Reviewerly/exHarmony huggingface dataset:
When more is less: Do information and communication technologies (ICTs) improve health outcomes? An empirical investigation in a non-linear framework

The purpose of our research is to investigate the influence of information and communication technology (ICT) on the physical health outcomes of nations. The covered countries are 185 (54 high-income and 131 low-income countries, also analysed on subsamples) and the period of the analysis is 2005–2018. We use parametric regression analysis of unbalanced panel data and our quadratic models validate the influence of ICT upon life expectancies, mortality rates and measles immunisation rates, controlling for economic prosperity, cultural dimensions and environmental performances. Our results are original within the specialized literature, shedding new light on the ICT infrastructure - health status nexus as they support the inverted U-shaped relationship between internet users and mobile cellular subscriptions as ICTs proxies on the one hand and population health outcomes proxies on the other. We also validate a multiplier effect of ICT proxies upon mortality rates from high-income countries to low-income countries, as follows: the negative effect of Internet access and online information usage is 3.34 times stronger in low-income countries compared to high-income countries. These findings are robust to various estimation techniques, alternative measures of ICT or health and various added controls. The drawbacks are major: in a world in which healthcare programs and policies consider digital inclusion, the limits of using technologies for the benefit of one's health have to be firmly determined. From regular individuals to top policymakers, one should consider when more ICT infrastructure becomes actually less for people's health status.
The Future of Digital Tourism Alternatives in Virtual Reality

In this study, a review of the future of VR technologies for digital tourism alternatives will be presented. The general purpose of the research is to contribute to VR technologies, which are developing and integrating with new systems with each passing day from the point of view of providing a general perspective in tourism. VR application information to deepen understanding of the scope of the digital future of tourism alternatives will be presented in a systematic framework. In general, VR technologies express the behavioral experiences of individuals in virtual environments. When tourism and VR technologies are combined, the composition of the product in which these virtual experiences are directed towards a purpose emerges. Every new development that occurs from a technological point of VR technology will make it possible to be used in every field of tourism. The contribution of this research is to provide suggestions for future studies as well as practical implications for the tourism industry regarding VR systems, which are among the digital tourism alternatives.
FPGA implementation of multi-dimensional Kalman filter for object tracking and motion detection

Object tracking and motion detection are the major challenges in the real-time image and video processing applications. There are several tracking and prediction algorithms available to estimate and predict the state of a system. Kalman filter is the most widely used prediction algorithm as it is very simple, efficient and easy to implement for linear measurements. However, these types of filter algorithms are customized on hardware platforms such as Field-Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs) to achieve design requirements for embedded applications. In this work, a multi-dimensional Kalman filter (MDKF) algorithm is proposed for object tracking and motion detection. The numerical analysis of proposed tracking algorithm achieves competitive tracking performance in contrast with state-of-the-art tracking algorithms trained on standard benchmarks. Furthermore, MDKF is implemented on Xilinx Zynq™-7000 System-on-a chip (SoC). The implementation of MDKF on SoC performs 2× times tracking speed than that of software approach. The experimental results provide resource utilization of about 61.43% of Block RAMs (BRAMs), 90.09% of DSPs, 83.27% of Look-up tables (LUTs) and 82.35% of logic cells operating at 140 MHz with power consumption of 780 mW which outperforms previous related methods.
Fractional-Order Ant Colony Algorithm: A Fractional Long Term Memory Based Cooperative Learning Approach

The present work discusses a novel conceptual formulation of the Fractional-Order Ant Colony Algorithm (FACA), which is a fractional long term memory based cooperative learning approach. In the standard Integer-order Ant Colony Algorithms (IACAs), each ant selects its next edge on the graph according to a transition probability that is obtained from the pheromone value and additional information found on the edges adjacent to its present node. To improve the optimization capability associated with traditional IACAs, we have studied here, as an interesting theoretical problem, whether it will be possible to apply the inherent strengths of fractional calculus to modify the transition behaviour of the classical IACAs by replacing the simple one-step probability by a more complex expression that seems to include some look-ahead information. The formal motivation for this expression is from the fractional derivatives where simple differences are replaced by more involved expressions. Motivated by this need, in this work, we introduce a novel algorithm called Fractional-Order Ant Colony Algorithm (FACA). At first, we extend the IACA to the FACA based on the long-term memory of fractional difference. Secondly, we discuss the algorithmic flow of the FACA. Thirdly, we present the mathematical proof and convergence analysis of the FACA. Fourthly, we present the experiment results obtained and the associated analyses carried out. The paper introduces a novel mathematical approach, fractional calculus, to the family of ant colony algorithms, that differs from most of the previous approaches and hence it presents an interesting theoretical contribution in this research domain.
Does explainable machine learning uncover the black box in vision applications?

Machine learning (ML) in general and deep learning (DL) in particular has become an extremely popular tool in several vision applications (like object detection, super resolution, segmentation, object tracking etc.). Almost in parallel, the issue of explainability in ML (i.e. the ability to explain/elaborate the way a trained ML model arrived at its decision) in vision has also received fairly significant attention from various quarters. However, we argue that the current philosophy behind explainable ML suffers from certain limitations, and the resulting explanations may not meaningfully uncover black box ML models. To elaborate our assertion, we first raise a few fundamental questions which have not been adequately discussed in the corresponding literature. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles in the related areas.
Technical potential of rooftop solar photovoltaic for Ankara

Turkey, which has immense solar potential, has recently shifted towards solar energy and new renewable energy deployment regulations, including implementation and commercializing rooftop PV technologies. Thus, these require a detailed assessment to determine the power source's capability and convince the policymakers. To the authors' best knowledge, there is no available data or a city-based study for the rooftop PV potential in Turkey. Here, we present the first city-based rooftop PV potential study in Ankara. The technical PV potential calculations are sensitive to the suitable area ratio, module efficiency, and module area assumptions. This study proposes a new, free, reliable, and open-source model for estimating the rooftop PV potential, and it is adaptable to every roof type. The suitable roof area for PV panels is calculated using Helioscope software. Five different modules with different efficiencies and module sizes are modelled in Helioscope. The results showed that Ankara's total technical PV potentials for residential, public, and commercial buildings are 1.15 TWh/year, 55 GWh/year, and 26.8 GWh/year, respectively. Best results are obtained by Mono-Si Halfcut (M2) and Thin Film (M5) modules in residentials, M2 and Mono-Si Bifacial Modules (M3) in Public and Commercial Buildings.
Cross-knowledge-graph entity alignment via relation prediction

The entity alignment task aims to align entities corresponding to the same object in different KGs.The recent work focuses on applying knowledge embedding or graph neural networks to obtain entity embedding for entity alignment.However, there are two challenges encountered by these models: one is some models need to design hyper-parameter to balance embedding loss and alignment loss, the other is the limited training data size.In this paper, we propose a novel entity alignment framework named RpAlign (Relation prediction based cross-knowledge-graph entity Alignment) to address these two issues.Specifically, RpAlign transforms the entity alignment task to the KG completion task to solve and does not need to design any extra alignment component.Unlike the existing models that predict aligned entities by using entity vector distance, the RpAlign defines a new relation called 'anchor' for aligned entities, and it predicts new aligned entities based on the relational predictions between the entities.RpAlign employs several data augmentation and improved self-training techniques to mitigate the impact of the data limitation.We conduct experiments on two datasets, and the experimental results show that the RpAlign model significantly outperforms the current state-of-the-art models.
Comparison of dynamic updating strategies for clinical prediction models

Prediction models inform many medical decisions, but their performance often deteriorates over time. Several discrete-time update strategies have been proposed in the literature, including model recalibration and revision. However, these strategies have not been compared in the dynamic updating setting.We used post-lung transplant survival data during 2010-2015 and compared the Brier Score (BS), discrimination, and calibration of the following update strategies: (1) never update, (2) update using the closed testing procedure proposed in the literature, (3) always recalibrate the intercept, (4) always recalibrate the intercept and slope, and (5) always refit/revise the model. In each case, we explored update intervals of every 1, 2, 4, and 8 quarters. We also examined how the performance of the update strategies changed as the amount of old data included in the update (i.e., sliding window length) increased.All methods of updating the model led to meaningful improvement in BS relative to never updating. More frequent updating yielded better BS, discrimination, and calibration, regardless of update strategy. Recalibration strategies led to more consistent improvements and less variability over time compared to the other updating strategies. Using longer sliding windows did not substantially impact the recalibration strategies, but did improve the discrimination and calibration of the closed testing procedure and model revision strategies.Model updating leads to improved BS, with more frequent updating performing better than less frequent updating. Model recalibration strategies appeared to be the least sensitive to the update interval and sliding window length.
Neural frame interpolation for rendered content

The demand for creating rendered content continues to drastically grow. As it often is extremely computationally expensive and thus costly to render high-quality computer-generated images, there is a high incentive to reduce this computational burden. Recent advances in learning-based frame interpolation methods have shown exciting progress but still have not achieved the production-level quality which would be required to render fewer pixels and achieve savings in rendering times and costs. Therefore, in this paper we propose a method specifically targeted to achieve high-quality frame interpolation for rendered content. In this setting, we assume that we have full input for every n -th frame in addition to auxiliary feature buffers that are cheap to evaluate (e.g. depth, normals, albedo) for every frame. We propose solutions for leveraging such auxiliary features to obtain better motion estimates, more accurate occlusion handling, and to correctly reconstruct non-linear motion between keyframes. With this, our method is able to significantly push the state-of-the-art in frame interpolation for rendered content and we are able to obtain production-level quality results.
Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Random Undersampling (RUS) is one of resampling approaches to tackle issues with imbalance data by removing instances randomly from the majority class. Anomaly is considered as a rare case, thus the number of instances in the anomaly class is usually much lower than instances in other classes. In anomaly detection of time series data, an anomaly is identified when an unusual pattern exists. Duplicating the unusual pattern may lead to overfitting, which is why this study considered an undersampling method over oversampling approach. This study applied RUS on data with several algorithms to observe its effectiveness on different types of classifier. To prove the overfitting and underfitting issues, different ratios of training and testing were used. Five different evaluation metrics were considered to evaluate the performance of the approach used. It was found that RUS could improve the classification performance of every classifier and the best result was shown when RUS was applied on a deep learning algorithm.
Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data

Designing a smart and robust predictive model that can deal with imbalanced data and a heterogeneous set of features is paramount to its widespread adoption by practitioners. By smart, we mean the model is either parameter-free or works well with default parameters, avoiding the challenge of parameter tuning. Furthermore, a robust model should consistently achieve high accuracy regardless of any dataset (imbalance, heterogeneous set of features) or domain (such as medical, financial). To this end, a computationally inexpensive and yet robust predictive model named smart robust feature selection (SoFt) is proposed. SoFt involves selecting a learning algorithm and designing a filtering-based feature selection algorithm named multi evaluation criteria and Pareto (MECP). Two state-of-the-art gradient boosting methods (GBMs), CatBoost and H2O GBM, are considered potential candidates for learning algorithms. CatBoost is selected over H2O GBM due to its robustness with both default and tuned parameters. The MECP uses multiple parameter-free feature scores to rank the features. SoFt is validated against CatBoost with a full feature set and wrapper-based CatBoost. SoFt is robust and consistent for imbalanced datasets, i.e., average value and standard deviation of log loss are low across different folds of K-fold cross-validation. Features selected by MECP are also consistent, i.e., features selected by SoFt and wrapper-based CatBoost are consistent across different folds, demonstrating the effectiveness of MECP. For balanced datasets, MECP selects too few features, and hence, the log loss of SoFt is significantly higher than CatBoost with a full feature set.
Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

For image translational tasks, the application of deep learning methods showed that Generative Adversarial Network (GAN) architectures outperform the traditional U-Net networks, when using the same training data size. This study investigates whether this performance boost can also be expected for segmentation tasks with small training dataset size.Two models were trained on varying training dataset sizes ranging from 1-100 patients: a) U-Net and b) U-Net with patch discriminator (conditional GAN). The performance of both models to segment the male pelvis on CT-data was evaluated (Dice similarity coefficient, Hausdorff) with respect to training data size.No significant differences were observed between the U-Net and cGAN when the models were trained with the same training sizes up to 100 patients. The training dataset size had a significant impact on the models' performances, with vast improvements when increasing dataset sizes from 1 to 20 patients.When introducing GANs for the segmentation task no significant performance boost was observed in our experiments, even in segmentation models developed on small datasets.
Propagation-Based Fake News Detection Using Graph Neural Networks with Transformer

The spread of fake news has become a worldwide problem, affecting public trust. Recent studies have reported that fake news and real news spread differently on social media. Thus, propagation-based detection methods, which construct graphs with users as nodes and news sharing chains as edges and simultaneously learn propagation patterns and users’ preferences using Graph Neural Networks (GNNs), have attracted much attention. However, for extracting users’ preferences from the graph, it is a challenge to learn the relationship between unconnected nodes. In this paper, we propose a method for fake news detection using Graph Transformer Network (GTN), which can learn efficient node representations while identifying useful connections between nodes in the original graph. The effectiveness of the proposed method is confirmed by comparison experiments using the real-world dataset composed of Twitter data.
Fast terminal sliding mode control design for position control of induction motors using adaptive quantum neural networks

In this work, an efficient robust control scheme is proposed for position control of induction motors based on field-oriented control (FOC). The proposed control scheme consists of three interior loops. In the outer loop, the central controller based on the nonsingular fast terminal sliding mode control (NFTSM) is designed. The objective of this loop is to provide the required stator current in the q-axis for regulating the electromagnetic torque. In the inner loops, two fractional-order PI (FOPI) controllers are designed to provide the stator voltages. Besides, since induction motors have a complex mathematical model, time-varying dynamics, and non-linear structure, to deal with these challenges in the controller design, the quantum neural network (QNN) is presented with a novel framework and employed to estimate the lumped uncertainties. This makes the main contribution of the paper. In addition, to further improve system performance, the parameters of the FOPI controllers are tuned by employing the artificial bee colony (ABC) optimization algorithm. The stability of the proposed control approach is proved by Lyapunov’s stability theory. According to the results of the comparative simulation, the superiority of this approach is confirmed.
Task-Consistent Path Planning for Mobile 3D Printing

In this paper, we explore the problem of task-consistent path planning for printing-in-motion via Mobile Manipulators (MM). MM offer a potentially unlimited planar workspace and flexibility for print operations. However, most existing methods have only mobility to relocate an arm which then prints while stationary. In this paper we present a new fully autonomous path planning approach for mobile material deposition. We use a modified version of Rapidly-exploring Random Tree Star (RRT*) algorithm, which is informed by a constrained Inverse Reachability Map (IRM) to ensure task consistency. Collision avoidance and end-effector reachability are respected in our approach. Our method also detects when a print path cannot be completed in a single execution. In this case it will decompose the path into several segments and reposition the base accordingly.
Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor

This study proposes a modeling method of soft measurement of offshore wind speed using Kernal Extreme Learning Machine (KELM). The soft measurement model of offshore wind speed is presented based on the data-driven method and kernel function extreme learning machine. An improved gray Wolf optimization algorithm is applied to optimize its parameters to enhance the measurement accuracy. Finally, based on the established offshore wind speed measurement model, a feedforward and feedback variable rotor controller is designed and verified by simulation, which proves the effectiveness of the research in this study.
Interaction analysis of teachers and students in inquiry class learning based on augmented reality by iFIAS and LSA

Inquiry-based learning (IBL) is one of the most important learning strategies. Augmented Reality (AR) is an effective way to support this strategy, which is considered conducive to enhance academic achievements of students during IBL. This paper investigated teacher-student interactions during AR-supported IBL by comparison and case study. First, four video clips carried by two high schools were coded by improved Flanders Interaction Analysis System (iFIAS) with two clips in AR-supported class and others in no AR-supported class. Then, Lag Sequence Analysis (LSA) was used to extract sequence features based on the codes. Finally, suggestions on using AR in IBL and details about study were discussed. Results show that AR-supported IBL has more active responses from students and a higher response rate from teachers. Student responses in AR-supported IBL have more tendency to be approved by teachers. The interactive component of AR software is in positive relation to its capacity of flexible inquiry, but it requires more time to learn to operate and demands more demonstration behavior from teachers. Teachers should develop skills of asking questions to guide and give timely feedback when carrying out IBL supported by AR.
Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.