Reviewerly/exHarmony huggingface dataset:
Text Classification Using Machine Learning and Deep Learning Models

In all applications where data plays an important role such as universities, business, research institutions, technology intensive companies and government funding agencies, maintaining irregular data is a big challenge. For an entity (object, place, thing), most of the data is in irregular form. Till now data analytics or text mining examine the entity relationships in a dataset to obtain significant patterns that reflects the information that is there in the dataset. This information is used in making decisions. “Text analytics translates text into numbers and numbers” in order makes up the data and assists to detect patterns. If the data is more organized, the better is the analysis, and eventually the decisions would be better. Manual processing of every bit of data is difficult and also to classify the data. This causes intelligent text processing tools to emerge in the area of NLP, to analyze linguistic and lexical patterns. Before mining it is significant to revise and be familiar with the nature of data. With the expanding measure of information and requirement for precision or accuracy automation process is required for the text classification. Another attractive research opportunity is constructing complex “text data models using Deep learning systems” which have the capability to carry out intricate NLP tasks with semantic requirements. Data analytics forms the basis of text classification and it can act as the engine behind information exploration. These results could be used for emergent applications that support decision making processes. These decisions assist human beings to improve resources and give the majority of benefits. Future research includes improved methods for parameter optimization that reflects effective knowledge discovery.
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network

Recently, \cite{frankle2018lottery} demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training and pruning weights. In this paper, we propose (and prove) a stronger \emph{Multi-Prize Lottery Ticket Hypothesis}: \emph{A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i.e., binary weights and/or activation) (prize 3).} \noindent This provides a new paradigm for learning compact yet highly accurate binary neural networks by pruning and quantizing randomly weighted full precision neural networks. These multi-prize tickets enjoy a number of desirable properties including drastically reduced memory size, faster test-time inference, and lower power consumption compared to their dense and full-precision counterparts. Furthermore, we propose an algorithm for finding multi-prize tickets and test it by performing a series of experiments on CIFAR-10 and ImageNet datasets. Empirical results indicate that as models grow deeper and wider, untrained multi-prize tickets start to reach similar (and sometimes even higher) test accuracy compared to their significantly larger and full-precision counterparts that have been weight-trained. With minimal hyperparameter tuning, our binary weight multi-prize tickets outperform current state-of-the-art in binary neural networks.
Comparing general and specialized word embeddings for biomedical named entity recognition

Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used. One common criterion for selecting a word embedding is the type of source from which it is generated; that is, general (e.g., Wikipedia, Common Crawl), or specific (e.g., biomedical literature). Using specific word embeddings for the BioNER task has been strongly recommended, considering that they have provided better coverage and semantic relationships among medical entities. To the best of our knowledge, most studies have focused on improving BioNER task performance by, on the one hand, combining several features extracted from the text (for instance, linguistic, morphological, character embedding, and word embedding itself) and, on the other, testing several state-of-the-art named entity recognition algorithms. The latter, however, do not pay great attention to the influence of the word embeddings, and do not facilitate observing their real impact on the BioNER task. For this reason, the present study evaluates three well-known NER algorithms (CRF, BiLSTM, BiLSTM-CRF) with respect to two corpora (DrugBank and MedLine) using two classic word embeddings, GloVe Common Crawl (of the general type) and Pyysalo PM + PMC (specific), as unique features. Furthermore, three contextualized word embeddings (ELMo, Pooled Flair, and Transformer) are compared in their general and specific versions. The aim is to determine whether general embeddings can perform better than specialized ones on the BioNER task. To this end, four experiments were designed. In the first, we set out to identify the combination of classic word embedding, NER algorithm, and corpus that results in the best performance. The second evaluated the effect of the size of the corpus on performance. The third assessed the semantic cohesiveness of the classic word embeddings and their correlation with respect to several gold standards; while the fourth evaluates the performance of general and specific contextualized word embeddings on the BioNER task. Results show that the classic general word embedding GloVe Common Crawl performed better in the DrugBank corpus, despite having less word coverage and a lower internal semantic relationship than the classic specific word embedding, Pyysalo PM + PMC; while in the contextualized word embeddings the best results are presented in the specific ones. We conclude, therefore, when using classic word embeddings as features on the BioNER task, the general ones could be considered a good option. On the other hand, when using contextualized word embeddings, the specific ones are the best option.
Modelling Agent Policies with Interpretable Imitation Learning

As we deploy autonomous agents in safety-critical domains, it becomes important to develop an understanding of their internal mechanisms and representations. We outline an approach to imitation learning for reverse-engineering black box agent policies in MDP environments, yielding simplified, interpretable models in the form of decision trees. As part of this process, we explicitly model and learn agents’ latent state representations by selecting from a large space of candidate features constructed from the Markov state. We present initial promising results from an implementation in a multi-agent traffic environment.
Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting

The state-of-the-art facial image inpainting methods achieved promising results but face realism preservation remains a challenge. This is due to limitations such as; failures in preserving edges and blurry artefacts. To overcome these limitations, we propose a Symmetric Skip Connection Wasserstein Generative Adversarial Network (S-WGAN) for high-resolution facial image inpainting. The architecture is an encoder-decoder with convolutional blocks, linked by skip connections. The encoder is a feature extractor that captures data abstractions of an input image to learn an end-to-end mapping from an input (binary masked image) to the ground-truth. The decoder uses learned abstractions to reconstruct the image. With skip connections, S-WGAN transfers image details to the decoder. Additionally, we propose a Wasserstein-Perceptual loss function to preserve colour and maintain realism on a reconstructed image. We evaluate our method and the state-of-the-art methods on CelebA-HQ dataset. Our results show S-WGAN produces sharper and more realistic images when visually compared with other methods. The quantitative measures show our proposed S-WGAN achieves the best Structure Similarity Index Measure (SSIM) of 0.94.
Academic Motivation and the Impact of Distance Learning During Lockdown: University Students' Perspective

High academic motivation is regarded as a key to a student’s good academic performance and, consequently, a successful future. The spread of online learning not only provided new opportunities for students and educators, but also stimulated interest in the factors that affect academic motivation for this model of education. The situation of spring 2020, when Russian universities switched to remote learning, provided an opportunity to research the relative value of motivation-influencing factors for the students who normally study offline, but unexpectedly found themselves in a new study environment. In early July, 2020, the authors conducted an opinion survey of students (N = 274) from three Russian universities with the following goals: creating a rating of factors that influence academic motivation, obtaining the respondents’ opinions regarding the amount of time spent on studying and its difficulty during lockdown, monitoring changes in motivation after the transition to remote learning. The obtained data show that most respondents rated intrinsic factors as being most important for their academic motivation, however, such extrinsic factors as teachers and their relations with students also make a considerable impact on motivation. Academic motivation is positively affected by the perceived value of new knowledge and skills, by understanding the role of education for future success, and also by material/mo­netary incentives. The most prominent negative factors are a lack of time and absence of a good study environment. Most respondents stated that transition to remote learning made studying harder and more time-consuming. The lockdown is shown to have had a negative effect on motivation for most students, which indicates that extrinsic factors, primarily, the study environment and the smoothness of communication, play a vital role in academic motivation. These results help to gain a better understanding of academic motivation from the students’ perspective, and could be taken into consideration by educators and administrators when making decisions on the study format and the design of offline and online courses.
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution

Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of Super-Resolution (SR). However, image viewer applications commonly allow users to zoom the images to arbitrary magnification scales, thus far imposing a large number of required training scales at a tremendous computational cost. To obtain a more computationally efficient model for arbitrary-scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details in a Laplacian Frequency Representation. For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels. SR of larger scales is computed by recursion from small scales, which significantly reduces the computational cost. For a full comparison, fixed- and any-scale experiments are conducted using various benchmarks. At fixed scales, ASDN outperforms predefined upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At any-scale, ASDN generally exceeds Meta-SR on many scales.
Realization of the unconventional photon blockade based on a three-wave mixing system

In this paper, the unconventional photon blockade is studied in a three-wave-mixing system with a non-degenerate parametric amplification. A method of only retaining the Fock-state basis in the interference path is used to calculate the optimal analytic conditions of unconventional photon blockade. The numerical results agree well with the analytic conditions, which verifies the validity of this method. Our calculations indicate that the strong photon antibunching can be obtained in the high-frequency mode of the three-wave mixing. And the influence of system parameters on photon blockade is also discussed.
A Comparative Study of Industry 4.0 with Education 4.0

With the growth of the fourth Industrial Revolution or IR 4.0, the major part of the industry is developing at an extreme pace. The encroachment of technology and enhancement of automated systems, including the Cyber-Physical Systems and Robotics alongside the use of Cloud Computing, Machine Learning, Data Science, Augmented & Virtual Reality, Artificial Intelligence and Internet of Things, the industry has started to transform into a hyperconnected world. To comply with the industries, the education sector is led to the revolution in the field of Education or Education 4.0. The use of the automated processes has made the education change from the mainstream methods to the path of how modern education should look like, resulting in the co-relative study with the industry which can help change the entire educational paradigm. The technical advancements during this aeon are to be analyzed and implemented upon. The paper explains the concepts of Education 4.0 alongside the study of the industrial revolution 4.0; hence having a comparative study of the revolutions led in the fields of both education and industry.
Kotak Edu: An Educational Augmented Reality Game for Early Childhood

Abstract Games are a part of children’s development, but there are still a few developers who make games for early childhood ages. The purpose of this research is to create an educational game for early childhood age in order to enhance early childhood knowledge of animal shape and literacy in a relaxed and fun way with augmented reality features. This augmented reality game purpose is media for parents to teach their children while playing. The Scrum method is used in developing this game. The evaluation of this experiment was been done using testing and survey with 30 parents using game experience questionnaire (GEQ) and comparison to similar game. The survey result show that this game is suitable for the parents and childhood children and compared to others game, Kotak Edu had augmented reality as advantage.
Blockchain-based Model for Health Information Exchange: A Case for Simulated Patient Referrals Using an Electronic Medical Record

Abstract Health information exchange (HIE) has been recognized as an effective mechanism to improve service delivery. The benefits are compounded with the introduction of electronic medical records (EMR). Exchanging information between health facilities remain a challenge due to the absence of an integrated health information system and issues pertaining to data integrity. The emergence of blockchain has provided an innovative solution to data sharing challenges in security, transparency and accessibility. This paper aims to develop a framework for health information exchange between health facilities with the use of EMR and blockchain-based network. The EMR was developed with functionalities such as adding and updating patient profile and health records, referring patient to another facility and monitoring patients referred. These functionalities have been simulated using hypothetical test cases. The simulation has demonstrated blockchain-related functions such as registration of health facility in the network, defining consensus rules and adding patient referral transactions in the network. The description of the proposed framework, role of blockchain and key features of the prototype EMR is summarized in the conclusion part.
Hierarchical Semantic Graph Reasoning for Train Component Detection

Recently, deep learning-based approaches have achieved superior performance on object detection applications. However, object detection for industrial scenarios, where the objects may also have some structures and the structured patterns are normally presented in a hierarchical way, is not well investigated yet. In this work, we propose a novel deep learning-based method, hierarchical graphical reasoning (HGR), which utilizes the hierarchical structures of trains for train component detection. HGR contains multiple graphical reasoning branches, each of which is utilized to conduct graphical reasoning for one cluster of train components based on their sizes. In each branch, the visual appearances and structures of train components are considered jointly with our proposed novel densely connected dual-gated recurrent units (Dense-DGRUs). To the best of our knowledge, HGR is the first kind of framework that explores hierarchical structures among objects for object detection. We have collected a data set of 1130 images captured from moving trains, in which 17 334 train components are manually annotated with bounding boxes. Based on this data set, we carry out extensive experiments that have demonstrated our proposed HGR outperforms the existing state-of-the-art baselines significantly. The data set and the source code can be downloaded online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ChengZY/HGR</uri> .
Does Blockchain Facilitate Responsible Sourcing? An Application to the Diamond Supply Chain

Blockchain technology has become widely accepted to demonstrate the provenance of physical goods, but there are open questions about its practical implementation and overall effect on ethical sourcing. In the diamond industry, blockchain enables credibility of the certificate of origin and therefore, allows to charge a premium for responsibly produced goods. Thus, one would expect blockchain to be the enabler of responsible sourcing. Using a stylized economic model, we demonstrate that, counter-intuitively, blockchain implementation may reduce incentives for customers to resell diamonds on the secondary markets and increase the cost of market segmentation. As a result, blockchain implementation could change a retailer's market segmentation strategy by increasing incentives to source from non-responsible suppliers. We also demonstrate that to reduce unintended consequences of blockchain implementation, the social planner should offer blockchain as an option (and not as a requirement) to a retailer. This work shows that caution is needed with wide-scale blockchain implementation. Although it is commonly recognized that blockchain's major application is in enhancing the traceability of durable goods or goods that should have a responsible supplier, we show that, in contrast, when factors of durability and responsibility come together, blockchain may negatively influence the responsibility level.
Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features

This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers.&#x0D;
Homophily outlier detection in non-IID categorical data

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10–28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications remains elusive. In this paper, we present a formal study of this impact by extending the notion of Answers for Codd tables, which has been explored by the database research community for decades, into the field of machine learning. Specifically, we focus on classification problems and propose the notion of Certain Predictions (CP) -- a test example can be certainly predicted (CP'ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of would yield the same prediction. We study two fundamental CP queries: (Q1) checking query that determines whether a example can be CP'ed; and (Q2) counting query that computes the number of classifiers that support a particular prediction (i.e., label). Given that general solutions to CP queries are, not surprisingly, hard without assumption over the type of classifier, we further present a case study in the context of nearest neighbor (NN) classifiers, where efficient solutions to CP queries can be developed -- we show that it is possible to answer both queries in linear or polynomial time over exponentially many possible worlds. We demonstrate one example use case of CP in the important application of data cleaning for machine learning (DC for ML). We show that our proposed CPClean approach built based on CP can often significantly outperform existing techniques in terms of classification accuracy with mild manual cleaning effort.