Reviewerly/exHarmony huggingface dataset:
Super-resolution X-ray imaging with hybrid pixel detectors using electromagnetic source stepping

With increasing demand for high-resolution X-ray images, the super-resolution method allows to estimate a single high-resolution image from several low-resolution images. Hybrid pixel detectors provide high-quality and low-resolution images, which makes them particularly well suited for super-resolution. However, such detectors consist of a limited number of pixels at high cost. Applying super-resolution with hybrid pixel detectors shows that it is a viable method to obtain high-resolution images. The point-spread function of such detectors can be idealised to be 1 pixel, adding no blur into the image making such detectors the ideal choice for the application of super-resolution X-ray imaging. However, there are charge sharing effects between the pixels caused by the energy and impact position of incoming photons. Utilising an X-ray source, which allows magnetic stepping of the X-ray spot, several slightly shifted images can be obtained without requiring mechanical movements. Registering the shifts between individual images with sub-pixel precision allows to estimate a high-resolution image. With repeatable and equally spaced X-ray spot position patterns, sufficient information can be obtained with only a few images. In this paper, we present the application of super-resolution for X-ray imaging using a Pilatus 100K hybrid pixel detector from Dectris Ltd. and a prototype micro-focus X-ray source from Excillum AB. Moreover, we analyse the image quality for applications in X-ray radiography and tomography. Using a sufficient number of low-resolution images allows us to achieve an increase in resolution, without introducing significant blur or artefacts into the image. Here we quantify the effects on the quality of resulting super-resolution images using different methods of image interpolation, interpolation factors, shifts of the sample on the detector, and amount of low-resolution images.
Design of Blockchain System for Protection of Personal Information in Digital Content Trading Environment

The Blockchain, which has attracted attention as the core technology of the fourth industrial revolution, is a form of distributed database as an open ledger. It has the characteristics of integrity, transparency, and confidentiality, and it is currently applied in various fields such as logistics, automobiles, industries, and medical care. Especially in the environment of trading digital content, which is an important asset in the information age, blockchain is attracting attention as a new solution to solve the existing problems(illegal copying, profit distribution, forgery, etc.). However, when the blockchain is integrated into the digital content trading environment, it is difficult to upload the content to the blockchain network due to the limited capacity of the blockchain. In addition, since the integrity and transparency of blockchain is pointed out as a weak point in terms of personal information, it is not suitable for incorporating blockchain into this environment. In this paper, we propose a blockchain, the SBBC(Secret Block based BlockChain), which solves the existing problems of blockchain and digital content trading environment. And we will build a safe and reliable digital content trading environment using WBFT(Weight of authentication Byzantine Fault Tolerance), a consensus algorithm used in the environment.
Heterogeneous Daily Living Activity Learning Through Domain Invariant Feature Subspace

In the practical applications of supervised learning methods, the high cost of obtaining labeled data for learning tasks is a critical problem. One promising research area for solving the problem is transfer learning, which aims to learn a task in target domain by utilizing the training data in a different but related source domain. In this article, we propose a novel heterogeneous transfer learning algorithm called Heterogeneous Daily Living Activity Learning (HDLAL) which derives domain invariant feature representation space from cross-domain data distributions by projecting both domain data into the derived space in the close proximity of each other using Maximum Mean Discrepancy. Within the new feature space, we utilize ensemble classification algorithm to train multi-label classifier using the projected data to predict the labels in the target domain. We show the effectiveness of our approach by experimenting on real-world smart home datasets. The results shows that our HDLAL algorithm outperforms the common direct learning approaches in the context of predicting labels of activities of daily living (ADL).
Keep Calm and Code on Your Phone

Africa lags behind the rest of the world in terms of digital literacy skills with less than one percent of African children leaving school with basic coding skills. One cause of this gap is poor access to equipment such as computers for teaching and learning. Yet, there is a proliferation of smartphones in Africa. Seeking to leverage this opportunity, we developed SuaCode, an online smartphone-based coding course to teach programming fundamentals to Africans. We designed the course to teach coding in a visual, interactive and fun way through the building of a pong game using Processing (a Java-based programming language). In this work, we describe our experience delivering the course online to 30 Ghanaian high school and college students. At the end of the course, 7 of the 30 students completed the first part of the course, building the pong game. The reflection essays from our students showed that they enjoyed the course and coding on a smartphone was not a barrier to completing the assignments. Improvements such as having more mentors and automated feedback on the coding assignments will improve the quality of the course. Given the difficulty in accessing computers in Africa, our work shows that smartphones can be leveraged to effectively introduce students to programming concepts via an online course. We are excited about the results of this pilot and see the potential to scale the course to eventually bring coding skills within arm's reach of millions across Africa, literally into their palms thereby bridging Africa's digital divide.
Study on displacement estimation in low illumination environment through polarized contrast-enhanced optical flow method for polarization navigation applications

Polarization navigation techniques become more and more significant for a wide range of researches and applications. However, most of the polarization navigation sensors can only output the navigation angle information of a carrier based on the angle of polarization (AoP) from the skylight in the daytime; they cannot provide the displacement information in day and night. In this paper, a polarized contrast-enhanced (PCE) optical flow method is proposed to improve the accuracy of displacement estimation and reduce the influence of the ambient brightness. We acquire the degree of linear polarization (DoLP) images during the motion of the carrier in low illumination environment, and the hypothesis of the DoLP continuity is used for the theoretical foundation of this PCE optical flow method under the conditions of parallel light source and small displacements. The displacement calibration on the experimental platform is obtained by using a laser interferometer and spectrometer, and the contrast of the polarimetric image and the calculated polarized optical flow value are used as the validation criteria. Under 0.7198Lx illumination intensity, the cumulative error of the displacement obtained by the PCE method is less than 8 mm and the accuracy of the displacement estimation is almost 2 times higher than that of the traditional Lucas-Kanade(LK) optical flow method. Our experimental results have also shown that under 10°, 10−1, or even 10-2 levels of the standard illumination, the displacement values from the PCE optical flow method are in good agreement with the theoretical values.
Dynamic hand gesture recognition based on textural features

This article proposes an implementation of a dynamic gesture recognition system using different textural descriptors such as the basic Local Binary Patterns (LBP), Rotation Invariant and Uniform LBP (LBP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">riu2</sup> ), Center-Symmetric LBP (CS-LBP) and Edge Histogram Descriptor (EHD). The recognition task is performed using two variants of the Artificial Neural Network (ANN), which are the Multilayer Perceptron (MLP) and the Radial Basis Function neural network (RBF). Experiments were performed on a user-independent database with a simple background where 95.83% recognition rate was achieved. A comparison with previous works shows the efficiency of our system.
The rise of artificial intelligence and the crisis of moral passivity

In “The rise of the robots and the crisis of moral patiency”, John Danaher argues that the rise of AI and robots will dramatically suppress our moral agency and encourage the expression of moral passivity. This discussion note argues that Danaher needs to strengthen his argument by supporting two key assumptions, that (a) AI will otherwise be friendly or neutral (instead of simply destroying humans), and that (b) humans will largely succumb to the temptation of over-relying upon AI for motivation and decision-making in their personal lives.
Picking Work using AR Instructions in Warehouses

In a warehouse, 60% of the costs are attributed to the picking work, which involves collecting products from specific locations on shelves. Improving work efficiency at the picking stage is the most important issue in logistics. Augmented reality (AR) technology is an obvious solution for intuitive instruction and presentation, but it does not lend itself to practical use. In this study, we will analyze the practical barriers of AR in the warehouse. We developed an AR picking system for commercial head mounted displays (HMDs) to investigate practical barriers to using them in picking, improve the HMDs through experiments, and evaluate the improvements.
A decision based unsymmetrical trimmed modified winsorized variants for the removal of high density salt and pepper noise in images and videos

A Decision Based Unsymmetrical trimmed modified Winsorized Variants for the removal of high density salt and pepper noise in images and videos is proposed. The algorithm initially checks for fixed outliers (0 or 255) in the images in a fixed 3x3 neighbourhood. If the processed pixel is an outlier then 4 neighbours are checked for noise. If all 4 neighbours are noisy then mean of 4 neighbours replaces the corrupted pixel. If 4 neighbours are not noisy then number of non noisy pixels is used to replace the corrupted pixel either with unsymmetrical trimmed modified Winsorized median or midpoint. The processed pixel is left unaltered if the pixel does not hold the outliers. The exhaustive experiments on standard database images and videos indicate that the DBUTMWV algorithm has good noise elimination capabilities with excellent information preservation characteristics even at very high noise densities.
Adversarial Examples for Edge Detection: They Exist, and They Transfer

Convolutional neural networks have recently advanced the state of the art in many tasks including edge and object boundary detection. However, in this paper, we demonstrate that these edge detectors inherit a troubling property of neural networks: they can be fooled by adversarial examples. We show that adding small perturbations to an image causes HED [42], a CNN-based edge detection model, to fail to locate edges, to detect nonexistent edges, and even to hallucinate arbitrary configurations of edges. More importantly, we find that these adversarial examples blindly transfer to other CNN-based vision models. In particular, attacks on edge detection result in significant drops in accuracy in models trained to perform unrelated, high-level tasks like image classification and semantic segmentation.
Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems

Recent years have seen a surge in studies developing clinical prediction models based on electronic health records (EHRs) as a result of advances in machine learning techniques and data availability. Yet, validation and implementation of such models in practice are rare, in part because EHR-based clinical prediction models are more difficult to apply to new data sets than results of classical clinical studies due to less controlled clinical environments. In this paper we propose to use the theoretical framework of domain adaptation to analyze the problem of transferring machine-learning-based clinical prediction models across different hospitals and EHR systems. Using the model of Thoral et al. [12] predicting patient-level risk of readmission and mortality after intensive care unit discharge as a case study, we discuss, apply and compare multiple domain adaptation methods. We transfer the model from the original source data set to two new target data sets. We find that, while model performance deteriorates substantially when applying a model developed for one data set to another directly, updating models with training data from the target set and using methods that explicitly model differences in data sets always improves model performance. In a simulation experiment, we show that having access to data or model parameters from another hospital can substantially reduce the amount of data required to build an accurate prediction model for a new hospital. We also show that these performance gains diminish with increasing availability of data from the target hospital.
Comparative Study of RDF and OWL Ontology Languages as Support for the Semantic Web

Web services have been consolidated as technology for the use of the Internet, these services require integration mechanisms, to establish themselves as useful technological tools in the process of information and knowledge management and in dissimilar research activities, development of the organizations. In the process of modeling the Semantic Web, natural language processing algorithms are used, which improve the quality of the information stored in the digital repositories. The metrics of precision and completeness that contain the Weekly Web contribute to corroborate the quality, relevance and relevance of the information held on the internet. The objective of this paper is to compare the RDF and OWL ontology languages, in order to select the language that best fits the use of the Semantic Web, to carry out the process of managing the information, corresponding to the research activities with the greatest impact on the digital repositories of Ecuadorian universities.
Distance-Based Mean Filter for Image Denoising

In this paper, we propose distance-based mean filter (DBMF) to remove the salt and pepper noise. Although DBMF also uses the adaptive conditions like AMF, it uses distance-based mean instead of median. The distance-based mean focuses on similarity of pixels based on distance. It also skips noisy pixels from evaluating new gray value. Hence, DBMF works more effectively than AMF. In the experiments, we test on 20 images of the MATLAB library with various noise levels. We also compare denoising results of DBMF with other similar denoising methods based on the peak signal-to-noise ratio and the structure similarity metrics. The results showed that DBMF can effectively remove noise with various noise levels and outperforms other methods.
Activity Prediction using LSTM in Smart Home

In the near future, smart home systems will play more and more important role to provide comfortable and safe life to human. Today, we already have some realistic way to monitor the daily life of human and recognize their activities by cameras or wireless sensing technology. However, the current research still faces the challenge to the prediction of human activities. In this paper, we analyse the similarity between human activities of daily living and deep neural networks. Inspired by this, the paper proposes a method to predict human activity by deep learning model and evaluates the performance of the approach with real world data. Compared with the traditional algorithm, our approach reaches higher prediction accuracy. In the future, we will try to improve the prediction accuracy and add more kinds of activities.
Artificial intelligence fairness in the context of accessibility research on intelligent systems for people who are deaf or hard of hearing

We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on HCI for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, we discuss the need for inclusion of data from people with disabilities in training sets, the lack of interpretability of AI systems, ethical responsibilities of access technology researchers and companies, the need for appropriate evaluation metrics for AI-based access technologies (to determine if they are ready to be deployed and if they can be trusted by users), and the ways in which AI systems influence human behavior and influence the set of abilities needed by users to successfully interact with computing systems.
Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixedtraffic simulations.The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format.Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation.We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents.With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior.Our main focus is to give a framework to represent realistic roaduser behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions.We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios.In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.
Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.
Synchronous stimulation in the rubber hand illusion task boosts the subsequent sense of ownership on the vicarious agency task

The relationship between sense of agency and sense of ownership remains unclear. Here we investigated this relationship by manipulating ownership using the rubber hand illusion and assessing the resulting impact on self-experiences during the vicarious agency illusion. We tested whether modulating ownership towards another limb using the rubber hand illusion would subsequently influence the illusory experience of ownership and agency towards a similar-looking limb in the vicarious agency task. Crucially, the vicarious agency task measures both sense of agency and sense of ownership at the same time, while removing the confounding influence of motor signals. Our results replicated the well-established effects of both paradigms. We also found that manipulating the sense of ownership with the rubber hand illusion influenced the subsequent vicarious experience of ownership but not the vicarious experience of agency. This supports the idea that sense of agency and sense of ownership are, at least partially, independent experiences.
Security issues in E-Learning system

E-Learning consists of main part of education systems. There are a lot of types and systems in this area. The increase in their number is assessed by an increase in demand for security. Therefor in this paper is presented components and given threats by components of information security. In addition, at the end of this work is proposed main information security tools to protect E-Learning systems.
Class-Discriminative Feature Embedding For Meta-Learning based Few-Shot Classification

Although deep learning-based approaches have been very effective in solving problems with plenty of labeled data, they suffer in tackling problems for which labeled data are scarce. In few-shot classification, the objective is to train a classifier from only a handful of labeled examples in a support set. In this paper, we propose a few-shot learning framework based on structured margin loss which takes into account the global structure of the support set in order to generate a highly discriminative feature space where the features from distinct classes are well separated in clusters. Moreover, in our meta-learning-based framework, we propose a context-aware query embedding encoder for incorporating support set context into query embedding and generating more discriminative and task-dependent query embeddings. The task-dependent features help the metalearner to learn a distribution over tasks more effectively. Extensive experiments based on few-shot, zero-shot and semi-supervised learning on three benchmarks show the advantages of the proposed model compared to state-of-the- art.