Reviewerly/exHarmony huggingface dataset:
Communicative patterns for IT professionals as means of mastering communication skills

Abstract The paper deals with the aspects of English communicative pattern implementation into IT professional training. It is emphasized that the communicative pattern usage enhances prompt and fluent profession-related real-life communication and it is the essential part of education of IT specialists. Peculiarities of communication in the IT environment are highlighted. Considering typical situations and issues of communication the guidebook which contains recommendations and communicative patterns for IT undergraduates both for oral and written communication has been developed. In order to evaluate the purposefulness of the guidebook implementation in IT specialist training, two surveys (among lecturers and among undergraduates) of six universities have been conducted. The guidebook has proved to be suitable for practical classes, independent learning and laboratory works in humanities and profession-related disciplines. The survey conducted among English teachers has proved the increased interest, higher motivation and willingness to participate in communicative tasks crucial for specific employment.
Touching Information with DIY Paper Charts &amp; AR Markers

Fostering data literacy has largely been the domain of formal educational systems and export-oriented tools. Informal educational approaches, such as games or family activities, may overcome barriers to engaging with data by fostering data literacy through casual engagement. This work in progress explores how informal learning through creation and play with interactive data representations (physicalizations) can foster increased literacy and engagement with data. We outline a series of DIY paper charts using AR markers and everyday materials to help children interact and explore data through the creative process of making.
Experimental Quantum Embedding for Machine Learning

Abstract The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, these ideas are implemented by engineering two different experimental platforms, based on quantum optics and ultra‐cold atoms, respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. A similar analysis is also performed on the Rigetti superconducting quantum computer. Therefore, it is found that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.
Autonomously Controlled Intelligent Wheelchair System for Indoor Areas

Autonomously controlled intelligent wheelchair is an electric wheelchair system that can reach a desired position by finding paths without any need for user intervention. Pathfinding can be performed in outdoor areas with the support of global positioning system (GPS) while interior mapping is required for indoor areas. In this study, an autonomous control interface design that can work with intelligent wheelchairs has been implemented to be used on indoor areas. This interface is used building floor plans and pathfinding algorithms for planning a path to a selected destination. The autonomous control of an electrically powered wheelchair is performed using fuzzy logic on the planned path. On the hardware side, a depth camera, a single board computer, microcontrollers, speed sensors, lighting systems and a motor driver are added on the electric-powered wheelchair. All components are operated under the coordination that provided with the interface. The instantaneously detection of obstacles that may be encountered causes the path to be changed with an alternative, and this situation can be shown synchronously within a simulation software.
Visual Respiratory Feedback in Virtual Reality Exposure Therapy: A Pilot Study

As the use of Virtual Reality (VR) expands across fields, new kinds of interaction methods are introduced. This study presents the Visual Heights VR experience that integrates natural breathing as an input method to provide visual respiratory feedback. Incorporating spatial audio, haptic feedback and breath visualisation, the experience aims to be highly immersive. This experience was made to be used as part of a controlled pilot study to see the effect of respiratory feedback on the user’s anxiety levels. The user’s anxiety is assessed by their heart rate, brain electrical activity, skin conductance and respiratory rate. These biosignals are recorded within the experience; captured by external hardware. The pieces of hardware used were Galvanic Skin Response to measure skin conductance, photoplethysmogram to measure heart rate; Electroencephalogram to measure the electrical activity in the brain, and a prototype device that records airflow on an axis from -1 to 1 for respiratory rate. It was found that the aforementioned prototype was not sufficient for calculating the respiratory rate. Results of the controlled study showed that the Visual Heights VR experience delivered the expected positive correlation between skin conductance and perceived height (r=.491, p < .05, N=1543) which suggests it is plausible to be used as a material for further research. As the integration of user’s physiological signals and breathing for visual feedback can contribute to therapeutic uses of VR, research with bigger sample sizes will be conducted to better investigate the relationship between visual respiratory feedback and anxiety using the Visual Heights VR experience.
Secure halftone image steganography based on density preserving and distortion fusion

Most state-of-the-art halftone image steganographic methods measure the flipping distortion according to the statistics of the image database but neglect the characteristic of a single image. In this paper, a halftone image steganography based on density preserving and distortion fusion is proposed for minimizing the flipping distortion. Different from ordinary binary images, halftone images express the texture information by the intensity of local regions that are represented by the pixel density. Based on the density distribution of a halftone image, the embedding costs defined by a single existing distortion function are modified adaptively to maintain the original texture structure as much as possible. Furthermore, to select more appropriate flipped pixels, a distortion fusion strategy is developed from the perspective of anti-steganographic analysis, which can be applied to a batch of distortion functions. Finally, the syndrome-trellis code (STC) is applied to minimize the embedding distortion for playing the advantage of the distortion measurement. Experimental results show that the proposed steganographic scheme achieves great visual quality and strong statistical security without degrading the embedding capacity.
Consensus Maximisation Using Influences of Monotone Boolean Functions

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.
Literature Study of Face Recognition using The Viola-Jones Algorithm

The face is the front part of a human expression comprising the eyes, nose, lips, cheeks, forehead, and chin. These characters are uniquely placed according to a human's pattern of face. The Viola-Jones algorithm is used to recognize and detect objects, including this human face. It consists of several stages, such as Haar-like Filter, Integral Image, Adaboost Algorithm, and Cascade. Haar-Like filter is used to determine feature values from images containing certain objects. Furthermore, integral image helps to find the feature value to quicken the calculation process. Adaboost algorithm processes feature selection by determining the threshold value in order to determine the existing object. Meanwhile, cascade performs an image selection process that contains or excludes objects with large amounts of test data. It directly discards the figure when no objects are detected to produce images containing objects. This study is a literature review on facial recognition using the Viola-Jones algorithm. It contributes to the search for the suitability of using the Viola-Jones Algorithm in certain cases. The research contribution also lies in the researcher's idea for future research, namely testing the Viola-Jones algorithm in recognizing objects other than facial images. Furthermore, five studies are analyzed and described the application of the Viola-Jones algorithm for facial recognition with their respective advantages. The first study had a very good accuracy level of 85%-95% in detecting faces. The second study had accuracy, precision, recall, and achievement times of 0.74, 0.73, 0.76, and 15 seconds in recognizing a person's emotions through facial expressions. Meanwhile, the third study had a very good accuracy level of 94.5% in recognizing faces that are 1 meter away.
Informed scenario-based RRT∗ for aircraft trajectory planning under ensemble forecasting of thunderstorms

Thunderstorms represent a major hazard for flights, as they compromise the safety of both the airframe and the passengers. To address trajectory planning under thunderstorms, three variants of the scenario-based rapidly exploring random trees (SB-RRTs) are proposed. During an iterative process, the so-called SB-RRT, the SB-RRT∗ and the Informed SB-RRT∗ find safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. Through parallelization on graphical processing units the required computational times are reduced substantially to become compatible with near real-time operation. The proposed methods are tested considering a kinematic model of an aircraft flying between two waypoints at constant flight level and airspeed; the test scenario is based on a realistic weather forecast and assumed to be described by an ensemble of equally likely members. Lastly, the influence of the number of scenarios, safety margin and iterations on the results is analyzed. Results show that the SB-RRTs are able to find safe and, in two of the algorithms, close-to-optimum solutions.
Semantic Regularized Class-Conditional GANs for Semi-Supervised Fine-Grained Image Synthesis

Learning effective generative models for natural image synthesis is a promising way to reduce the dependence of deep models on massive training data. This work focuses on Fine-Grained Image Synthesis (FGIS) in the semi-supervised setting where a small number of training instances are labeled. Different from generic image synthesis tasks, the available fine-grained data may be inadequate, and the differences among the object categories are typically subtle. To address these issues, we propose a Semantic Regularized class-conditional Generative Adversarial Network, which is referred to as SReGAN. We incorporate an additional discriminator and classifier into the generator-discriminator minimax game. Competing with two discriminators enforces the generator to model both marginal and class-conditional data distributions, which alleviates the problem of limited training data and labels. However, the discriminators may overlook the class separability. To induce the generator to discover the distinctions between classes, we construct semantically congruent and incongruent pairs in the generation process, and further regularize the generator by encouraging high similarities of congruent pairs, while penalizing that of incongruent ones in the classifier's feature space. We have conducted extensive experiments to verify the capability of SReGAN in generating high-fidelity images on a variety of FGIS benchmarks.
Artificial neural networks for global and direct solar irradiance forecasting: a case study

The current study treats the prediction of the direct normal and the global horizontal irradiances (DNI and GHI) for five years measurements in Oujda city, Morocco. The prediction is performed by the mean of proposed and developed models based on the Artificial Neural Network method, which is the most common machine-learning algorithm today. The purpose of this research is to achieve more accurate daily and monthly GHI and DNI by using many combinations of a new calculated input; cloudy sky index (Cs) with temperature (T), wind speed (WS), wind direction (WD), relative humidity (RH), diffuse, and global horizontal irradiances (DHI and GHI). Several models are developed by forming combinations according to the variation of the input number. These models are evaluated by different statistical tools: Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient (R2). For the DNI estimation, the most appropriate inputs were GHI, Cs, T, RH, WS, WD, and BP. Thus, the best performance is found at the monthly time scale to be −0.35%, 3.33%, 1.26%, and 0.98 for MBE, r-RMSE, MAPE and R2 respectively. The overall errors obtained for the best GHI prediction at the daily time scale are −0.0004%, 0.0099%, 0.0067% and 1 for MBE, r-RMSE, MAPE, and R2, respectively. The cloudy sky index has shown good accuracies for the estimation of GHI and DNI. Moreover, literature about the prediction of different solar irradiance components under Moroccan climate is reviewed in this work.
3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks

To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.
Adaptive mask generating algorithm based on the fuzzy set theory for the weighted least-squares phase unwrapping

In the digital speckle pattern interferometry (DSPI), accurate phase unwrapping is essential for quantitative phase measurement. In this paper, a novel adaptive mask generating algorithm based on the fuzzy set theory is proposed, which can automatically calculate an optimal threshold for the reliability masked weighted least-squares phase unwrapping. The reliability map of a wrapped phase map is firstly calculated based on the second differences of each pixel. Then, the fuzzy interval of the threshold is determined according to reliability values of the residues and standard deviation of the reliability map, and a membership function describing the fuzzy threshold is established to map the reliability values to the interval of [0, 1]. At last, the membership matrix is used to perform weighted average processing on the reliability map to automatically calculate the optimal threshold for generating a 0-1 mask. Two sets of simulated phase maps under different noise levels and fringe abnormal conditions are generated to evaluate and the accuracy of the adaptive threshold and the performance of the proposed algorithm. Experimental phase data of deformation dynamic measurement in DSPI is adopted to verify the effectiveness of the proposed algorithm. Simulation and experimental results show that the algorithm can adaptively calculate an optimal threshold of different wrapped phase maps, which is very close to the threshold elaborately selected by manual intervention, and can provide a reliable and accurate unwrapping phase for actual phase measurement.
Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.
An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

The haze scenes will bring negative impact to the object tracking process. In haze scenes, aerosols in the air will decrease the information entropy of the image generated by the imaging device. The reduction of information entropy means the loss of image detail information, which will mislead the existing algorithms to extract wrong object feature, causing the failure of tracking. Firstly, this paper explores the problem and proposes an end-to-end Dehazing Siamese Region Proposal Network (DH-SiamRPN). Then, we innovatively design an adaptive transmittance estimation module based on image quality evaluation index, and pre-integrate it into the dark channel dehazing algorithm to preprocess the video sequence. After that, based on the siamese network framework, AlexNet is employed to extract the depth features of the image pair, and the cross-correlation operation of classification and regression is performed according to the regional proposal network (RPN). In addition, inspired by DIoU modeling ideas, we innovatively optimize the proposal selection part. The experimental results demonstrate that the DH-SiamRPN has obvious advantages in precision and speed compared with other state-of-the-art trackers. Its tracking performance meets the requirements of high robustness and speed.
Light Weight IBP Deep Residual Network for Image Super Resolution

Single -image super resolution (SR) is used to reconstruct a high-resolution image with more high-frequency details based on a low-resolution image as input.In recent years, image SR reconstruction based on deep learning methods has shown a considerably better performance than traditional methods.Early deep-learning-based methods deepen convolutional layers and directly reconstruct high-resolution images with complex neural networks.However, with the stacking of modules, network depth and model parameters increase, thereby raising computational resource; hence, it is difficult to apply on low-configuration devices.Furthermore, existing methods ignore the high-frequency details of the image, resulting in unsatisfactory performance.To solve these problems, a lightweight network model that applies the iterative back projection (IBP) mechanism to the network and reduces the dimensionality of the input image features is proposed.The proposed network model consists of three parts, namely, entrance module, main body module, and exit module.It designs a lightweight modular design to reduce the model calculation and control the network depth more easily by adjusting the number of ADB modules.The main part of the model consists of four lightweight accelerating deep residual back projection (ADB) modules.Each ADB module initially decreases the dimensionality of the input image features through a 1 × 1 convolutional layer to reduce the amount of calculation.Then, IBP is used to back project the image iteratively.Each ADB module only performs the downsampling back projection operation because the image features become larger after upsampling.Through three iterative downsampling back projection units, the high-frequency features of the image are fully explored, and the output image features are then compared with the shallow layer.Image feature fusion is used as input of the next ADB module, and the output part combines the highand low-frequency image feature output by multiple ADB modules to complete the upsampling by using the PixelShuffle method to generate high-resolution images.Several experiments confirm that the proposed algorithm achieves better SR reconstruction accuracy with faster reconstruction speed than existing image SR methods.
Multi-band on-chip photonic spin Hall effect and selective excitation of whispering gallery modes with metasurface-integrated microcavity

We propose an approach to realize a multi-band on-chip photonic spin Hall effect and selective excitation of whispering gallery modes (WGMs) by integrating metasurfaces with microcavities. Free-space circularly polarized light with opposite spin angular momentum can effectively excite WGMs with opposite propagation directions at fixed wavelengths. Moreover, the different WGMs with different propagation directions and polarizations can be selectively excited by manipulating the number of antennas. We demonstrate that the optical properties (i.e., coupling efficiency, peak positions, and peak widths) of the proposed metasurface-integrated microcavities can be easily tailored by adjusting different geometric parameters. This study enables the realization of chiral microcavities with exciting novel functionalities, which may provide a further step in the development of photonic integrated circuits, optical sensing, and chiral optics.