF is used for feature extraction. Machine learning is creating a computer programs that improve performance with experience. Machine learning is combination of the techniques and basis from both statistics and computer science. There are generally three types of machine learning algorithm such as supervised machine learning, unsupervised machine learning and hybrid learning. Supervised machine learning also known as classification algorithm that models the relationships between the feature set and the label set. In this system, Myanmar sentiment analysis system is implemented using supervised machine learning method. This system shows the comparison results of support vector machine (SVM) and Naïve Bayes algorithms.
Multi-Thread Concurrent Compression Algorithm for Genomic Big Data

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://github.com/APandaThief/FastLNGC.
Pengaruh Kualitas Informasi, Kualitas Sistem Informasi, dan Perceived Usefulness pada Kepuasan Pengguna SIMDA

This study aims to examine the effect of information quality, information system quality and perceived usefulness on user satisfaction of regional management information systems (SIMDA). Data are collected using research instruments in the form of questionnaires distributed to the Head of Financial Subdivision, Treasurer of Revenue, Treasurer of Expenditures, and Operators SIMDA to the Regional Government of Badung Regency. The data analysis technique used in this study is multiple linear regression analysis. The results of the analysis show that information quality, information system quality, and perceived usefulness have a positive effect on SIMDA user satisfaction. Thus, the better the quality of information, the quality of information systems, and perceived usefulness, it will increase SIMDA user satisfaction.&#x0D; Keywords: Information Quality; Information System Quality; Perceived Usefulness; User Satisfaction.
Online Network Topology Inference with Partial Connectivity Informatio

We develop algorithms for online topology inference from streaming nodal observations and partial connectivity information; i.e., a priori knowledge on the presence or absence of a few edges may be available as in the link prediction problem. The observations are modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Said stationarity assumption implies the simultaneous diagonalization of the observations' covariance matrix and the so-called graph shift operator (GSO), here the adjacency matrix of the sought graph. When the GSO eigenvectors are perfectly obtained from the ensemble covariance, we examine the structure of the feasible set of adjacency matrices and its dependency on the prior connectivity information available. In practice one can only form an empirical estimate of the covariance matrix, so we develop an alternating algorithm to find a sparse GSO given its imperfectly estimated eigenvectors. Upon sensing new diffused observations in the streaming setting, we efficiently update eigenvectors and perform only one (or a few) online iteration(s) of the proposed algorithm until a new datum is observed. Numerical tests showcase the effectiveness of the novel batch and online algorithms in recovering real-world graphs.
Emerging Applications of Generative Adversarial Networks

Abstract With the idea of generative adversarial network (GAN) being brought up since 2014, a serial of research emerged in this field. While bring many exciting opportunities, these applications also raise the awareness of the risk of fake images which may cause huge damage. In this paper, we revisit the history of GAN’s development and summarize some important models and applications. Additionally, we discuss the worries which GAN would bring to people and possible solutions.
BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer

An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets.
Total Variation and Alternate Direction Method for Deblurring of Digital Images

Images captured using smartphones and video cameras are recorded and can be used anywhere and at any time. While taking a quick shot or while capturing the moving objects, it may lead to the motion blurred images. In order to recover the sharp images from motion blurred images, blind motion deblurring can be used. Motion deblurring can be done by knowing both edge and non-edge of motion blurred images. Edge and non-edge are the two methods used in total variation and alternate direction method for deblurring of digital images. Step edges can be predicted and detected by using edge-specific method. In non-edge method, it explores various image statistics, such as the prior distributions and it is sensitive to statistical variation over different images. Both methods are used in large dataset images, but it fails extremely in simple images. To overcome this problem, total variation (TV) based regularization method is used which is followed by an iteratively reweighted algorithm based on alternating direction method. To get higher results, LSED prediction—based technique is employed, which first of all restores sharp edges and then uses them to estimate initial kernel that traps the optimization of local minimum corresponding to sharp images.
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just 4 labels per class. Since FixMatch bears many similarities to existing SSL methods that achieve worse performance, we carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. We make our code available at this https URL.
The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

Solar radiation is a critical requirement for all solar power plants. As it is a time-varying quantity, the power output of any solar power plant is also time variant in nature. Hence, for the prediction of probable electricity generation for a few days in advance, for any solar power plant, forecasting solar radiation a few days into the future is vital. Hourly forecasting for a few days in advance may help a utility or ISO in the bidding process. In this study, 1-day-ahead to 6-day-ahead hourly solar radiation forecasting was been performed using the MARS, CART, M5 and random forest models. The data required for the forecasting were collected from a solar radiation resource setup, commissioned by an autonomous body of the Government of India in Gorakhpur, India. From the results, it was determined that, for the present study, the random forest model provided the best results, whereas the CART model presented the worst results among all four models considered.
TIDE: A General Toolbox for Identifying Object Detection Errors

We introduce TIDE, a framework and associated toolbox ( https://dbolya.github.io/tide/ ) for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction system. Thus, our framework can be used as a drop-in replacement for the standard mAP computation while providing a comprehensive analysis of each model’s strengths and weaknesses. We segment errors into six types and, crucially, are the first to introduce a technique for measuring the contribution of each error in a way that isolates its effect on overall performance. We show that such a representation is critical for drawing accurate, comprehensive conclusions through in-depth analysis across 4 datasets and 7 recognition models.
From a ‘race to AI’ to a ‘race to AI regulation’: regulatory competition for artificial intelligence

Against a background of global competition to seize the opportunities promised by Artificial Intelligence (AI), many countries and regions are explicitly taking part in a ‘race to AI’. Yet the increased visibility of the technology’s risks has led to ever-louder calls for regulators to look beyond the benefits, and also secure appropriate regulation to ensure AI that is ‘trustworthy’ – i.e. legal, ethical and robust. Besides minimising risks, such regulation could facilitate AI’s uptake, boost legal certainty, and hence also contribute to advancing countries’ position in the race. Consequently, this paper argues that the ‘race to AI’ also brings forth a ‘race to AI regulation’. After discussing the regulatory toolbox for AI and some of the challenges that regulators face when making use thereof, this paper assesses to which extent regulatory competition for AI – or its counterpart, regulatory convergence – is a possibility, a reality and a desirability.
Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning (ML) model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML-parameters to edge server, based on the non-orthogonal multiple access (NOMA) and time division multiple access (TDMA), respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit (CPU) frequencies for local update. We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.
Multi-Object Representation Learning with Iterative Variational Inference

Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. Instead, we argue for the importance of learning to segment and represent objects jointly. We demonstrate that, starting from the simple assumption that a scene is composed of multiple entities, it is possible to learn to segment images into interpretable objects with disentangled representations. Our method learns -- without supervision -- to inpaint occluded parts, and extrapolates to scenes with more objects and to unseen objects with novel feature combinations. We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences.
On extended long short-term memory and dependent bidirectional recurrent neural network

In this work, we first analyze the memory behavior in three recurrent neural networks (RNN) cells; namely, the simple RNN (SRN), the long short-term memory (LSTM) and the gated recurrent unit (GRU), where the memory is defined as a function that maps previous elements in a sequence to the current output. Our study shows that all three of them suffer rapid memory decay. Then, to alleviate this effect, we introduce trainable scaling factors that act like an attention mechanism to adjust memory decay adaptively. The new design is called the extended LSTM (ELSTM). Finally, to design a system that is robust to previous erroneous predictions, we propose a dependent bidirectional recurrent neural network (DBRNN). Extensive experiments are conducted on different language tasks to demonstrate the superiority of the proposed ELSTM and DBRNN solutions. The ELTSM has achieved up to 30% increase in the labeled attachment score (LAS) as compared to LSTM and GRU in the dependency parsing (DP) task. Our models also outperform other state-of-the-art models such as bi-attention [1] and convolutional sequence to sequence (convseq2seq) [2] by close to 10% in the LAS.
Building Modern Clouds: Using Docker, Kubernetes &amp; Google Cloud Platform

To develop and build a modern cloud infrastructure or DevOps implementation than both Docker and Kubernetes have revolutionized the era of software development and operations. Although both are different, they unify the process of development and integration, it is now possible to build any architecture by using these technologies. Docker is used to build, ship and run any application anywhere. Docker allows the use of the same available resources. These containers can be used to make deployments much faster. Containers use less space, are reliable and are very fast. Docker Swarm helps to manage the docker container. Kubernetes is an automated container management, deployment and scaling platform. Using Google Cloud Platform to deploy containers on Kubernetes Engine enabling rapid application development and management. Kubernetes provides key features like deployment, easy ways to scale, and monitoring.