Reviewerly/exHarmony huggingface dataset:
Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration.
smplot: An R Package for Easy and Elegant Data Visualization

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called "smplot" for easy and elegant data visualization. The R package "smplot" generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).
A Hybrid GAN-Based Approach to Solve Imbalanced Data Problem in Recommendation Systems

With the advent of information technology, the amount of online data generation has been massive. Recommendation systems have become an effective tool in filtering information and solving the problem of information overload. Machine learning algorithms to build these recommendation systems require well-balanced data in terms of class distribution, but real-world datasets are mostly imbalanced in nature. Imbalanced data imposes a classifier to focus more on the majority class, neglecting other classes of interests and thus hindering the predictive performance of any classification model. There exist many traditional techniques for oversampling minority classes. Still, generative adversarial networks (GAN) have been showing excellent results in generating realistic synthetic tabular data that keeps the probability distribution of the original data intact. In this paper, we propose a hybrid GAN approach to solve the data imbalance problem to enhance recommendation systems’ performance. We implemented conditional Wasserstein GAN with gradient penalty to generate tabular data containing both numerical and categorical values. We also augmented auxiliary classifier loss to enforce the model to explicitly generate data belonging to the minority class. We designed the discriminator architecture with the concept of PacGAN to receive m-packed samples as input instead of a single input. This inclusion of the PacGAN architecture eliminated the mode collapse problem in our proposed model. We did a two-fold evaluation of our model. Firstly based on the quality of the generated data and secondly on how different recommendation models perform using the generated data compared to original data.
Knowing Your Student: Targeted Teaching Decision Support Through Asymmetric Mixed Reality Collaborative Learning

The Collaborative Virtual Environments (CVEs) created by Mixed Reality (MR) technologies have been classified as symmetric and asymmetric CVEs, where the latter aim to provide different authorities for different collaborator roles utilizing heterogeneous techniques that cover the full gamut of Milgram’s Mixed Reality continuum. The Light Field Display (LFD), as a new type of MR display that generates auto-stereoscopic viewing experience without head-mounted devices, has been incorporated with Augmented Reality (AR) and Virtual Reality (VR) headsets to create remote and co-located asymmetric collaborative environments. In previous asymmetric CVE research, LFDs were adapted to simultaneously render multi-contents for multiple students to lower down average device costs for the MR vet training. However, multiple students sharing one LFD to interact with the teacher may weaken the teacher’s understanding of individual student’s current learning progress, which may make teaching decisions even harder. Therefore, this paper presents an enhanced solution that supports teaching decisions targeted at each student without increasing the device costs. The context-aware LFD student clients, which render a dynamic viewing zone for each student by face encoding tracking, are implemented and applied for anti-cheat quiz support. By synchronizing each student’s tracking data with a Local Area Network (LAN) middleware, the AR teacher client can distinguish different students to in-situ superimpose the quiz progress and targeted-explainable teaching decision support over each corresponding student’s head. Ten University vet/anatomy teachers participated in the remote expert review study to provide professional feedback. According to the questionnaire results, they think the designed collaborative learning tool will be helpful for both teachers and students.
Virtual and Space Tourism as New Trends in Travelling at the Time of the COVID-19 Pandemic

The first European COVID-19 infection was recorded in February 2020, and Poland followed in mid-March. Restrictions were imposed on traveling between states and using public space. These movement restrictions forced a search for new, often innovative, forms of tourism. Google Earth virtual reality (VR), Google Street View, and the Chernobyl VR Project are just some of the selected opportunities to create virtual tours. Different activities using VR mean that people can experience the illusion of travelling in time and space, outside of their everyday surroundings, in a digitally constructed three-dimensional (3D) environment, for cognition or entertainment. Therefore, this study aimed to present virtual and space tourism as new traveling trends during various crises, such as health, economic, etc. A diagnostic survey with a developed questionnaire was conducted in June and July 2021 in Poland. A total of 564 fully answered responses were collected from randomly selected respondents. We found that around 82% of Polish people were aware of VR technology, and 70% believed that new technologies determine VR tourism development. VR presents the possibility of travelling to places that no longer exist in their original form, but have been reconstructed only in VR. Around 75% of the respondents agreed that VR tourism plays an essential role in tourism promotion in Poland and throughout the world. Moreover, VR and augmented tourism lets us visit fictitious and dangerous, politically restricted, and geographically as well as economically difficult destinations. For example, our results revealed that many people want to experience North Korea, the USA, Antarctica, Syria, etc. At the same time, people recommended the NASA space station as a visiting destination using VR and augmented reality. VR offers an alternative form of tourism during crises and pandemics such as COVID-19. We found over 26% of the respondents were satisfied with contemporary tourists’ cognitional needs during VR sightseeing. More than 87% of the respondents believed that VR tourism cannot substitute real-world tourism in the long run. However, VR tourism will be more beneficial for developing countries facing difficulties in economic aspects, and easier than attaining visas to enter developed countries. Furthermore, virtual sightseeing may also constitute an alternative for people who are disabled or sick, and who cannot undertake the effort of active tourism and explore tourist resources of the world on their own.
Blockchain and Cyberphysical Systems

This theme issue will elaborate on the opportunities, challenges, and solutions to be offered by combining blockchain
An Empirical Evaluation of Asymmetric Synchronous Collaboration Combining Immersive and Non-Immersive Interfaces Within the Context of Immersive Analytics

Collaboration is an essential part of data analysis, allowing multiple users to combine their expertise and to debate about the interpretation of data discoveries using their contextual knowledge. The design of collaborative interfaces within the context of Immersive Analytics remains challenging, particularly due to the various user-centered characteristics of immersive technologies. In this article, we present the use case of a system that enables multiple users to synchronously explore the same data in a collaborative scenario that combines immersive and non-immersive interfaces in an asymmetric role setup. Such a setup allows for bridging the gap when applying heterogeneous display and interaction technologies, enabling each analyst to have an independent and different view of the data, while maintaining important collaborative aspects during the joint data exploration. We developed an immersive VR environment (head-mounted display, 3D gestural input) and a non-immersive desktop terminal (monitor, keyboard and mouse) centered around spatio-temporal data exploration. Supported through a real-time communication interface, synchronous collaborative features are integrated in both interfaces, facilitating the users in their ability to establish a shared context and to make spatio-temporal references. We conducted an empirical evaluation with five participant pairs (within-subject design) to investigate aspects of usability, user engagement, and collaboration during a confirmative analysis task. Synthesis of questionnaire results in combination with additional log file analysis, audio activity analysis, and observations, revealed good usability scores, high user engagement, as well as overall close and balanced collaboration of enthusiastic pairs during the task completion independent of their interface type, validating our system approach in general. Further supported through the self-constructed Spatio-Temporal Collaboration Questionnaire, we are able to contribute with discussion and considerations of the presented scenario and the synchronous collaborative features for the design of similar applications.
A Study on Artificial intelligence with Machine learning and Deep Learning Techniques

The density of an object varies with temperature and pressure.This variation is generally smaller for solids and liquids, bu t too much for gases.As the pressure on an object increases The volume of the material decreases and its density increases.Increasing the temperature of an object (with some exceptions) decreases its density by increasing its volume.In most materials, as the base of the liquid heats up, the density o f the heated liquid decreases, causing heat to condense from the bottom up, it will rise compared to dense unheated materials.The interaction of the density of an object is sometimes called its specific volume, which is sometimes used in thermodynamics.Density is an increase in the size of an object that does not increase its density.Rather it increases its mass.Water density also depends on the purity of water.Pure water is less den se as compared to the saline water.The decrease in the purity of water increases the density of water.The seawater is denser than pure water.In the deep oceans, the density of water increases in depths.This change or fluctuation in the density in oceans , produce the currents in the oceans.These currents circulate around the world.When the temperature rises in summers, the seawater becomes less dense.On the rise of temperature, water gets warmer and molecules spread out.When the temperature goes down in winters, the seawater becomes denser as most of the chemicals pres ent in the seawater become denser.
A hybrid genetic algorithm, list-based simulated annealing algorithm, and different heuristic algorithms for the travelling salesman problem

Abstract The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued with the GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances.
A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm

Fuzzy inference systems, referring to a system that works on fuzzy sets, have been used in many areas such as classification, information order, especially in the field of forecasting. Because fuzzy inference systems are based on certain rules, determining these rules is the most important problem of many well-known fuzzy inference systems in the literature. To overcome this problem, the Type-1 fuzzy regression functions approach uses fuzzy functions instead of relations, unlike many fuzzy inference systems that operate on a rule base and establish a compound relation between the input and output of the system. One of the most important success criteria of Type-1 fuzzy regression functions is the type of clustering method. The Gustafson Kessel clustering algorithm has no limitations unlike the fuzzy clustering algorithm and can recognize ellipsoidal clusters. In this study, the Gustafson Kessel clustering algorithm is used instead of the fuzzy clustering algorithm and thus the membership values of the input set are obtained with the Gustafson Kessel clustering algorithm in the structure of the fuzzy regression functions approach. The analysis results show that the forecasting performance is increased considerably with the use of the Gustafson Kessel clustering algorithm.
Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting

Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.
Dimensions of Digital Literacy in the 21st Century Competency Frameworks

The Information and Knowledge Society demands the development of skills for the critical and responsible consumption and use of technology for leisure, personal, professional, academic development, and citizen participation. The international frameworks of “21st Century Competences” underline the importance of digital competence as the axis to enhance the rest of competences. This key competence goes beyond the operational use of technological tools and applications and has been studied from different approaches. This research explores digital literacy in eight international frameworks from different institutions and initiatives: UNESCO, European Union, OECD, ATCS, P21, NETS, NAEP, and Engauge, from which a content analysis is performed, and where the areas of scope of the competencies and the relationships between the different proposals are explored. The findings contribute to the understanding of an integrated approach to digital literacy, where six dimensions are identified: critical, cognitive, operational, social, emotional, and projective. Three dimensional profiles are also identified that point towards the critical use of technology, the appropriation of technology in daily life and social innovation, which invites a rethink towards digital literacy from a multi and interdimensional vision.
Using a small number of training instances in genetic programming for face image classification

Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods.
Occlusion-Aware Video Object Inpainting

Conventional video inpainting is neither object-oriented nor occlusion-aware, making it liable to obvious artifacts when large occluded object regions are inpainted. This paper presents occlusion-aware video object inpainting, which recovers both the complete shape and appearance for occluded objects in videos given their visible mask segmentation.To facilitate this new research, we construct the first large-scale video object inpainting benchmark YouTube-VOI to provide realistic occlusion scenarios with both occluded and visible object masks available. Our technical contribution VOIN jointly performs video object shape completion and occluded texture generation. In particular, the shape completion module models long-range object coherence while the flow completion module recovers accurate flow with sharp motion boundary, for propagating temporally-consistent texture to the same moving object across frames. For more realistic results, VOIN is optimized using both T-PatchGAN and a new spatio-temporal attention-based multi-class discriminator.Finally, we compare VOIN and strong baselines on YouTube-VOI. Experimental results clearly demonstrate the efficacy of our method including inpainting complex and dynamic objects. VOIN degrades gracefully with inaccurate input visible mask.
Smart-Sleeve: A Wearable Textile Pressure Sensor Array for Human Activity Recognition

Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify performed activities in this work. Additionally, a dataset including 18 activities, performed by 14 subjects in 10 repetitions, is generated. The Smart-Sleeve is evaluated over six classical machine learning classifiers (support vector machine, k-nearest neighbor, logistic regression, random forest, decision tree and naive Bayes) and a convolutional neural network model. For classical machine learning, a new normalization approach is proposed to overcome signal differences caused by different body sizes and statistical, geometric, and symmetry features are used. All classification techniques are compared in terms of classification accuracy, precision, recall, and F-measure. Average accuracies of 82.02% (support vector machine) and 82.30% (convolutional neural network) can be achieved in 10-fold cross-validation, and 72.66% (support vector machine) and 74.84% (convolutional neural network) in leave-one-subject-out validation, which shows that the Smart-Sleeve and the proposed data processing method are suitable for human activity recognition.
Programmable low-threshold optical nonlinear activation functions for photonic neural networks

We experimentally demonstrate two types of programmable, low-threshold, optically controlled nonlinear activation functions, which are challenging to realize in photonic neural networks (PNNs). These devices rely on on-chip integrated Ge-Si photoelectric detectors and silicon electro-optical switches, and they generate rectified linear unit (ReLU) or sigmoid functions with arbitrary slopes without additional electrical processing. Both devices function at an extremely low threshold of 0.2 mW. The embedding of these nonlinear activation functions into convolutional neural networks facilitates the attainment of high inference accuracies of up to 95% when applied to Modified National Institute of Standards and Technology (MNIST) handwritten digit-classification tasks. The devices are suitable for low-power PNNs with an arbitrary number of propagation layers in photonic-computing chips.