Reviewerly/exHarmony huggingface dataset:
LOCAL HERITAGE (WARISAN BUDAYA) DAN PENGAJARAN BAHASA INGGRIS

This research was conducted to get an overview of local heritage in teaching English in the classroom. The teaching of foreign languages is one of the factors that can affect the neglect of existing local heritage. The teacher understands the concept of cultural heritage or local wisdom. This concept is believed to be important to be applied in language teaching and learning. This is because the teaching materials presented in the English textbook contain language content that is oriented towards general knowledge and cross-cultural knowledge. This study used a qualitative method where the data sampling techniques were carried out by purposive and snowball sampling. In this study, it was found that linking external knowledge and existing knowledge in students' daily lives was considered important to enable students to express themselves in a foreign language. However, teachers are constrained by the existing curriculum targets so that the teaching of local wisdom is solely in the textbooks.
Remote-Sensing Applications for Mineral Mapping: Boosting Zimbabwe’s Foreign Direct Investment Potential Through Sustainable Technology

Zimbabwe has abundant mineral resource reserves, and like most other countries on the African continent, it is mainly dependent on primary productions for exports as well as economic growth. In fact, much of Zimbabwe’s Gross Domestic Product (GDP) is hinged on the mineral sector, though vast mineral reserves are yet to be quantified and exploited due to a lack of capital and infrastructure. While Zimbabwe is well-endowed, the location, variety and quantities of these reserves is unknown, as the last, comprehensive geological survey occurred at the time of independence in 1980. This has hindered new and current interest in the mining sector, resulting in the nation losing out on potential foreign direct investment. Seeking a solution to this, on the 26th of July 2018, the Ministry of Higher Education and Technology established the Zimbabwean National and Geo-Spatial Space Agency (ZINGSA), intended to use satellite Geospatial Information Services (GIS) technology to monitor the country’s large, unmapped mineral reserves, focusing on lithium and graphite, through its Mineral Mapping Programme. The author pays particular focus to the potential socio-economic benefits of a satellite mineral-mapping programme for Zimbabwe, and presents it as a feasible option to good natural resources management within Zimbabwe’s borders and beyond.
Estimating Technical Efficiency of Selected Commercial Banks in Ethiopia Using Data Envelopment Analysis

The main objective of this study is to estimate the technical efficiency of 13 selected commercial banks in Ethiopia for the period 2010 to 2017 using Data Envelopment Analysis. Both the constant return to scale and variable return to scale efficiency indicate that the state owned bank has better technical efficiency than private banks. Moreover, Dashen Bank Share Company, Awash international bank, United bank Share Company, Nib international bank and Birhan international bank were efficient banks under constant return to scale. Besides, all banks are technically efficient under variable return to scale input oriented except Cooperative bank of Oromia. Finally, Commercial Bank of Ethiopia, Dashen Bank Share Company, United Bank Share Company, Nib International Bank and Birhan international Bank were efficient banks under output oriented constant returns to scale DEA and cooperative bank of Oromia is the only bank that has increasing return to scale in the study period.
BENEFITS OF DIGITAL LEARNING APPLICATION AT UNIVERSITIES

The main goal of ongoing paper is to emphasize on the top priority benefits dealt with the urgent modern time issue such as digitalization and computerization of the educational process at higher educational institutions. The background of the survey states that the use of information technology facilitates and improves learning process, activates and stimulates the mental activity of students and allows to diversify and increase its efficiency. The main findings of the survey can be summed up in three blocks. Firstly, we have presented plenty of definitions of the term "digital learning", which not only differ in their formulation and the terms used, but also, in some cases, they have opposite, sometimes narrower focus. To add, the present study shows various definitions: Dutch, Portugal, Serbia, Serbia and Austrian. Secondly, the results were connected with general overview of five competences, such as: information and data literacy, communication and collaboration, digital content creation, protecting personal data and privacy, problem solving. identifying digital competence gaps. Thirdly, the ten sufficient benefits of digital learning have been identified as well. They were titled in the following way: personalized learning, expanded learning opportunities, high engagement learning, competency- based learning, collaborative learning, quality learning products, relevant and regularly updated content learning, next-gen learning for educators. The uniqueness of the research findings is approved with: the broad multi-exemplifications of the term definition "digital learning" in different countries; the presentation of more precise term substitute "one-to-one technology- enhanced learning"; the provision of the key digital learning competences and benefits among the great number of the in the scientific researches. Moreover, we must mention that proper consideration and application of all five competences and ten benefits of digital learning in the higher education process will: improve students’ motivation, accountability; help them to become more engaged in the process, more interested in growing their knowledge base; assist them to acquire problem-solving skills and familiarity with emerging technologies. In particular, the practical value of digital learning skills is of a great assistance during learning English, using numerous on-line digital free available sources.
Choice of plausible alternatives dataset in Croatian COPA-HR

The COPA-HR dataset (Choice of plausible alternatives in Croatian) is a translation of the English COPA dataset (https://people.ict.usc.edu/~gordon/copa.html) by following the XCOPA dataset translation methodology (https://arxiv.org/abs/2005.00333). The dataset consists of 1000 premises (My body cast a shadow over the grass), each given a question (What is the cause?), and two choices (The sun was rising; The grass was cut), with a label encoding which of the choices is more plausible given the annotator or translator (The sun was rising).The observed agreement of the English annotator and the Croatian translator is perfect on the training and the validation dataset, with one different label (agreement of 99.8%) on the test dataset.The current state-of-the-art on this dataset is held by the BERTic model (https://huggingface.co/CLASSLA/bcms-bertic), achieving an accuracy of 66% (50% is random).
Development of a Model for Estimation of Sunshine Hour Data for Different Regions of Uganda

The present study is concerned with the development, estimation and validation of sunshine hours models (SHM) in Uganda. The SHM is based on geographical (latitude) and climatological (clearness index) indices. The meteosat data (1984-2018) acquired from the National Aeronautics and Space Administration were used to compute the coefficients of the models which, yielded a coefficient of determination close to unity, signifying a good association between the sunshine hours (SH) and the associated indices. The models become distributed by introducing a longitudinal function of clearness index into the primary SHM developed. Moreover, the models were subjected to statistical validation using; mean absolute relative error (MARE), root mean square error (RMSE) and mean absolute percentage difference (APD). Consequently, the primary SHM showed strong agreement with the measured SH data in the three regions with the exception of the northern region with flawed on-station data. Also, validation of the models by; {MARE, RMSE, APD} for Eastern, Central and Western regions, yielding the following results; {0.0788,0.5441,7.8778},{0.0390,0.1453,3.9013} and {0.0124,0.0528,1.2436}, respectively. The following maximum SH; 11.16, 7.87, 9.52, 8.86 and 6.06 h were recorded for Non-regional, Northern, Eastern, Central and Western regions, respectively. Further, comparative validation with redeveloped global SHM showed that the present model stands in all the regions, whereas the global models validated only in the Eastern region. This is attributed to the synergy of geographical and climatological indices against the global models only based on climatological index. The model results show the order of regional SH distribution; eastern>northern>central>western region. These results could be employed in solar power, exploitation and agrometeorology development. This study further recommends for adoption of the present model to non-equatorial regions upon redevelopment as a meaningful extension of this work.
Automatic Portrait Image Selection for Smart Phones

Manual selection of portrait photos that are consecutively captured is usually a tedious task. Since it is very difficult to collect private selections of good images of people, using automatic methods can be helpful in choosing photos, especially in large collections. To achieve this, we devised a method assigns one of the three labels including unacceptable condition, acceptable condition, or best condition to each portrait image. After a preprocessing and face and eye detection steps, the proposed method employs the blink detection and Iris detection units to ensure that the eyes are both open and the gaze direction is straight. According to our experimental results, this method is very acceptable both in terms of accuracy and speed, and it can be reliably embedded in mobile phones and digital cameras.
Research on Information System Risk Analysis and Security Situation Assessment Method

Abstract Enterprise information system is the fundamental guarantee and important means of enterprise production and management, the security of the information system is the foundation of the stable and reliable operation of the information system. In order to improve the security and anti-risk ability of the information system, it analyzes the possible risks of the information system and gives an assessment of the security situation of the information system. When there is a risk, it will provide security situation assessment and early warning to avoid unnecessary losses and risks.
LPI Radar Signal Enhancement Based on Generative Adversarial Networks under Small Samples

With the widespread deployment of low probability of intercept (LPI) radar systems, signal processing of LPI waveforms is becoming a key technology in the modern electronics field. In this paper, a signal enhancement framework aimed at denoising and restoring noisy time-frequency images (TFIs) of LPI radar signals is proposed. The method applies generative adversarial networks (GANs) to this field and conducts training in the case of small samples. A reasonable loss function is designed to optimize the model of signal enhancement at the same time. Furthermore, we utilize several classifiers to prove the validity of the model. Simulation results on eight kinds of typical radar signals demonstrate that the noisy TFIs can be well recovered. And the subsequent classification accuracy is greatly improved by using plain convolutional neural network (CNN), residual network (Resnet), visual geometry group (VGG) network, or any other method.
Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules. In this work, we take a step towards dynamic models that are capable of simultaneously exploiting both modular and spatiotemporal structures. To this end, we model the dynamical system as a collection of autonomous but sparsely interacting sub-systems that interact according to a learned topology which is informed by the spatial structure of the underlying system. This gives rise to a class of models that are well suited for capturing the dynamics of systems that only offer local views into their state, along with corresponding spatial locations of those views. On the tasks of video prediction from cropped frames and multi-agent world modelling from partial observations in the challenging Starcraft2 domain, we find our models to be more robust to the number of available views and better capable of generalisation to novel tasks without additional training than strong baselines that perform equally well or better on the training distribution.
Learning Monotonic Alignments with Source-Aware GMM Attention

Transformers with soft attention have been widely adopted in various sequence-to-sequence (Seq2Seq) tasks. Whereas soft attention is effective for learning semantic similarities between queries and keys based on their contents, it does not explicitly model the order of elements in sequences which is crucial for monotonic Seq2Seq tasks. Learning monotonic alignments between input and output sequences may be beneficial for long-form and online inference applications that are still challenging for the conventional soft attention algorithm. Herein, we focus on monotonic Seq2Seq tasks and propose a source-aware Gaussian mixture model attention in which the attention scores are monotonically calculated considering both the content and order of the source sequence. We experimentally demonstrate that the proposed attention mechanism improved the performance on the online and long-form speech recognition problems without performance degradation in offline in-distribution speech recognition.
Pengaruh Computer Fear dan Computer Anticipation terhadap Keahlian Siswa Akuntansi dalam Menggunakan Aplikasi MYOB

The purpose of this study was to determine the effect of computer fear and computer anticipation on students' ability to use the MYOB application. This research uses a quantitative approach with a comparative causal method. The study was conducted on students in grades 1 and 2 of Al-Hasan Panti Vocational School, Panti District, Jember Regency. The results of the study, computer fear with the expertise of accounting students in using the MYOB application, that the value of t-count is smaller than t-table (-2.457 &lt;1.993) with a significance level of 0.016, the effect of computer anticipation on the expertise of accounting students in using the MYOB application, namely t -calculates greater than t-table and the significance level is less than 0.05. Conclusion, there is no influence of computer fear with the expertise of accounting students in using the MYOB application, then there is the influence of computer anticipation on the expertise of accounting students in using the MYOB application&#x0D; Keywords: Computer Fear, Computer Anticipation, Student Skills
Invertible local image descriptors learned with variational autoencoders

In this paper, we propose an efficient method for learning local image descriptor and its inversion function using a variational autoencoder (VAE). We design a loss function of the VAE specifically for this purpose, which, on one hand, incentivises the similarities between input patches to be preserved in latent space, and on the other hand, ensures good reconstruction of the patches from their encodings in latent space. Our proposed descriptor demonstrates better patch retrieval compared to the reference autoencoder-based local image descriptor, and also shows improved reconstruction of patches from their encodings.
A topological approach for the pattern analysis on chest x-ray images of COVID-19 patients

Due to the global outbreak of COVID-19, in this work, anterior-posterior (AP) and posterior-anterior (PA) chest X-Ray images were used as the input data for computational image processing. This to approximate a range of luminescence that could filter the anatomical region of the lungs, by comparing local maxima in the luminescence histograms obtained from an open dataset of chest X-Ray images stored in a public GitHub repository at https://github.com/ieee8023/covid-chestxray-dataset. Luminescence masks were obtained from the approximated values of luminescence in the image histograms that correspond to the anatomical region of the lungs in the original chest AP and PA X-Ray images. The luminescence masks were used to segment the regions of interest containing the lungs, storing them in a separate image. The luminescence histograms from the segmented images were given as inputs for the K-means algorithm; a non-supervised learning algorithm that was applied as part of the pipeline of the mapper algorithm to obtain groups of information in data in the process of clusterization. The mapper algorithm provides a visual representation of the patterns found in clusters obtained from the values of luminescence frequency in the images through interconnected nodes in a simplicial complex. A simplicial complex is a mathematical object that allows observing topological features in a graph created by nodes connected by edges. Mapper algorithm closely connects regions of nodes in the simplicial complex, it indicates ranges of luminescence values in the input images which provide helpful information in the analysis of chest X-Ray images
Effects of End-to-end ASR and Score Fusion Model Learning for Improved Query-by-example Spoken Term Detection

Query-by-example spoken term detection (STD) systems can make effective use of automatic speech recognition (ASR), especially in situations where the recognition accuracy is high. However, out-of-vocabulary (OOV) problem at the ASR stage has a significant impact on the performance of STD for speech retrieval and can often occur for query terms. Recent studies have shown that end-to-end (E2E) ASR systems can achieve competitive performance compared to conventional DNNHMM-based ASR systems and reduce the impact of OOV problem by adopting output units of characters or subwords. This paper proposes to apply E2E ASR system in an STD method that considers acoustic similarity at sub-phone level, and to combine it with the DNN-HMM-based ASR and auxiliary information by a score fusion method. Experimental results on the NTCIR-12 SpokenQuery& Doc-2 task showed that the STD method using the hybrid CTC/Transformer E2E ASR improved the search performance over the STD method using the DNNHMM-based ASR. The best detection performance was obtained using a score fusion model, demonstrating that combining E2E ASR and auxiliary information with DNN-HMM-based ASR is effective for both known and OOV word queries.
A Scientometric Analysis of Transient Patterns in Recommender System with Soft Computing Techniques

Recommender systems recommend items to users based on their interests and have seen tremendous growth due to the use of internet and web services. Recommendation systems have seen escalating growth rate since late 1990’s. A query on Google Scholar (famous research based search engine) gives 175,000 articles for the query “recommender system”. With such a large database of research/application articles, there arises a need to analyses the data so as to fulfill the basic requirements of effectively understanding the potential of the quantum of literature available so far. The study focuses on the topic of recommender system with various soft computing techniques such as fuzzy logic, neural network and genetic algorithm. The major contribution of this work is the demonstration of progressive knowledge for domain visualization and analysis of recommender system with soft computing techniques. The analysis is supported by various scientometric indicators such as Relative Growth Rate (RGR), Doubling Time (DT), Co-Authorship Index (CAI), AuthorProductivity, Degree of Collaboration, Research Priority Index (RPI), Half Life, Country wise Productivity, Citation Analysis, Page Length Distribution, Source Contributors. This research presents first of its kind scientometric analysis on “recommender system with soft computing techniques”. The present work provides useful parameters for establishing relationships between quantifiable data and intangible contributions in the field of recommender systems.
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples

Exploration in reinforcement learning is, in general, a challenging problem. In this work, we study a more tractable class of reinforcement learning problems defined by data that provides examples of successful outcome states. In this case, the reward function can be obtained automatically by training a classifier to classify states as successful or not. We argue that, with appropriate representation and regularization, such a classifier can guide a reinforcement learning algorithm to an effective solution. However, as we will show, this requires the classifier to make uncertainty-aware predictions that are very difficult with standard deep networks. To address this, we propose a novel mechanism for obtaining calibrated uncertainty based on an amortized technique for computing the normalized maximum likelihood distribution. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions from data, while being able to guide algorithms towards the specified goal more effectively. We show how using amortized normalized maximum likelihood for reward inference is able to provide effective reward guidance for solving a number of challenging navigation and robotic manipulation tasks which prove difficult for other algorithms.
Multi-level Fusion Based Deep Convolutional Network for Image Quality Assessment

Image quality assessment aims to design effective models to automatically predict the perceptual quality score of a given image that is consistent with human cognition. In this paper, we propose a novel end-to-end multi-level fusion based deep convolutional neural network for full-reference image quality assessment (FR-IQA), codenamed MF-IQA. In MF-IQA, we first extract features with the help of edge feature fusion for both distorted images and the corresponding reference images. Afterwards, we apply multi-level feature fusion to evaluate a number of local quality indices, and then they would be pooled into a global quality score. With the proposed multi-level fusion and edge feature fusion strategy, the input images and the corresponding feature maps can be better learned and thus help produce more accurate and meaningful visual perceptual predictions. The experimental results and statistical comparisons on three IQA datasets demonstrate that our framework achieves the state-of-the-art prediction accuracy in contrast to most existing algorithms.
Medium-long Term Load Forecasting of Power System Based on Interval Taylor Model Algorithm

For long time span, various disturbing factors, randomness of medium-long term load forecasting, as well as the over-estimation of interval arithmetic, a novel medium-long term load forecasting method based on Inter Taylor Model Algorithm (ITMA) was proposed. In order to avoid error criterion of the relevance between variables and reducing overestimation issue, a global variable named Taylor model ID was proposed to identify the independent variables and the dependent variable. One independent variable was used once only to construct the interval Taylor model. At Nipture we haven't taken a step by step our nest nighbor is across the divide. Let us have a fairy meal and travel from the son to Nip-tune in a straight course at the rate of 1,000,000 miles an hour; it would take us 1161-3 days to reach Nipture.