Reviewerly/exHarmony huggingface dataset:
Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.
The Importance of Discount Rate and Trustfulness of A Local Currency for the Development of Local Tourism

Local currencies have been used to achieve local economic, social and environmental goals that are not attained by national currencies. Thus, they can also be applied as a mean of the regional development focused on the development of the local tourism. However, trustworthiness and costs of local currencies have been some of the main impediments in their widely usage. Therefore, discount rates that practitioners (including municipal authorities and governments) of local currencies present for potential users might play a key role to deal with these issues. Within this context, this research intends to assess the relationship between discount rates that potential users demand to use local currencies (a paper-based and a digital) and their trust to these currencies. To hit this target, a questionnaire survey was directed to 407 employees of a regional business in Cieszyn Silesia region and the responses were analyzed by binary logistic regression test in SPSS Statistical Software. The results confirm that potential users with more trust to local currencies have more tendencies to demand higher discount rates. Cultural values, occupational status, age, income levels of respondents and potential roles of governments and local authorities in local currency implementations might be the reasons of these findings.&#x0D;
Sunshine duration in Poland from ground‐ and satellite‐based data

Abstract The aim of the study is to characterise the spatial variability of sunshine duration in Poland. The study used ground‐based and satellite‐based data from 1983 to 2018 multi‐annual period, from the territory of Poland. The ground‐based data include daily sums of sunshine duration from 29 Institute of Meteorology and Water Management—National Research Institute stations and two stations of the Jagiellonian University. Satellite‐based data from the product “SDU ‐ Sunshine Duration, v003” were used with a resolution of 0.05° × 0.05°, coming from the nearest pixel of a given meteorological station. These values were obtained from the SARAH‐2.1 database, published by The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). It was found that the mean annual sums of sunshine duration calculated on the basis of ground‐based data in Poland range from about 1,460–1,500 hr in the south and south‐west of the country (mountain areas) to 1,800–1,830 hr in the north—the region of the eastern Baltic coast. Satellite‐based data are in line with ground‐based data in relation to the dependence of sunshine duration on latitude, that is, the increase in sunshine duration from the south to the north in the summer and in the spring, and the decrease in the winter. In the autumn, the spatial variability based on satellite data is more latitudinal than in the case of ground‐based data. Moreover, in the autumn and winter, the sums of sunshine duration based on satellite data are overestimated and, in the summer, especially since the turn of the 20th and 21st century, they have been underestimated. The performed analysis has shown that there is some conformity of the results, but the accuracy of matching satellite‐based data and ground‐based data depends on the location of the station and is subject to fluctuations in the annual and multi‐annual cycle.
A Simple and Efficient Tensor Calculus

Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A key concern is the efficiency of evaluating the expressions and their derivatives that hinges on the representation of these expressions. Recently, an algorithm for computing higher order derivatives of tensor expressions like Jacobians or Hessians has been introduced that is a few orders of magnitude faster than previous state-of-the-art approaches. Unfortunately, the approach is based on Ricci notation and hence cannot be incorporated into automatic differentiation frameworks like TensorFlow, PyTorch, autograd, or JAX that use the simpler Einstein notation. This leaves two options, to either change the underlying tensor representation in these frameworks or to develop a new, provably correct algorithm based on Einstein notation. Obviously, the first option is impractical. Hence, we pursue the second option. Here, we show that using Ricci notation is not necessary for an efficient tensor calculus and develop an equally efficient method for the simpler Einstein notation. It turns out that turning to Einstein notation enables further improvements that lead to even better efficiency.
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature

Recently, many zero-shot learning (ZSL) methods focused on learning discriminative object features in an embedding feature space, however, the distributions of the unseen-class features learned by these methods are prone to be partly overlapped, resulting in inaccurate object recognition. Addressing this problem, we propose a novel adversarial network to synthesize compact semantic visual features for ZSL, consisting of a residual generator, a prototype predictor, and a discriminator. The residual generator is to generate the visual feature residual, which is integrated with a visual prototype predicted via the prototype predictor for synthesizing the visual feature. The discriminator is to distinguish the synthetic visual features from the real ones extracted from an existing categorization CNN. Since the generated residuals are generally numerically much smaller than the distances among all the prototypes, the distributions of the unseen-class features synthesized by the proposed network are less overlapped. In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features. Extensive experimental results on six benchmark datasets demonstrate that our method could achieve a significantly better performance than existing state-of-the-art methods by ∼1.2-13.2% in most cases.
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool

With the ever-increasing number of pre-processing and classification algorithms, manually selecting the best algorithm and their best hyper-parameter settings (i.e. the best classification workflow) is a daunting task. Automated Machine Learning (Auto-ML) methods have been recently proposed to tackle this issue. Auto-ML tools aim to automatically choose the best classification workflow for a given dataset. In this work we analyse the predictive accuracy and overfit of the state-of-the-art auto-sklearn tool, which iteratively builds a classification ensemble optimised for the user’s dataset. This work has 3 contributions. First, we measure 3 types of auto-sklearn’s overfit, involving the differences of predictive accuracies measured on different data subsets: two parts of the training set (for learning and internal validation of the model) and the hold-out test set used for final evaluation. Second, we analyse the distribution of types of classification models selected by auto-sklearn across all 17 datasets. Third, we measure correlations between predictive accuracies on different data subsets and different types of overfitting. Overall, substantial degrees of overfitting were found in several datasets, and decision tree ensembles were the most frequently selected types of models.
Dynamic programming method in the tasks of optimal labor capital distribution programs

Abstract This article shows the application of the dynamic programming method to the tasks of the optimal distribution of labor resources by region. It discusses possible options for the formulation of this task, describes two models of the optimal distribution of labor resources, provides numerical examples.
Digital education in digital cooperative environments

The globalizing world of international business, the mobility of workplaces and the accelerated technological development have made the educational sphere competitive as well. Consequently, educational institutions and the educational process itself have to come up with new methods to meet the respective challenges. Digitalization of education provides such an option as the education process serves as one of the foundations of the economic prosperity of any country. Our article focuses on the tendencies and phenomena generated by digitalization and introduce its immediate, practical impact on the teaching and learning process. We will emphasize such key competences, which are relevant and crucial for the support of digitalization in the 21st century.
Mobile apps in agriculture and allied sector : An extended arm for farmers

ICTs are boon to the farmers and the introduction of Mobile apps in Agriculture and allied sector has accelerated the pace of technology transfer among the farmers at their fingertips.Information dissemination to the knowledge-intensive agriculture sector is upgraded by mobile-enabled information services and the rapid growth of mobile telephony.Today farmers are receiving diverse facts or information about farming like seeds, crop selection, crop cultivation, weather, fertilizer, pesticides etc. from various resources that are distributed in different locations according to its origin, its processors, producers or vendors using the app.However, due to the inefficacy of field level extension workers to provide information and service to a large segment of farmers in offline mode; the mobile app therein plays a vital role and offers a user-friendly solution to effective management and communication with the farmers.
Formalizing 𝜋-calculus in guarded cubical Agda

Dependent type theories with guarded recursion have shown themselves suitable for the development of denotational semantics of programming languages. In particular Ticked Cubical Type Theory (TCTT) has been used to show that for guarded labelled transition systems (GLTS) interpretation into the denotational semantics maps bisimilar processes to equal values. In fact the two notions are proved equivalent, allowing one to reason about equality in place of bisimilarity.
Detecting Java software similarities by using different clustering techniques

Research on empirical software engineering has increasingly been conducted by analysing and measuring vast amounts of software systems. Hundreds, thousands and even millions of systems have been (and are) considered by researchers, and often within the same study, in order to test theories, demonstrate approaches or run prediction models. A much less investigated aspect is whether the collected metrics might be context-specific, or whether systems should be better analysed in clusters. The objectives of this study are (i) to define a set of clustering techniques that might be used to group similar software systems, and (ii) to evaluate whether a suite of well-known object-oriented metrics is context-specific, and its values differ along the defined clusters. We group software systems based on three different clustering techniques, and we collect the values of the metrics suite in each cluster. We then test whether clusters are statistically different between each other, using the Kolgomorov-Smirnov (KS) hypothesis testing. Our results show that, for two of the used techniques, the KS null hypothesis (e.g., the clusters come from the same population) is rejected for most of the metrics chosen: the clusters that we extracted, based on application domains, show statistically different structural properties. The implications for researchers can be profound: metrics and their interpretation might be more sensitive to context than acknowledged so far, and application domains represent a promising filter to cluster similar systems.
Towards electronic learning features in education 4.0 environment: literature study

This paper presents electronic learning features in Education 4.0 environment. Malaysian government encourages Higher Education Institution (HEIs) to embed technologies in order to prepare future education (Education 4.0). Besides, developing skills among students is important to be more adaptable in changing the environment. Current HEIs using Learning Management System (LMS) has lack of interactive features and non-personalized learning. Therefore, this article set out to analyze the existing literature on e-learning practices in Education 4.0 and to propose e-learning features suits in Education 4.0 environment. Guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) review method, a systematic review of the Scopus 24 related studies. Further review of these articles resulted in seven principles of e-learning features based on the constructivism principle – Self-regulation, Personal perspective, Experiential learning, Social learning, Learning Community, Creating and Sharing Knowledge, Structure and layout which can be used to upgraded and redesigned LMS.
Dairy Goat Image Generation Based on Improved-Self-Attention Generative Adversarial Networks

The lack of long-range dependence in convolutional neural networks causes weaker performance in generative adversarial networks(GANs) with regard to generating image details. The self-attention generative adversarial network(SAGAN) use the self-attention mechanism to calculate the correlation coefficient between feature vectors, which improves the global coherence of the network. In this paper, we put forward an improved-self-attention GANs(Improved-SAGAN) to improve the method for calculating correlation in the SAGAN. We can better measure the correlation between features by normalizing the feature vectors to eliminate as many errors caused by noise as possible. As the network learns the global information by calculating the correlation coefficient between all features, it can make up for the defects of local receptive field in the convolution network. We replace the conventional one-hot label with multi-label to obtain more supervised information for generative adversarial networks. We generate dairy goat images based on auxiliary condition generative adversarial network(ACGAN) incorporating the normalized self-attention mechanism and prove that images generated under multi-label are of higher quality than images generated under one-hot label. The generative results of different networks on the public dataset are compared by the inception score and FID evaluation algorithms, and we propose a new evaluation algorithm called SSIM-Mean to measure the quality of generated dairy goat images to further verify the effectiveness of the improved-self-attention GANs.
Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition

Deep learning approaches are now a popular choice in the field of automatic emotion recognition (AER) across various modalities. Due to the high costs of manually labeling human emotions however, the amount of available training data is relatively scarce in comparison to other tasks. To facilitate the learning process and reduce the necessary amount of training-data, modern approaches therefore often rely on leveraging knowledge from models that have already been trained on related tasks where data is available abundantly. In this work we introduce a novel approach to transfer learning, which addresses two shortcomings of traditional methods: The (partial) inheritage of the original models structure and the restriction to other neural network models as an input source. To this end we identify the parts in the input that have been relevant for the decision of the model we want to transfer knowledge from, and directly encode those relevant regions in the data on which we train our new model. To validate our approach we performed experiments on well established datasets for the task of automatic facial expression recognition. The results of those experiments are suggesting that our approach helps to accelerate the learning process.
Video-Based Vehicle Counting for Expressway: A Novel Approach Based on Vehicle Detection and Correlation-Matched Tracking Using Image Data from PTZ Cameras

Vehicle counting plays a significant role in vehicle behavior analysis and traffic incident detection for established video surveillance systems on expressway. Since the existing sensor method and the traditional image processing method have the problems of difficulty in installation, high cost, and low precision, a novel vehicle counting method is proposed, which realizes efficient counting based on multivehicle detection and multivehicle tracking. For multivehicle detection tasks, a construction of the new expressway dataset consists of a large number of sample images with a high resolution (1920 × 1080) captured from real-world expressway scenes (including the diversity climatic conditions and visual angles) by Pan-Tilt-Zoom (PTZ) cameras, in which vehicle categories and annotation rules are defined. Moreover, a correlation-matched algorithm for multivehicle tracking is proposed, which solves the problem of occlusion and vehicle scale change in the tracking process. Due to the discontinuity and unsmooth of the trajectories that occurred during the tracking process, we designed a trajectory optimization algorithm based on least square method. Finally, a new vehicle counting method is designed based on the tracking results, in which the driving direction information of the vehicle is added in the counting process. The experimental results show that the proposed counting method in this research can achieve more than 93% accuracy and an average speed of 25 frames per second in expressway video sequence.
Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure

Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project.
Blockchain Adoption Model for the Global Banking Industry

Blockchain has become the new hype term in the business world for the last decade.Due to the new technology's characteristics and innovative applications, it is being adopted globally in a wide number of industries including the banking industry, yet no adoption model is provided to guide this process.This research aims to contribute to facilitating the successful adoption and implementation of the blockchain new technology in the banking industry.Building on the assumption that the blockchain's adoption in banking will be directed by the regulations and best practices guidelines of the global banking regulatory bodies and practitioner, this research asks: What is the blockchain adoption model for the banking industry?The currently available official documents of the regulatory bodies, practitioners, and research bodies were collected, text mined and analysed, based on the adoption factors identified in the literature review and investigating the adoption factors' importance.This research was able to find three categories of adoption factors: supporting, hindering and circumstantial, identify a new adoption factor and establish the factors' importance.As a result, an adoption model for blockchain technology in the banking industry from an institutional perspective is proposed.Based on this, it is recommended to carry further research on applying the proposed model at banks adopting the new technology to study its fitness.
On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat Window Filter

The problem of computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has received significant attention for a long time. The first stage of sFFT is hashing the frequency coefficients into $B(\approx {K})$ buckets named frequency bucketization. The process of frequency bucketization is achieved through the use of filters: Dirichlet kernel filter, aliasing filter, flat filter, etc. The frequency bucketization through these filters can decrease runtime and sampling complexity in low dimensions. It is a hot topic about sFFT algorithms using the flat filter because of its convenience and efficiency since its emergence and wide application. The next stage of sFFT is the spectrum reconstruction by identifying frequencies that are isolated in their buckets. Up to now, there are more than thirty different sFFT algorithms using the sFFT idea as mentioned above by their unique methods. An important question now is how to analyze and evaluate the performance of these sFFT algorithms in theory and practice. In this paper, it is mainly discussed about sFFT algorithms using the flat filter. In the first part, the paper introduces the techniques in detail, including two types of frameworks, five different methods to reconstruct spectrum and corresponding algorithms. We get the conclusion of the performance of these five algorithms, including runtime complexity, sampling complexity and robustness in theory. In the second part, we make three categories of experiments for computing the signals of different SNR, different $N$ , and different $K$ by a standard testing platform and record the run time, percentage of the signal sampled, and $L_{0},L_{1},L_{2}$ error both in the exactly sparse case and general sparse case. The result of experiments is consistent with the inferences obtained in theory. It can help us to optimize these algorithms and use them correctly in the right areas.