Reviewerly/exHarmony huggingface dataset:
Domain-adversarial Network Alignment

Network alignment is a critical task in a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DANA</i> ) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results.
Website Classification from Webpage Renders

In this paper, we present a fast and accurate method for the classification of web content. Our algorithm uses the visual information of the main homepage saved in an image format by means of a full body snapshot. Sliding windows of different sizes and overlaps are used to obtain a large subset of images for each render. For each sub-image, a feature vector is extracted by means of a pre-trained deep learning model. A Extreme Learning Machine (ELM) model is trained for different values of hidden neurons using the large collection of features from a curated dataset of 5979 webpages with different classes: adult, alcohol, dating, gambling, shopping, tobacco and weapons. Our results show that the ELM classifier can be trained without the manual specific object tagging of the sub-images by giving excellent results in comparison to more complex deep learning models. A random forest classifier was trained for the specific class of weapons providing an accuracy of 95% with a F1 score of 0.8.
Control and synchronization of hyperchaos in digital manufacturing supply chain

In this paper, we construct a mathematical model for a supply chain with computer aided digital manufacturing process. We show that the model exhibits various complicated behaviors including chaotic attractors. A stabilizing linear feedback controller is designed to stabilize the supply chain system. We move on to develop an active controller to synchronize two such systems. The design methodologies are based on Lyapunov stability theory. By simulating the model numerically, we observe the chaotic behaviours of the model. We find that the system has chaotic behaviours over a wide range of the values of the system parameters. The numerical results obtained demonstrate the effectiveness of the designed controllers. Our study reveals the interactions between the production and demand, in which the computer aided design plays an important role.
Two families of two-weight codes over $$\mathbb {Z}_4$$

Two infinite families of $$\mathbb {Z}_4$$ -codes with two nonzero Lee weights are constructed by their generator matrices. Their Gray images are nonlinear with the same weight distribution as that of the two-weight binary codes of type SU1 in the sense of (Calderbank, Kantor, 1986).
Architectural Technical Debt: A Grounded Theory

Architectural technical debt in a software-intensive system is driven by design decisions about its structure, frameworks, technologies, languages, etc. Unlike code-level technical debt, which can be readily detected by static analysers, and can often be refactored with minimal efforts, architectural debt is hard to detect, and its remediation is wide-ranging, daunting, and often avoided. The objective of this study is to develop a better understanding of how software development organisations conceptualize their architectural debt, and how they deal with it, if at all. We used a grounded theory method, eliciting qualitative data from software architects and senior technical staff from a wide range of software development organizations. The result of the study, i.e., the theory emerging from the collected data, constitutes an encompassing conceptual theory of architectural debt, identifying and relating concepts such as symptoms, causes, consequences, and management strategies. By grounding the findings in empirical data, the theory provides researchers and practitioners with evidence of which crucial factors of architectural technical debt are experienced in industrial contexts.
Influence of ultramafic rocks and hot springs with travertine depositions on geochemical composition and baseline of soils. Application to eastern central Greece

Ultramafic rocks and hot springs, which occur on almost every continent of our planet, lead to enriching concentration to several elements, including some potentially toxic ones, to the soil, groundwater, fauna and flora, and subsequently are posing potential risks to human health. A geochemical and mineralogical soil survey was undertaken in eastern central Greece, an area characterised by the presence of ultramafic rocks of ophiolite complexes and hot springs, with thermogenic travertine depositions. The research aimed to investigate their influence on soil mineralogy and geochemistry and to establish the soil geochemical baseline values. One hundred four soil samples were collected from 74 sites at depths of 0–25 cm and 25–50 cm. They were found to be enriched in Ca (5.17%), Fe (3.72%), Mg (2.08%), Co (28 mg/kg), Cr (252 mg/kg), Cu (32 mg/kg) and Ni (267 mg/kg) relative to the median values of both Greek and European soil and upper continental crust. Factor analysis revealed three main factors controlling the soil chemical composition reflecting the influence of ultramafic rocks (Ni, Co, Cr and Fe), pedogenesis processes (Ba, Al and K) and thermogenic travertine deposits (Ca and Sr). All the factors showed a significant spatial correlation, with the geological features within the study area. Geochemical baselines were estimated for three sub-areas, based on the three controlling factors, revealed by the multivariance statistical analysis and spatial data. Calculating only one baseline value, based on the total dataset, would lead to incorrect baseline values for several elements. The outcome of the present research could be used in future studies at regions with similar geological features, i.e. serpentine soils and hot springs depositions, providing a comparative example concerning the concentrations of several elements, including some potentially toxic ones to the soil, their host mineral phases and a scientific approach for setting pragmatic geochemical baseline values for pollution assessment.
Extreme value theory for anomaly detection – the GPD classifier

Abstract Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown new classes. It is therefore fundamental to develop algorithms able to distinguish between normal and abnormal test data. In the last few years, extreme value theory has become an important tool in multivariate statistics and machine learning. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresses this problem and achieves competitive performance in specific cases. We show that this algorithm has some theoretical and practical drawbacks and can fail even if the recognition task is fairly simple. To overcome these limitations, we propose two new algorithms for anomaly detection relying on approximations from extreme value theory that are more robust in such cases. We exploit the intuition that test points that are extremely far from the training classes are more likely to be abnormal objects. We derive asymptotic results motivated by univariate extreme value theory that make this intuition precise. We show the effectiveness of our classifiers in simulations and on real data sets.
Fine-grained activities recognition with coarse-grained labeled multi-modal data

Fine-grained human activities recognition focuses on recognizing event- or action-level activities, which enables a new set of Internet-of-Things (IoT) applications such as behavior analysis. Prior work on fine-grained human activities recognition relies on supervised sensing, which makes the fine-grained labeling labor-intensive and difficult to scale up. On the other hand, it is much more practical to collect coarse-grained label at the level of activity of daily living (e.g., cooking, working), especially for real-world IoT systems. In this paper, we present a framework that learns fine-grained human activities recognition with coarse-grained labeled and a small amount of fine-grained labeled multi-modal data. Our system leverages the implicit physical knowledge on the hierarchy of the coarse- and fine-grained labels and conducts data-driven hierarchical learning that take into account the coarse-grained supervised prediction for fine-grained semi-supervised learning. We evaluated our framework and CFR-TSVM algorithm on the data gathered from real-world experiments. Results show that our CFR-TSVM achieved an 81% recognition accuracy over 10 fine-grained activities, which reduces the prediction error of the semi-supervised learning baseline TSVM by half.
Learning Unbiased Zero-Shot Semantic Segmentation Networks Via Transductive Transfer

Semantic segmentation aims to obtain a detailed understanding of images. Deep learning has achieved great advances in semantic segmentation over the past years. In practice, however, the classes do not always correspond to the ones in the training stage. Since it is impractical to collect sufficient labeled data for all classes, zero-shot semantic segmentation has received increasing attentions recently. Although semantic segmentation neural networks can transfer knowledge from seen classes to unseen classes by incorporating the class-level semantic information, it shows a strong bias towards seen classes. In this letter, we propose an easy-to-implement transductive approach to alleviate the prediction bias in zero-shot semantic segmentation. We assume that both source images with full pixel-level labels and unlabeled target images are available for training. The source images are used to build the relationship between visual images and class-level semantic embeddings. On the other hand, the target images are used to alleviate the bias towards seen classes. Comprehensive experiments over the PASCAL dataset clearly demonstrate the effectiveness of our approach.
Differential Privacy Practice on Diagnosis of COVID-19 Radiology Imaging Using EfficientNet

Medical sciences are an important application area of artificial intelligence.Healthcare requires meticulousness in the whole process from collecting data to processing.It should also be handled in terms of data quality, data size, and data privacy.Various data are used within the scope of the COVID-19 outbreak struggle.Medical and location data collected from mobile phones and wearable devices are used to prevent the spread of the epidemic.In addition to this, artificial intelligence approaches are presented by using medical images in order to identify COVID-19 infected people.However, studies should be carried out by taking care not to endanger the security of the data, people, and countries needed for these useful applications.Therefore, differential privacy (DP) application, which was an interesting research subject, has been included in this study.CXR images have been collected from COVID-19 infected 139 and a total of 373 public data sources were used for a diagnostic concept.It has been trained with EfficientNet-B0, a recent and robust deep learning model, and proposal the possibility of infected with an accuracy of 94.7%.Other evaluation parameters were also discussed in detail.Despite the data constraint, this performance showed that it can be improved by augmenting the dataset.The most important aspect of the study was the proposal of differential privacy practice for such applications to be reliable in real-life use cases.With this view, experiments were repeated with DP applied images and the results obtained were presented.Here, Private Aggregation of Teacher Ensembles (PATE) approach was used to ensure privacy assurance.
Concept of Digital Competences in Service Training Systems

The article describes learning space and the concept of implementing digital competences in learning systems. The concept includes the following main phases: creating a digital competence framework for different target groups; creation and filling of training modules; choice of individual learning environment; creating a mobile learning system based on your chosen environment. The system should include web and mobile interfaces, appropriately scoped server subsystems and up-to-date dynamic learning content. Functionality of the system should also provide modern concepts of learning and interaction in the following areas: student-student, student-teacher, teacher-teacher. The implementation of the described concept will contribute to the development of digital competences of different target groups: teachers, unemployed persons, persons in need of retraining and so on. Each of these target groups will have its own, customized digital competence system. The feature of this system is its dynamism, since it changes in content and even content type over time as well as its refinement according to the needs of the audience. The implementation of the concept will build a comprehensive roadmap to support the development of digital competences in learning for many years to come. This will enhance the role of interactive communications and digital learning in the community, attract more and more target groups to ICT, and deploy mobile applications in digital competence training. Creating a digital competence concept is part of the Erasmus plus project "dComFra - Digital Competence Framework for Ukrainian Teachers and Other Citizens". 598236-EPP-1-2018-1-LT-EPPKA2-CBHE-SP. The European Commission's support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
Privacy in Pandemic: Law, Technology, and Public Health in the COVID-19 Crisis

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Channels used to deliver agricultural information and knowledge to smallholder farmers

This study examined the effectiveness of the channels used to deliver agricultural information and knowledge to smallholder farmers in Tanzania. A descriptive cross-sectional design alongside quantitative and qualitative approaches was employed to collect data from 341 respondents. While the data collected through the questionnaire was analysed by using Statistical Product and Service Solutions (SPSS) version 21, the data collected through the interview was analysed by using thematic analysis. The findings suggest that delivery of and access to timely and relevant agricultural information and knowledge, appropriately packaged, is one of the critical problems undermining smallholder farmers’ efforts to increase their production. As a result, most smallholder farmers mainly depend on informal channels. Besides informal channels, farmer groups and demonstration plots are becoming popular channels to deliver and access agricultural information and knowledge. To make a difference in agricultural production, deliberate efforts should be made to enhance the delivery of agricultural information and knowledge.
Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing

One of the major challenges in using distributed learning to train complicated models with large data sets is to deal with stragglers effect. As a solution, coded computation has been recently proposed to efficiently add redundancy to the computation tasks. In this technique, coding is used across data sets, and computation is done over coded data, such that the results of an arbitrary subset of worker nodes with a certain size are enough to recover the final results. The major drawbacks with those approaches are (1) they are limited to polynomial functions, (2) the number of servers that we need to wait for grows with the degree of the model, (3) they are not numerically stable for computation over real numbers. In this paper, we propose Berrut Approximated Coded Computing (BACC), as an alternative approach, as a numerically stable solution, which works beyond polynomial functions computation and with any number of servers. The accuracy of the approximation is established theoretically and verified by simulation. In particular, BACC is used to train a deep neural network on a cluster of servers, which outperforms alternative uncoded solutions in terms of the rate of convergence.
Fair Meta-Learning For Few-Shot Classification

Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine learning algorithm trained based on biased data, however, tends to make unfair predictions. Developing classification algorithms that are fair with respect to protected attributes of the data thus becomes an important problem. Motivated by concerns surrounding the fairness effects of sharing and few-shot machine learning tools, such as the Model Agnostic Meta-Learning [1] framework, we propose a novel fair fast-adapted few-shot meta-learning approach that efficiently mitigates biases during meta-train by ensuring controlling the decision boundary covariance that between the protected variable and the signed distance from the feature vectors to the decision boundary. Through extensive experiments on two real-world image benchmarks over three state-of-the-art meta-learning algorithms, we empirically demonstrate that our proposed approach efficiently mitigates biases on model output and generalizes both accuracy and fairness to unseen tasks with a limited amount of training samples.
Alternatives for facilitating automatic transformation of BIM data using semantic query languages

In the Architecture Engineering and Construction (AEC) industry, Building Information Model (BIM) authoring tools enable the creation of digital representations of buildings. Each tool implements its own building data model, which makes it difficult to achieve the desired interoperability when building data have to be exchanged with other software (e.g., building energy simulation tools). The representation of BIM models through open standards (e.g., IFC) and Semantic Web technologies can facilitate building data transformation in an automated and flexible way. This is achieved by taking advantage of the logical basis of the Resource Description Framework (RDF) data model and queries created in the Semantic Web query languages. The result is a pragmatic mechanism to transform the data from one data domain to another. This article analyses the potential of Semantic Web query languages to facilitate the data transformation of building data through different alternatives. The first contribution is the identification of fourteen data mapping patterns and three cases of data transformation that enable transforming one data model into another, considering the semantic and structural differences between them. The second contribution is the review and comparison of query languages to carry out the transformations through two different alternatives: using SPARQL-Generate and SPARQL Construct queries. And finally, the third contribution is the definition of a metric to assess the complexity of SPARQL queries.
Analysing body composition as compositional data: An exploration of the relationship between body composition, body mass and bone strength

Human body composition is made up of mutually exclusive and exhaustive parts (e.g. %truncal fat, %non-truncal fat and %fat-free mass) which are constrained to sum to the same total (100%). In statistical analyses, individual parts of body composition (e.g. %truncal fat or %fat-free mass) have traditionally been used as proxies for body composition, and have been linked with a range of health outcomes. But analysis of individual parts omits information about the other parts, which are intrinsically co-dependent because of the constant sum constraint of 100%. Further, body mass may be associated with health outcomes. We describe a statistical approach for body composition based on compositional data analysis. The body composition data are expressed as logratios to allow relative information about all the compositional parts to be explored simultaneously in relation to health outcomes. We describe a recent extension to the logratio approach to compositional data analysis which allows absolute information about the total of the compositional parts (body mass) to be considered alongside relative information about body composition. The statistical approach is illustrated by an example that explores the relationships between adults’ body composition, body mass and bone strength.
Grid Anchor Based Image Cropping: A New Benchmark and An Efficient Model

Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available at: https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping-Pytorch.