Reviewerly/exHarmony huggingface dataset:
Partial correlations in compositional data analysis

Partial correlations quantify linear association between two variables while adjusting for the influence of the remaining variables. They form the backbone for graphical models and are readily obtained from the inverse of the covariance matrix. For compositional data, the covariance structure is specified from log ratios of variables, which implies changes in the definition and interpretation of partial correlations. In the present work, we elucidate how results derived by Aitchison (1986) lead to a natural definition of partial correlation that has a number of advantages over current measures of association. For this, we show that the residuals of log-ratios between a variable with a reference, when adjusting for all remaining variables including the reference, are reference-independent. Since the reference itself can be controlled for, correlations between residuals are defined for the variables directly without the necessity to explicitly specify the reference (as it is implicit in the variables that are partialled out). Thus, perhaps surprisingly, partial correlations do not have the problems commonly found with measures of pairwise association on compositional data. They are well-defined between two variables, are properly scaled, and allow for negative association. By design, they are subcompositionally incoherent, but they share this property with conventional partial correlations (where results change when adjusting for the influence of fewer variables). We discuss the reference-dependence of the multiple correlation coefficient as well as partial correlations that are obtained after effective library-size normalizations. We also determine the partial variances and correlations on two previously studied data sets and compare with symmetric balance correlations and the proportionality coefficient.
Aprendizaje en tiempos de aislamiento social: cursos masivos abiertos en línea sobre la COVID-19

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Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features.
An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining

The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional datasets. In addition, the traditional outlier detection method does not consider the frequency of subsets occurrence, thus, the detected outliers do not fit the definition of outliers (i.e., rarely appearing). The pattern mining-based outlier detection approaches have solved this problem, but the importance of each pattern is not taken into account in outlier detection process, so the detected outliers cannot truly reflect some actual situation. Aimed at these problems, a two-phase minimal weighted rare pattern mining-based outlier detection approach, called MWRPM-Outlier, is proposed to effectively detect outliers on the weight data stream. In particular, a method called MWRPM is proposed in the pattern mining phase to fast mine the minimal weighted rare patterns, and then two deviation factors are defined in outlier detection phase to measure the abnormal degree of each transaction on the weight data stream. Experimental results show that the proposed MWRPM-Outlier approach has excellent performance in outlier detection and MWRPM approach outperforms in weighted rare pattern mining.
Data Usage and Access Control in Industrial Data Spaces: Implementation Using FIWARE

In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to create new applications and services. Protecting data goes beyond restricting who can access what resource (covered by identity and Access Control): it becomes necessary to control how data are treated once accessed, which is known as data Usage Control. Data Usage Control provides a common and trustful security framework to guarantee the compliance with data governance rules and responsible use of organizations’ data by third-party entities, easing and ensuring secure data sharing in ecosystems such as Smart Cities and Industry 4.0. In this article, we present an implementation of a previously published architecture for enabling access and Usage Control in data-sharing ecosystems among multiple organizations using the FIWARE European open source platform. Additionally, we validate this implementation through a real use case in the food industry. We conclude that the proposed model, implemented using FIWARE components, provides a flexible and powerful architecture to manage Usage Control in data-sharing ecosystems.
A Framework for Data Sharing between Healthcare Providers using Blockchain

The healthcare data are considered as a highly valuable source of information that can improve healthcare systems to be more intelligent and improve the quality of the provided services.However, due to security and privacy issues, sharing data between healthcare organisations is challenging.This has led to data shortage in the healthcare sector which is considered as a significant issue not only in the Kingdom of Saudi Arabia (KSA) but also worldwide.The primary objective of conducting this paper is to investigate the various factors that enable secure sharing and exchange of healthcare information between different healthcare providers in the KSA.It starts by discussing the current literature and frameworks for managing healthcare data information and the challenges that health providers encounter, particularly when it comes to issues such as data security, patient privacy, and healthcare information exchange.These challenges in managing healthcare data have necessitated the need for implementing a solution that can allow medical providers to have access to updated healthcare information.Attention in the healthcare sector has been drawn to blockchain technology as a part of the solution, especially after the technology was successfully applied in the financial sector to improve the security of financial transactions, particularly involving digital currencies such as Bitcoin.Therefore, a framework based on the blockchain technology has been proposed to achieve the goals of the present research.
A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors

Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features extracted from the filtered signals, the proposed algorithm outperformed previous work on the DaLiAc (Daily Life Activity) and mHealth datasets. The algorithm also represents a significant improvement in terms of computational costs and requires no feature selection and hyper-parameter tuning. The algorithm still showed a robust performance with only two (ankle and wrist) out of the four devices (chest, wrist, hip and ankle) placed on the body (96.8% vs. 97.3% mean accuracy for the DaLiAc dataset). The present work shows that low-complexity models can compete with heavy, inefficient models in classification of advanced activities when designed with a careful upstream inspection of the data.
Privacy Laws, Non-Fungible Tokens, and Genomics

This article analyses the main legal requirements in the California Consumer Protection Act (CCPA), general data protection regulation (GDPR) and the intersections between privacy laws, genomic data and smart contracts (such as fungible and non-fungible tokens (NFTs). The CCPA and GDPR laws impose several restrictions on the storing, accessing, processing and transferring of personal data. This has generated some challenges for lawyers, data processors and business enterprises engaged in blockchain offerings, especially as they pertain to high-risk data sets such as genomic data. The technical features of NFT, distributed storage and wallets to trace and govern genomic (DNA) data sets will allow data donors to establish digital ownership and control in line with privacy laws using ‘programmable privacy smart contracts’. To be legally compliant, the design of blockchain value propositions should include privacy-by-design capabilities in the smart contract coding language itself. This article describes three domains (privacy laws, genomics and NFTs) and begins to explore how data engineers can address the challenges of coding privacy laws, the legal requirements into smart contracts. This current approach focuses on NFTs and genomic data requirements which include the selection of genetic metadata borrowing from developing ERC specifications and their programming logic. Programmable privacy is a unique way to write and design computer code, which can automatically check the legal compliance of the smart contract in a trust-less and decentralised way. We exemplify the approach by describing the conceptual value proposition of Genobank.io, a privacy-preserving genomic data platform.
Supporting Visual Artists in Programming through Direct Inspection and Control of Program Execution

Programming offers new opportunities for visual art creation, but understanding and manipulating the abstract representations that make programming powerful can pose challenges for artists who are accustomed to manual tools and concrete visual interaction. We hypothesize that we can reduce these barriers through programming environments that link state to visual artwork output. We created Demystified Dynamic Brushes (DDB), a tool that bidirectionally links code, numerical data, and artwork across the programming interface and the execution environment - i.e., the artist's in-progress artwork. DDB automatically records stylus input as artists draw, and stores a history of brush state and output in relation to the input. This structure enables artists to inspect current and past numerical input, state, and output and control program execution through the direct selection of visual geometric elements in the drawing canvas. An observational study suggests that artists engage in program inspection when they can visually access geometric state information on the drawing canvas in the process of manual drawing.
SOMHunter for Lifelog Search

We present an extended version of SOMHunter, an interactive retrieval tool designed for known-item and ad-hoc search tasks over image and video datasets. Even though SOMHunter has achieved significant success at the most recent Video Browser Showdown 2020 competition, lifelog datasets and related search tasks constitute a different challenge with specific problems to be addressed. The presented version of SOMHunter integrates functionality required to deal with lots of highly similar images collected in typical lifelogs. Additionally, we employ search in available metadata including date and time filters.
Attitudes towards technology among distance education students: Validation of an explanatory model

Attitudes towards technology are preconceived notions that have an effect on the teaching-learning process and on the academic-professional performance of students, in particular those who use technology as a means of study. This investigation has two objectives: on the one hand, to test the measuring properties (reliability, factorial structure) of an instrument to measure attitudes and, on the other hand, to propose and validate a model that hypothesizes that digital competence and frequency of use of technologies have a positive impact on same. 1251 students of the Madrid Open University participated. It is a non-experimental, explanatory study using structural equation methodology. The results show adequate psychometric properties for the test and good adjustment of the proposed model (χ²=163.91, df=37, p&lt;.001) allowing for further exploration of the relationship between use, skill and attitudes in the distance education context and improving the properties of measuring instruments proposed in Spanish.
Tensor-based multi-view label enhancement for multi-label learning

Label enhancement (LE) is a procedure of recovering the label distributions from the logical labels in the multi-label data, the purpose of which is to better represent and mine the label ambiguity problem through the form of label distribution. Existing LE work mainly concentrates on how to leverage the topological information of the feature space and the correlation among the labels, and all are based on single view data. In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE. Firstly, we introduce the tensor factorization to get the common subspace which contains the high-order relationships among different views. Secondly, we use the common representation and multiple views to jointly mine a more comprehensive topological structure in the dataset. Finally, the topological structure of the feature space is migrated to the label space to get the label distributions. Extensive comparative studies validate that the performance of multi-view multi-label learning can be improved significantly with TMV-LE.
Crossing Variational Autoencoders for Answer Retrieval

Answer retrieval is to find the most aligned answer from a large set of candidates given a question.Learning vector representations of questions/answers is the key factor.Questionanswer alignment and question/answer semantics are two important signals for learning the representations.Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes.However, the alignment and semantics were too separate to capture the aligned semantics between question and answer.In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions.Experiments show that our method outperforms the state-of-theart answer retrieval method on SQuAD. !~( ! ) " ~( " )
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model

Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements over various cross-lingual and low-resource tasks. Through training on one hundred languages and terabytes of texts, cross-lingual language models have proven to be effective in leveraging high-resource languages to enhance low-resource language processing and outperform monolingual models. In this paper, we further investigate the cross-lingual and cross-domain (CLCD) setting when a pretrained cross-lingual language model needs to adapt to new domains. Specifically, we propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features and domain-invariant features from the entangled pretrained cross-lingual representations, given unlabeled raw texts in the source language. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts. Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
Towards Fully 8-bit Integer Inference for the Transformer Model

8-bit integer inference, as a promising direction in reducing both the latency and storage of deep neural networks, has made great progress recently. On the other hand, previous systems still rely on 32-bit floating point for certain functions in complex models (e.g., Softmax in Transformer), and make heavy use of quantization and de-quantization. In this work, we show that after a principled modification on the Transformer architecture, dubbed Integer Transformer, an (almost) fully 8-bit integer inference algorithm Scale Propagation could be derived. De-quantization is adopted when necessary, which makes the network more efficient. Our experiments on WMT16 En&lt;-&gt;Ro, WMT14 En&lt;-&gt;De and En-&gt;Fr translation tasks as well as the WikiText-103 language modelling task show that the fully 8-bit Transformer system achieves comparable performance with the floating point baseline but requires nearly 4x less memory footprint.
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation

Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation.Combining backtranslated data from different sources has led to better results than when using such data in isolation.In this work we analyse the impact that data translated with rule-based, phrasebased statistical and neural MT systems has on new MT systems.We use a real-world low-resource use-case (Basque-to-Spanish in the clinical domain) as well as a high-resource language pair (German-to-English) to test different scenarios with backtranslation and employ data selection to optimise the synthetic corpora.We exploit different data selection strategies in order to reduce the amount of data used, while at the same time maintaining highquality MT systems.We further tune the data selection method by taking into account the quality of the MT systems used for backtranslation and lexical diversity of the resulting corpora.Our experiments show that incorporating backtranslated data from different sources can be beneficial, and that availing of data selection can yield improved performance.
Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT.

This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT. For each task, we first replicate and simplify the current state-of-the-art approach to enhance its model efficiency. We then evaluate our simplified approaches on those three tasks using token embeddings generated by BERT. 12 datasets in both English and Chinese are used for our experiments. The BERT models outperform the previously best-performing models by 2.5% on average (7.5% for the most significant case). All models and source codes are available in public so that researchers can improve upon and utilize them to establish strong baselines for the next decade.
English Learning with Powtoon Animation Video

The use of technology media for teaching young learners was really needed. In daily teaching, teacher rarely use the technology based on media. This happened because it was quite difficult to find the media which was appropriate with the syllabus. Therefore, the aim of this research was to develop PowToon Animation Video on fifth grade students of SD N 3 Banjar Jawa. This research used Design and Development as the method with ADDIE model as the research procedures. This research answered the research questions related to the development of the product. Some steps were conducted to develop the product. In developing the product, it needed analysis, designed the blueprint, asked for expert judgments before the product was developed, evaluated and revised the product. The result of this research was categorized as the best media based on the expert judgment rubrics. So it could be said that PowToon media was effective applied in elementary school, this would directly able to enrich the existing media technology in learning English.
Securing communication by attribute-based authentication in HetNet used for medical applications

Abstract One of the major applications of the Heterogeneous Network (HetNet) is in the healthcare system. Deploying HetNet in healthcare systems enables patients, physicians, and other stakeholders to communicate easily with each other. Due to the large growth in the network’s subscribers, the security of the stored health data became one of the major concerns because unauthorized access to this data may lead to very serious complications, and unreliable transmission of data may lead to fatal risks to the patient’s life. Therefore, taking data integrity into consideration, user authentication has become one of the main factors. However, significant research work has been performed at HetNet’s physical layer to secure communication, but the result of this leads to an increase in hardware components. The increasing hardware components not only costs money but also power consumption. Therefore, this paper presents an alternate way of securing communication in HetNet at the network layer. However, resolving security problems at the network layer increases computational complexity. Nevertheless, earlier, some encryption techniques like identity-based encryption (IBE), symmetric key encryption (SKE), and public-key encryption (PKE) have been utilized for securing data. Due to their own disadvantages, this paper utilizes an attribute-based encryption (ABE) authentication scheme for securing health data in medical applications. With the help of this method, access to the intruders is denied which results in reduced communication overhead. This authentication scheme helps protect the essential information against attacks by the intruders. It includes a third party server that helps to authenticate and store patient’s information. The whole security technique has been written in the form of HLPSL (high-level protocol specification language) codes, and the results are then validated with the help of AVISPA (automated validation of Internet security protocols and applications) tool.