bluuebunny/crossref_metadata_2025 huggingface dataset:
Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network
Authors: Liu Chun , Zeng Doudou , Wu Hangbin , Wang Yin , Jia Shoujun , Xin Liang
Published: January 2020

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.
Correction: Laengner, M. L., et al. Trends in the Seaward Extent of Saltmarshes across Europe from Long-Term Satellite Data. Remote Sensing 2019, 11, 1653
Authors: Laengner Marieke Liesa , Siteur Koen , van der Wal Daphne
Published: January 2020

In this paper [...]
Measuring the Directional Ocean Spectrum from Simulated Bistatic HF Radar Data
Authors: Hardman Rachael L. , Wyatt Lucy R. , Engleback Charles C.
Published: January 2020

HF radars are becoming important components of coastal operational monitoring systems particularly for currents and mostly using monostatic radar systems where the transmit and receive antennas are colocated. A bistatic configuration, where the transmit antenna is separated from the receive antennas, offers some advantages and has been used for current measurement. Currents are measured using the Doppler shift from ocean waves which are Bragg-matched to the radio signal. Obtaining a wave measurement is more complicated. In this paper, the theoretical basis for bistatic wave measurement with a phased-array HF radar is reviewed and clarified. Simulations of monostatic and bistatic radar data have been made using wave models and wave spectral data. The Seaview monostatic inversion method for waves, currents and winds has been modified to allow for a bistatic configuration and has been applied to the simulated data for two receive sites. Comparisons of current and wave parameters and of wave spectra are presented. The results are encouraging, although the monostatic results are more accurate. Large bistatic angles seem to reduce the accuracy of the derived oceanographic measurements, although directional spectra match well over most of the frequency range.
Drift of the Earth’s Principal Axes of Inertia from GRACE and Satellite Laser Ranging Data
Authors: Ferrándiz José M. , Modiri Sadegh , Belda Santiago , Barkin Mikhail , Bloßfeld Mathis , Heinkelmann Robert , Schuh Harald
Published: January 2020

The location of the Earth’s principal axes of inertia is a foundation for all the theories and solutions of its rotation, and thus has a broad effect on many fields, including astronomy, geodesy, and satellite-based positioning and navigation systems. That location is determined by the second-degree Stokes coefficients of the geopotential. Accurate solutions for those coefficients were limited to the stationary case for many years, but the situation improved with the accomplishment of Gravity Recovery and Climate Experiment (GRACE), and nowadays several solutions for the time-varying geopotential have been derived based on gravity and satellite laser ranging data, with time resolutions reaching one month or one week. Although those solutions are already accurate enough to compute the evolution of the Earth’s axes of inertia along more than a decade, such an analysis has never been performed. In this paper, we present the first analysis of this problem, taking advantage of previous analytical derivations to simplify the computations and the estimation of the uncertainty of solutions. The results are rather striking, since the axes of inertia do not move around some mean position fixed to a given terrestrial reference frame in this period, but drift away from their initial location in a slow but clear and not negligible manner.
Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece
Authors: Polykretis Christos , Grillakis Manolis , Alexakis Dimitrios
Published: January 2020

The main objective of this study was to explore the impact of various spectral indices on the performance of change vector analysis (CVA) for detecting the land cover changes on the island of Crete, Greece, between the last two decades (1999–2009 and 2009–2019). A set of such indices, namely, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), albedo, bare soil index (BSI), tasseled cap greenness (TCG), and tasseled cap brightness (TCB), representing both the vegetation and soil conditions of the study area, were estimated on Landsat satellite images captured in 1999, 2009, and 2019. Change vector analysis was then applied for five different index combinations resulting to the relative change outputs. The evaluation of these outputs was performed towards detailed land cover maps produced by supervised classification of the aforementioned images. The results from the two examined periods revealed that the five index combinations provided promising performance results in terms of kappa index (with a range of 0.60–0.69) and overall accuracy (with a range of 0.86–0.96). Moreover, among the different combinations, the use of NDVI and albedo were found to provide superior results against the other combinations.
Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors
Authors: Li Yaxin , Li Wenbin , Darwish Walid , Tang Shengjun , Hu Yuling , Chen Wu
Published: January 2020

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).
Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array
Authors: Garcia Heriberto A. , Couture Trenton , Galor Amit , Topple Jessica M. , Huang Wei , Tiwari Devesh , Ratilal Purnima
Published: January 2020

A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.
Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services
Authors: Barbierato Elena , Bernetti Iacopo , Capecchi Irene , Saragosa Claudio
Published: January 2020

There is an urgent need for holistic tools to assess the health impacts of climate change mitigation and adaptation policies relating to increasing public green spaces. Urban vegetation provides numerous ecosystem services on a local scale and is therefore a potential adaptation strategy that can be used in an era of global warming to offset the increasing impacts of human activity on urban environments. In this study, we propose a set of urban green ecological metrics that can be used to evaluate urban green ecosystem services. The metrics were derived from two complementary surveys: a traditional remote sensing survey of multispectral images and Laser Imaging Detection and Ranging (LiDAR) data, and a survey using proximate sensing through images made available by the Google Street View database. In accordance with previous studies, two classes of metrics were calculated: greenery at lower and higher elevations than building facades. In the last phase of the work, the metrics were applied to city blocks, and a spatially constrained clustering methodology was employed. Homogeneous areas were identified in relation to the urban greenery characteristics. The proposed methodology represents the development of a geographic information system that can be used by public administrators and urban green designers to create and maintain urban public forests.
Digital Drill Core Models: Structure-from-Motion as a Tool for the Characterisation, Orientation, and Digital Archiving of Drill Core Samples
Authors: Betlem Peter , Birchall Thomas , Ogata Kei , Park Joonsang , Skurtveit Elin , Senger Kim
Published: January 2020

Structure-from-motion (SfM) photogrammetry enables the cost-effective digital characterisation of seismic- to sub-decimetre-scale geoscientific samples. The technique is commonly used for the characterisation of outcrops, fracture mapping, and increasingly so for the quantification of deformation during geotechnical stress tests. We here apply SfM photogrammetry using off-the-shelf components and software, to generate 25 digital drill core models of drill cores. The selected samples originate from the Longyearbyen CO2 Lab project’s borehole DH4, covering the lowermost cap rock and uppermost reservoir sequences proposed for CO2 sequestration onshore Svalbard. We have come up with a procedure that enables the determination of bulk volumes and densities with precisions and accuracies similar to those of such conventional methods as the immersion in fluid method. We use 3D printed replicas to qualitatively assure the volumes, and show that, with a mean deviation (based on eight samples) of 0.059% compared to proven geotechnical methods, the photogrammetric output is found to be equivalent. We furthermore splice together broken and fragmented core pieces to reconstruct larger core intervals. We unwrap these to generate and characterise 2D orthographic projections of the core edge using analytical workflows developed for the structure-sedimentological characterisation of virtual outcrop models. Drill core orthoprojections can be treated as directly correlatable to optical borehole-wall imagery data, enabling a direct and cost-effective elucidation of in situ drill core orientation and depth, as long as any form of borehole imagery is available. Digital drill core models are thus complementary to existing physical and photographic sample archives, and we foresee that the presented workflow can be adopted for the digitisation and digital storage of other types of geological samples, including degradable and dangerous ice and sediment cores and samples.
Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification
Authors: Ma Bian , Teng Jing , Zhu Huixian , Zhou Rong , Ju Yun , Liu Shi
Published: January 2020

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.
LPI Radar Waveform Recognition Based on Features from Multiple Images
Authors: Ma Zhiyuan , Huang Zhi , Lin Anni , Huang Guangming
Published: January 2020

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.
Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues
Authors: Yilmaz Tuba
Published: January 2020

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.
Functional Evaluation Using Inertial Measurement of Back School Therapy in Lower Back Pain
Authors: Celletti Claudia , Mollica Roberta , Ferrario Cristina , Galli Manuela , Camerota Filippo
Published: January 2020

Lower back pain is an extremely common health problem and globally causes more disability than any other condition. Among other rehabilitation approaches, back schools are interventions comprising both an educational component and exercises. Normally, the main outcome evaluated is pain reduction. The aim of this study was to evaluate not only the efficacy of back school therapy in reducing pain, but also the functional improvement. Patients with lower back pain were clinically and functionally evaluated; in particular, the timed “up and go” test with inertial movement sensor was studied before and after back school therapy. Forty-four patients completed the program, and the results showed not only a reduction of pain, but also an improvement in several parameters of the timed up and go test, especially in temporal parameters (namely duration and velocity). The application of the inertial sensor measurement in evaluating functional aspects seems to be useful and promising in assessing the aspects that are not strictly correlated to the specific pathology, as well as in rehabilitation management.
Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility
Authors: Mauri Antoine , Khemmar Redouane , Decoux Benoit , Ragot Nicolas , Rossi Romain , Trabelsi Rim , Boutteau Rémi , Ertaud Jean-Yves , Savatier Xavier
Published: January 2020

In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.
Real-Time 3D Reconstruction of Thin Surface Based on Laser Line Scanner
Authors: He Yuan , Zheng Shunyi , Zhu Fengbo , Huang Xia
Published: January 2020

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.
Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm
Authors: Cao Guocan , Xu Xiang , Xu Dacheng
Published: January 2020

This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.
Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion
Authors: Song Jiyuan , Zhu Aibin , Tu Yao , Wang Yingxu , Arif Muhammad Affan , Shen Huang , Shen Zhitao , Zhang Xiaodong , Cao Guangzhong
Published: January 2020

Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.
Spatial and Temporal Variations of Polar Ionospheric Total Electron Content over Nearly Thirteen Years
Authors: Xi Hui , Jiang Hu , An Jiachun , Wang Zemin , Xu Xueyong , Yan Houxuan , Feng Can
Published: January 2020

It is of great significance for the global navigation satellite system (GNSS) service to detect the polar ionospheric total electron content (TEC) and its variations, particularly under disturbed ionosphere conditions, including different phases of solar activity, the polar day and night alternation, the Weddell Sea anomaly (WSA) as well as geomagnetic storms. In this paper, four different models are utilized to map the ionospheric TEC over the Arctic and Antarctic for about one solar cycle: the polynomial (POLY) model, the generalized trigonometric series function (GTSF) model, the spherical harmonic (SH) model, and the spherical cap harmonic (SCH) model. Compared to other models, the SCH model has the best performance with ±0.8 TECU of residual mean value and 1.5–3.5 TECU of root mean square error. The spatiotemporal distributions and variations of the polar ionospheric TEC are investigated and compared under different ionosphere conditions in the Arctic and Antarctic. The results show that the solar activity significantly affects the TEC variations. During polar days, the ionospheric TEC is more active than it is during polar nights. In polar days over the Antarctic, the maximum value of TEC always appears at night in the Antarctic Peninsula and Weddell Sea area affected by the WSA. In the same year, the ionospheric TEC of the Antarctic has a larger amplitude of annual variation than that of the TEC in the Arctic. In addition, the evolution of the ionization patch during a geomagnetic storm over the Antarctic can be clearly tracked employing the SCH model, which appears to be adequate for mapping the polar TEC, and provides a sound basis for further automatic identification of ionization patches.
Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model
Authors: Brousseau Braiden , Rose Jonathan , Eizenman Moshe
Published: January 2020

This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.
Research on the Design and Automatic Recognition Algorithm of Subsidence Marks for Close-Range Photogrammetry
Authors: Meng Liyuan , Zou Jingui , Liu Guojian
Published: January 2020

In China, traditional techniques for measuring structural subsidence cannot keep pace with the rapid development of critical national infrastructure such as the growing network of high-speed railways. Traditional monitoring methods using leveling instruments are inefficient and time consuming when monitoring structures like bridges and tunnels. Thus, a fast, economical, and more accurate and precise way to survey building subsidence is urgently needed to address this problem. This paper introduces a new close-range photogrammetry technique that deploys a fixed camera with tilt compensator to measure changes in height over small areas. A barcode subsidence mark that can be identified automatically during digital image processing replaces the leveling points used in traditional methods. Four experiments at different locations verified that results from the new method were stable and consistent with total station measurements. This approach is simple, inexpensive, and produces accurate and precise results as our evaluation results show.
Development of Low-Cost Portable Spectrometers for Detection of Wood Defects
Authors: Sandak Jakub , Sandak Anna , Zitek Andreas , Hintestoisser Barbara , Picchi Gianni
Published: January 2020

Portable spectroscopic instruments are an interesting alternative for in-field and on-line measurements. However, the practical implementation of visible-near infrared (VIS-NIR) portable sensors in the forest sector is challenging due to operation in harsh environmental conditions and natural variability of wood itself. The objective of this work was to use spectroscopic methods as an alternative to visual grading of wood quality. Three portable spectrometers covering visible and near infrared range were used for the detection of selected naturally occurring wood defects, such as knots, decay, resin pockets and reaction wood. Measurements were performed on wooden discs collected during the harvesting process, without any conditioning or sample preparation. Two prototype instruments were developed by integrating commercially available micro-electro-mechanical systems with for-purpose selected lenses and light source. The prototype modules of spectrometers were driven by an Arduino controller. Data were transferred to the PC by USB serial port. Performance of all tested instruments was confronted by two discriminant methods. The best performing was the microNIR instrument, even though the performance of custom prototypes was also satisfactory. This work was an essential part of practical implementation of VIS-NIR spectroscopy for automatic grading of logs directly in the forest. Prototype low-cost spectrometers described here formed the basis for development of a prototype hyperspectral imaging solution tested during harvesting of trees within the frame of a practical demonstration in mountain forests.
Dependence on Interprovincial Migrant Labour in Atlantic Canadian Communities: The Role of the Alberta Economy
Authors: Lionais Doug , Murray Christina , Donatelli Chloe
Published: January 2020

(1) Background: In the face of persistent and chronically weak labour markets, Atlantic Canada has become increasingly dependent on mobile oil work in Northern Alberta for employment and income. In the regions, most intensely engaged in this form of employment, mobile oil work has largely replaced the dominant industries of the previous century. This geographic shift in Canadian investment and production has created uneven labour markets, with high demand for labour in the Northern Alberta and high unemployment in de-industrialized communities in Atlantic Canada. (2) Methods: There is little quantitative evidence on the flows of mobile workers from the East to the West and the impact of this movement on the Atlantic Canadian economy. Data for this paper were obtained through a special arrangement with Statistics Canada in the fall of 2015 and winter of 2016, from the Canadian Employer–Employee Dynamics Database (CEEDD). (3) Results: Analysis of CEEDD revealed that the oil and gas industry of Northern Alberta has a significant impact on the economies of Atlantic Canada with an increasing dependence for interprovincial workers. (4) Conclusions: To the extent that mobile work has served as a replacement for traditional industries, mobile work is re-structuring the social and economic makeup of Atlantic Canadian communities. The more reliant Atlantic Canadian communities become on oil-related mobile work, the more precarious their economies will become as global markets for oil and gas change and targeted actions on climate change increase.
Post-Editorial of “The Multiverse” Special Volume
Authors: Alonso-Serrano Ana , Da̧browski Mariusz P. , Naumann Thomas
Published: January 2020

A succesful series of papers devoted to various aspects of an idea of the Multiverse have been gathered together and presented to the readers. In this post-editorial we briefly challenge the content referring to the main issues dealt with by the Authors. We hope that this will inspire other investigators for designing future tests which could make this very notion of the Multiverse falsifiable.
Acknowledgement to Reviewers of Vision in 2019
Authors: Vision Editorial Office
Published: January 2020

The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not [...]
Scale Development: Measuring Job-Family Incivility
Authors: Mahalekamge W G S , Galahitiyawe Nilakshi W K
Published: January 2020

The main contribution of this paper is the development of the job-family incivility scale. This paper seeks to explore the complex inter-relationships between workplace incivility and family incivility dimensions of job-family incivility by examining the dynamic processes by which job-family incivility is initiated and sustained using a scale development approach. In doing so, the paper highlights the absence of valid and reliable measures that estimate the compound effect of workplace incivility and family incivility – referred to as job-family incivility. A scale development process introduced by Quazi et al. (2016) is presented and stages in the construction of the scale are discussed including measures of validity and reliability. The findings of the study demonstrate the validity and reliability of the job-family incivility scale developed by the current researchers. The main aims of this research are to emphasize the impact of workplace incivility and family incivility on the employees in the work environment, and to discuss the techniques that can be used to mitigate such detrimental activities.
The study of personnel stimulation methods on the way to a new system of motivation
Authors: Kharitonova Ju.V.
Published: January 2020

The article is devoted to the study of methods of motivation and stimulation of personnel to work in the conditions of modern dynamic changes in the global market. The author noted that in the current unstable conditions of functioning of domestic enterprises, the system for ensuring their development acquires such features as the need and reliability of effective work of personnel. Personnel management of enterprises is impossible without understanding the essence, content and role of the labor incentive process, which has led to the relevance of its study, that is, conducting a comprehensive study of the needs, motives and incentives of a person, clarifying the classification of these categories, which contributes to a proper understanding of the categorical apparatus and ensuring effective staff motivation and incentives. At this stage of development, the Russian economy is experiencing instability and inability to fully meet the needs of the population in the workplace. There is a staff turnover that adversely affects the quality of work of staff who understand the precariousness of their position and therefore do not have sufficient incentive to fully benefit the company for which they work. To ensure the effective use of all the professional and intellectual potential of the worker, it is necessary to find new or time-tested mechanisms to stimulate his work activity. In the course of the study, the author reviewed the theoretical foundations of the methods of motivating and stimulating labor highlighted the features of the functioning of the domestic labor market. The experience of foreign companies in relation to non-standard methods of motivation and stimulation was also studied, on the basis of which reasonable directions for the formation of a system of motivation and incentives for labor in Russia at the present stage of development of economic relations were proposed.
The formation of professional portrait of a specialist of agro-industrial complex in the conditions of modernization of agrarian sector of economy
Authors: Alekseeva S.A. , Lomakin O.E.
Published: January 2020

Today, agriculture is undergoing fundamental transformation. Modern technologies are changing the face of agriculture radically. The professional portrait of a specialist in the agro-industrial sector is also being transformed. In modern conditions it is necessary to provide as quickly as possible with the personnel meeting the new requirements dictated by the hi-tech environment of innovative economy which will promote fast modernization of agrarian sector on the basis of achievements of a new technological wave.
ESTIMATION OF COASTAL ASIMILATION CAPACITY BASED ON STANDARD EFFLUENT WASTE NITROGEN AND PHOSFORUS OF SHRIMP CULTIVATION
Authors: Muqsith Abdul , Harahab Nuddin , Mahmudi Mohammad , Fadjar Muhammad
Published: January 2020

This research was conducted in May 2018 in the coastal area of ​​Banyuputih Subdistrict, Situbondo Regency, East Java with the aim of estimating the volume of seawater available in coastal waters and the capacity of coastal waters in assimilating N and P waste load based on N and P effluent standards for cultivation shrimp. The results showed that the coastal waters of Banyuputih had a volume of water available for dilution of N and P wastes of 43,198,298 m3. The maximum N and P waste load that can be assimilated by water according to the standard effluent of N waste for shrimp farming is 172, 8 tons and for the P waste load is 17.28 tons. The results of this study can be used as a reference in determining the environmental carrying capacity of coastal waters in the development of intensive shrimp ponds in the study area.
РОЗВИТОК АНГЛОМОВНОЇ ПРОФЕСІЙНОЇ КОМПЕТЕНТНОСТІ ВИКЛАДАЧІВ І СТУДЕНТІВ ЗАКЛАДІВ ВИЩОЇ ОСВІТИ
Authors: Халемендик Юлія
Published: January 2020

У статті досліджена проблема ролі викладача в процесі розвитку англомовної професійної компетентності студентів як майбутніх фахівців в умовах заклад вищої освіти. Метою статті був аналіз діяльність викладачів закладів вищої освіти з розвитку англомовної професійної компетентності майбутніх фахівців на прикладі студентів спеціальності «Психологія» Запорізького національного університету. Підкреслено, що в Україні англомовна підготовка студентів-психологів спирається на концепцію іншомовної освіти, яка базується на інтегрованому навчанні мови та культури відповідних країн, на діалозі рідної та іноземної культур. Експериментально доведено, що загальне значення середнього показника англомовної компетентності студентів як майбутніх фахівців становило 9,13 балів із 20 можливих, що свідчить про нагальну необхідність удосконалення викладання англійської мови у закладах вищої освіти.

Ключові слова: англомовна професійна компетентність; викладач; заклад вищої освіти; майбутні фахівці; професійна підготовка; розвиток.
Daha İyi Bir İş Yaşamı: Örgütsel Toksisite Faktörlerinin AHP İle Belirlenmesi
Authors: ARAR Tayfun , ARAR Eda Sultan , ÖNEREN Melahat
Published: January 2020

Bu araştırma; iş dünyasının her alanında karşılaşılabilecek olan örgütsel toksisite olgusuna etki eden faktörleri akademik bağlamda incelemek amacı ile üniversitelerin akademik kadrolarında hiyerarşik olarak en altta yer alan araştırma görevlileri kapsamında yapılmıştır. Örgütsel toksisiteye neden olan faktörler temel olarak; yöneticiden, iş arkadaşlarından ve kurumdan kaynaklanan olmak üzere 3 ana faktörde ve her bir ana faktör altında dört alt faktör olmak üzere toplamda 12 faktör; literatür taraması sonucunda elde edilerek Analitik Hiyerarşi Prosesi (AHP) yöntemi ile hiyerarşik olarak sıralanmıştır. Araştırma neticesinde ilgili örnekleme göre örgütsel toksisiteye neden olan en önemli etmen ana faktör bağlamında yönetici olurken; alt faktör bağlamında kurumdan kaynaklı kurum kültürü ile yöneticiden kaynaklı etik dışı davranışlar olmuştur.  Elde edilen bulgulara göre genel iklimi ve performans düzeyini olumsuz yönde etkileyen örgütsel toksisitenin engellenmesine veya azaltılmasına yönelik çeşitli önerilerde bulunulmuştur.
Women in Difficult Life Situations: Managerial Practices of Social Services
Authors: Galindabaeva V. V.
Published: January 2020

The problem of managerialization of the social services practice in Russia and abroad is considered on the example of conducting evaluation studies of services for women in difficult life situations. Women in difficult life situations began to stand out in a separate category of social services clients, work with which requires the opening of specialized departments and the development of special programs. The article provides an overview of foreign evolutionary research services for this category. There are programs which value were considered obvious and not requiring evidence, but they turned out to be ineffective. At the same time, most programs have a positive effect on clients’ lives. One of the consequences of managerialization the social sphere abroad was the proliferation of evaluative studies of social programs. The author also considered the typology of evaluative research, which is offered by foreign scientists.
Peculiarities of Connection of Unconditional and Conditional Law Enforcement
Authors: Utkin G. N.
Published: January 2020

The article substantiates that the external, formal side of law enforcement is characterized by a complex combination of unconditional and conditional. With the external, formal side of it as unconditional (mandatory, categorical and unambiguous); with the internal, substantive side, the conditional (appropriate, due to the variable activity). As an independent, the question of how the features of connection in the law enforcement of unconditional and conditional should be taken into account in solving the problems of increasing its effectiveness is considered.
Attention-direction versus retrieval practice: which fosters the productive recall of German formulaic sequences best?
Authors: Boone Griet , Eyckmans June
Published: January 2020

This article reports on a classroom-based (quasi)-experiment with a pre-test post-test design that explored the effect of two types of activities on the productive recall of German formulaic sequences (FS): 1) attention-directing activities and 2) retrieval practice. Two intact classes of Dutch-speaking university students of German participated in the study. One class was randomly assigned to the attention-directing condition (n=18), the other one to the retrieval condition (n=11). Twenty-two target FS were selected as learning items. Each group processed the FS in a different condition. In the attention-directing condition, students had to 1) re-read a video transcript with the FS in bold typeface and 2) translate the targets into Dutch. In the retrieval condition, students had to 1) complete a transcript in which the FS were deleted and 2) translate the targets into German. Results indicate that the retrieval condition led to better productive phrase learning than the attention-directing condition.
Divulgación de información medioambiental: análisis exploratorio para las entidades cotizadas en Argentina
Authors: Rabasedas María
Published: January 2020

La divulgación de información contable medioambiental es una tendencia creciente a nivel mundial que busca responder a la intensa demanda social de rendición de cuentas empresarial.

Para contribuir al desarrollo de la contabilidad ambiental en Argentina, el presente trabajo indaga respecto a las tendencias de divulgación de información medioambiental en empresas cotizadas del país, examinado sus características y patrones determinantes.

Para lograrlo, se propuso la generación de un índice de divulgación medioambiental mediante la aplicación de la técnica de análisis de contenido sobre estados financieros y memorias de sostenibilidad de una muestra de empresas cotizadas en Argentina. Sobre los resultados obtenidos se condujo un análisis clúster, con el objeto de explorar características que definan a las entidades bajo estudio.

Los resultados permiten concluir que existe un bajo nivel de divulgación en general y alta dispersión de contenidos. Se conformaron tres grupos diferenciados de empresas de baja, media y alta divulgación, caracterizadas principalmente por el sector, tamaño y la presencia de una gerencia ambiental o similar. Estos hallazgos proporcionan una base para comenzar a evaluar el desarrollo de posibles estándares ambientales obligatorios, enfocados hacia las características empresariales y las necesidades de información.