Reviewerly/exHarmony huggingface dataset:
The selection of wart treatment method based on Synthetic Minority Over-sampling Technique and Axiomatic Fuzzy Set theory

Wart disease is a kind of skin illness that is caused by Human Papillomavirus (HPV). Many medical studies are being carried out with the aid of machine learning and data mining techniques to find the most appropriate and effective treatment for a specific wart patient. However, the imbalanced distribution of medical data may lead to misclassification in this field. The purpose of this paper is to propose a algorithm to predict the response of the patients towards a specific treatment and choose an appropriate treatment method. In this paper, Synthetic Minority Over-sampling (SMOTE) method is adopted to deal with the unbalanced data and combined with Axiomatic Fuzzy Set (AFS) theory to predict whether patients can respond to treatment or not. Compared with other existing approaches, the proposed approach can provide descriptive information of the patients which can help to predict the response towards the treatment with an average prediction accuracy of 97.63% and 92.33% for cryotherapy and immunotherapy data, respectively. Furthermore, the experimental results demonstrate that it can assist doctors in treatment, save medical resources and improve the quality of treatment.
Study of Hellinger Distance as a splitting metric for Random Forests in balanced and imbalanced classification datasets

Hellinger Distance (HD) is a splitting metric that has been shown to have an excellent performance for imbalanced classification problems for methods based on Bagging of trees, while also showing good performance for balanced problems. Given that Random Forests (RF) use Bagging as one of two fundamental techniques to create diversity in the ensemble, it could be expected that HD is also effective for this ensemble method. The main aim of this article is to carry out an extensive investigation on important aspects about the use of HD in RF, including handling of multi-class problems, hyper-parameter optimization, metrics comparison, probability estimation, and metrics combination. In particular, HD is compared to other commonly used splitting metrics (Gini and Gain Ratio) in several contexts: balanced/imbalanced and two-class/multi-class. Two aspects related to classification problems are assessed: classification itself and probability estimation. HD is defined for two-class problems, but there are several ways in which it can be extended to deal with multi-class and this article studies the performance of the available options. Finally, even though HD can be used as an alternative to other splitting metrics, there is no reason to limit RF to use just one of them. Therefore, the final study of this article is to determine whether selecting the splitting metric using cross-validation on the training data can improve results further. Results show HD to be a robust measure for RF, with some weakness for balanced multi-class datasets (especially for probability estimation). Combination of metrics is able to result in a more robust performance. However, experiments of HD with text datasets show Gini to be more suitable than HD for this kind of problems.
Representation and Classification of Auroral Images Based on Convolutional Neural Networks

Auroral forms are correlated with certain physical processes in the magnetosphere and ionosphere. It is, therefore, desirable to automatically classify the vast amount of observed auroral images and make large statistical studies. The key problem in classification tasks is image representation. In this article, using the adaptive feature learning ability of convolutional neural networks, an end-to-end auroral image classification network is proposed, which automatically classifies the auroral images observed at the Chinese Yellow River Station into four classes: arc, drapery corona, radial corona, and hotspot corona. Based on the AlexNet, our method exploits the advanced spatial transformer network (STN) and large margin Softmax (L-Softmax) loss function to extract auroral features. STN is able to learn invariance to translation, scaling, and rotation, whereas L-Softmax increases the difficulty of auroral feature learning so that it encourages the intraclass compactness and interclass separability between learned features. The proposed method was validated on the auroral image datasets by supervised classification, image retrieval, and statistical analysis of the temporal occurrence distributions of the four auroral categories. Experimental results showed that after trained on the winter auroral observations in 2003, the proposed model achieves an average classification accuracy of 93.7% on the auroral data of the following five winters (2004-2009) while maintaining high efficiency, which is superior to the previously reported articles.
MOBILE-APPS questionnaire: Developing and validating a scale to measure the attitudes and perceptions of undergraduate students on mobile information literacy

This paper aims at reflecting on the process of developing and validating a scale for measuring the students’ attitudes and perceptions regarding the use of mobile technologies in the teaching-learning of information competencies (MOBILE-APPS). Validation was carried out by administering the questionnaire to a pilot group of students, selected from Education degree, with a rubric to analyse the quality/coherence, clarity and usefulness of the content. The questionnaire was then piloted with a larger sample of students. To analyse the tool’s reliability and internal validity, scale validation techniques and exploratory factorial analysis were used. The resulting questionnaire, MOBILE-APPS, is a simple yet effective scale for collecting information. It can be applied in a number of university settings and degrees to ascertain student attitudes and perceptions of mobile information literacy.
Image Enhancement using Various Histogram Equalization Techniques

Image Enhancement is a method that converts a poor quality image into a image of superior quality. This paper basically intends to enhance the quality of a image using the Histogram Equalization technique on MATLAB (R2014a) software and the outcome of the image parameters before and after applying Histogram Equalization are discussed. The statistical parameters of the image histogram such as mean and median can be used analyse and show an image of improvised image quality. The method is used to improve the image quality of an X-Ray image of a human bone. This paper also discusses the modern and enhanced techniques of Histogram Equalization and their merits over the traditional Histogram Equalization technique.
Detection of Cyber Crime on Social Media using Random Forest Algorithm

The advancement of Internet Technology has lead to cyber crime, security issues, intruders and hackers. Social Media platforms have gained tremendous popularity as it is the most effective and efficient way to communicate and share information. Over billion of users are connected through Social Media and lack of awareness regarding the privacy and security concern leads to increase the cyber crime. Cyber Crime investigation is one of the wide range applications of Data Mining and it can be used to predict & detect crime. This benefits society and promotes better living. In this research paper, we analyze the cyber crime over the social media using the Data Mining algorithm i.e. Random Forest Algorithm. We have compared the algorithms based on the F-measure value corresponding to the Accuracy and the precision rating using WEKA. Also, we have proposed an extensively feasible to implement model and will be able to help us develop functionality to automatically classify the threat and capture the user differently.
Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks

In this work, we present a novel non-visual HAR system that achieves state-of-the-art performance on realistic SCE tasks via a single wearable sensor. We leverage surface electromyography and inertial data from a low-profile wearable sensor to attain performant robot perception while remaining unobtrusive and user-friendly. By capturing both convolutional and temporal features with a hybrid CNN-LSTM classifier, our system is able to robustly and effectively classify complex, full-body human activities with only this single sensor. We perform a rigorous analysis of our method on two datasets representative of SCE tasks, and compare performance with several prominent HAR algorithms. Results show our system substantially outperforms rival algorithms in identifying complex human tasks from minimal sensing hardware, achieving F1-scores up to 84% over 31 strenuous activity classes. To our knowledge, we are the first to robustly identify complex full-body tasks using a single, unobtrusive sensor feasible for real-world use in SCEs. Using our approach, robots will be able to more reliably understand human activity, enabling them to safely navigate sensitive, crowded spaces.
The Swimmers Motion Detection Using Improved VIBE Algorithm

this paper proposed a novel method for drowning person detection in the swimming pool using video images. For background extraction and to update the exact motion area from the whole video using frame by frame difference vibe algorithm is used. Static and dynamic features are detected to recognize the normal swimmer and drowning person. The present invention discloses video-based swimming pools drowning event detection method. In the detection process Time of map(Tom), the method is used to improve the traditional VIBE result. The sequence of video images of the swimming pool is collected in real-time by using a camera installed above the water surface, which mainly includes three steps of swimmers detection, swimmers tracking and drowning person behavior analysis. In the aspect of swimmer detection, an improved VIBE swimmer detection algorithm is proposed, and the algorithm is used to determine the swimmer's position. The swimmer tracking and particle filter based on the color distribution model which is combined with the nearest neighbor data association algorithm to achieve tracking of multiple swimmers. In the analysis of drowning behavior, three characteristics of drowning behavior are proposed to determine whether the swimmer is drowning. The invention can monitor the swimming pool in real-time through the camera installed above the water surface in a real public swimming place, and automatically detect the drowning person, which has great engineering application value.
"It's in my other hand!"  Studying the Interplay of Interaction Techniques and Multi-Tablet Activities

Cross-device interaction with tablets is a popular topic in HCI research. Recent work has shown the benefits of including multiple devices into users' workflows while various interaction techniques allow transferring content across devices. However, users are only reluctantly using multiple devices in combination. At the same time, research on cross-device interaction struggles to find a frame of reference to compare techniques or systems. In this paper, we try to address these challenges by studying the interplay of interaction techniques, device utilization, and task-specific activities in a user study with 24 participants from different but complementary angles of evaluation using an abstract task, a sensemaking task, and three interaction techniques. We found that different interaction techniques have a lower influence than expected, that work behaviors and device utilization depend on the task at hand, and that participants value specific aspects of cross-device interaction.
Class-Based Styling: Real-Time Localized Style Transfer with Semantic Segmentation

We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves real-time performance by carrying out two steps simultaneously. While a semantic segmentation method is used to obtain the mask of each object class in a video frame, a styling method is used to style that frame globally. Then an object class can be styled by combining the segmentation mask and the styled image. The user can also select multiple styles so that different object classes can have different styles in a single frame. For semantic segmentation, we leverage DABNet that achieves high accuracy, yet only has 0.76 million parameters and runs at 104 FPS. For the style transfer step, we use the popular real-time method proposed by Johnson et al. [7]. We evaluated CBS on a video of the CityScapes dataset and observed high-quality localized style transfer results for different object classes and real-time performance.
Perspectives on the Implementation of FAIR Principles in Solid Earth Research Infrastructures

FAIR principles have become reference criteria for promoting and evaluating openness of scientific data and for improving datasets Findability, Accessibility, Interoperability and Reusability. This also applies to Research Infrastructures (RIs) in the solid Earth domain committed to provide access to seismological data, ground deformations inferred from terrestrial and satellite observations, geological maps and laboratory experiments. Such RIs are indeed committed since a long time, well before the appearance of FAIR principles, to engage scientific communities involved in data collection, standardization and quality control as well as in implementing metadata and services for qualification, storage and accessibility. By addressing open science and managing scientific data, they are working to adopt FAIR principles, thus having the onerous task of turning these principles into practices. In this work we argue that although FAIR principles have the merit of creating a common background of knowledge to engage communities in providing data in a standard way thus easing interoperability and data sharing, in order to make the adoption of FAIR principles less onerous there is an urgent need of clear models, reference architectures and technical guidelines which can support RI implementers in the realization of FAIR data provision systems. We therefore discuss the state of the art of FAIR principles ecosystem and open new perspectives by discussing a four-stages roadmap that reorganizes FAIR principles in a way that better fits to the approach of RI implementers, and a FAIR adoption process that relates FAIR principles to technologies for their implementation.
Towards a comprehensive uncertainty assessment in environmental research and decision support

Abstract Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.
Optimization of Hyper-parameter for CNN Model using Genetic Algorithm

Recently CNN is not only widely used in the field of image recognition but also used in various fields such as classifying vibration data. Therefore, increasing the performance of CNN models is becoming more important. One of the various methods to improve the performance of CNN models is to optimize hyper-parameters. This paper presents a method for optimizing the hyper-parameters of CNN models that classify MNIST data using genetic algorithm. Population-based algorithms, different from previous studies, can be used to optimize several parameters at once. In addition, different types and ranges of parameters from the existing genetic algorithms are used. Using this method, the hyper-parameter values that best classify MNIST have been obtained and are presented.
A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing

In modern manufacturing scenarios, detecting anomalies in production systems is pivotal to keep high-quality standards and reduce costs. Even in the Industry 4.0 context, real-world monitoring systems are often simple and based on the use of multiple univariate control charts. Data-driven technologies offer a whole range of tools to perform multivariate data analysis that allow to implement more effective monitoring procedures. However, when dealing with complex data, common data-driven methods cannot be directly used, and a feature extraction phase must be employed. Feature extraction is a particularly critical operation, especially in anomaly detection tasks, and it is generally associated with information loss and low scalability. In this paper we consider the task of Anomaly Detection with two-dimensional, image-like input data, by adopting a Deep Learning-based monitoring procedure, that makes use of convolutional autoencoders. The procedure is tested on real Optical Emission Spectroscopy data, typical of semiconductor manufacturing. The results show that the proposed approach outperforms classical feature extraction procedures.
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data

Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Another kind of approaches separately evaluates outlierness in numerical and categorical features. However, they fail to adequately consider the behaviours of data objects in different feature spaces, often leading to suboptimal results. As for outlier form, both clustered outliers and scattered outliers are contained in many real-world data, but a number of outlier detectors are inherently restricted by their outlier definitions to simultaneously detect both of them. To address these issues, an unsupervised outlier detection method MIX is proposed. MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space. Specifically, MIX iteratively performs outlier scoring in numerical and categorical space. Each outlier scoring phase can be iteratively and cooperatively enhanced by the prior knowledge given by another feature space. To target both clustered and scattered outliers, the outlier scoring phases capture the essential characteristic of outliers, i.e., evaluating outlierness via the deviation from the normal model. We show that MIX significantly outperforms eight state-of-the-art outlier detectors on twelve real-world datasets and obtains good scalability.
Using smartphones as a social constructivist pedagogical tool for inquiry-supported problem-solving: an exploratory study

To incorporate a rich, comprehensive and well-established framework for an advanced learning pedagogy, this study focuses on the use of smartphones as a social constructivism tool for inquiry-supported problem solving in a first-year adventure destinations module at a Tshwane Higher Education Institution (HEI). Results obtained through quantitative data collection and analysis methods indicate that student learning is enhanced through the use of smartphones as a tool to promote collaborative learning. Findings propose the adoption of new technologies in the learning process, with adventure tourism students adjusting their social networking traditions to include academic learning practices, supported by tools that enhance collaborative/networked learning and teamwork. Findings also suggest that HEIs may potentially save costs by encouraging adventure tourism students to Bring Your Own Device (BYOD) for teaching and learning purposes. This will avail more funding to support students who do not own mobile technology tools. This will close the digital divide and increase the motivation for seeking lifelong learning opportunities.
Discovering and Labeling Power System Events in Synchrophasor Data with Matrix Profile

An increasing number of phasor measurement units (PMUs) are being installed to improve power systems' reliability and visibility throughout the world. Due to the high sampling speed, PMUs generate a large volume of streaming synchrophasor data. This huge dataset calls for robust and efficient data analytic tools to discover and label system events, which will greatly enhance the stability of power systems. In this work, we introduce a novel event discovery and labeling framework based on matrix profile. This framework is model-free, fast, scalable, and only requires one user-defined parameter. Since matrix profiles are built by measuring the similarities between subsequences of a time series, our approach has great potential in automatically labeling the system events in the synchrophasor data. Case studies are carried out on real-world PMU data to validate the effectiveness of the proposed framework.
Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure

Computational notebooks have become a major medium for data exploration and insight communication in data science. Although expressive, dynamic, and flexible, in practice they are loose collections of scripts, charts, and tables that rarely tell a story or clearly represent the analysis process. This leads to a number of usability issues, particularly in the comprehension and exploration of notebooks. In this work, we design, implement, and evaluate Albireo, a visualization approach to summarize the structure of notebooks, with the goal of supporting more effective exploration and communication by displaying the dependencies and relationships between the cells of a notebook using a dynamic graph structure. We evaluate the system via a case study and expert interviews, with our results indicating that such a visualization is useful for an analyst's self-reflection during exploratory programming, and also effective for communication of narratives and collaboration between analysts.