Reviewerly/exHarmony huggingface dataset:
Clear-sky index space-time trajectories from probabilistic solar forecasts: Comparing promising copulas

Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm improves both the calibration and sharpness of the predictive distributions. Furthermore, we employ four copulas, i.e., (1) Gaussian, (2) Student-t, (3) Clayton, and (4) empirical, to generate space-time trajectories. The results highlight the necessity to rigorously assess the calibration of the space-time trajectories and the correct modeling of the spatiotemporal dependence structure, which we show through techniques introduced in atmospheric sciences. The code used to generate the results in this study can be found at https://github.com/DWvanderMeer/SpaceTimeTrajectories.
Use of disease embedding technique to predict the risk of progression to end-stage renal disease

The accurate prediction of progression of Chronic Kidney Disease (CKD) to End Stage Renal Disease (ESRD) is of great importance to clinicians and a challenge to researchers as there are many causes and even more comorbidities that are ignored by the traditional prediction models. We examine whether utilizing a novel low-dimensional embedding model disease2disease (D2D) learned from a large-scale electronic health records (EHRs) could well clusters the causes of kidney diseases and comorbidities and further improve prediction of progression of CKD to ESRD compared to traditional risk factors. The study cohort consists of 2,507 hospitalized Stage 3 CKD patients of which 1,375 (54.8%) progressed to ESRD within 3 years. We evaluated the proposed unsupervised learning framework by applying a regularized logistic regression model and a cox proportional hazard model respectively, and compared the accuracies with the ones obtained by four alternative models. The results demonstrate that the learned low-dimensional disease representations from EHRs can capture the relationship between vast arrays of diseases, and can outperform traditional risk factors in a CKD progression prediction model. These results can be used both by clinicians in patient care and researchers to develop new prediction methods.
A CitSci Approach for Rapid Earthquake Intensity Mapping: A Case Study from Istanbul (Turkey)

Nowadays several scientific disciplines utilize Citizen Science (CitSci) as a research approach. Natural hazard research and disaster management also benefit from CitSci since people can provide geodata and the relevant attributes using their mobile devices easily and rapidly during or after an event. An earthquake, depending on its intensity, is among the highly destructive natural hazards. Coordination efforts after a severe earthquake event are vital to minimize its harmful effects and timely in-situ data are crucial for this purpose. The aim of this study is to perform a CitSci pilot study to demonstrate the usability of data obtained by volunteers (citizens) for creating earthquake iso-intensity maps in a short time. The data were collected after a 5.8 Mw Istanbul earthquake which occurred on 26 September 2019. Through the mobile app “I felt the quake”, citizen observations regarding the earthquake intensity were collected from various locations. The intensity values in the app represent a revised form of the Mercalli intensity scale. The iso-intensity map was generated using a spatial kriging algorithm and compared with the one produced by The Disaster and Emergency Management Presidency (AFAD), Turkey, empirically. The results show that collecting the intensity information via trained users is a plausible method for producing such maps.
A Survey of Blockchain Applications in IoT Systems

This paper presents an extensive study of the different ways the Internet of Things (IoT) applications exploit the recently developed blockchain technology. Even though the blockchain technology was originally presented as a security mechanism, its numerous benefits such as decentralization, immutability, persistence, anonymity and auditability can be used by IoT systems in different ways. More specifically, we classify the ways IoT systems exploit blockchain into three categories: resource management, decentralized information sharing, and IoT security. The comprehensive study of the applications of the blockchain technology in the different IoT domains presented in this paper sheds the light on the future research directions in the integration of the two technologies.
A Multi-Modal Human Machine Interface for Controlling a Smart Wheelchair

As the number of disabled people all over the world is increasing very fast, the role of an electric wheelchair is becoming crucial to improve the mobility for them. Independent mobility is a vital aspect of self-respect and plays an important role in the life of a disabled person. The smart wheelchair is an endeavor to provide an self-supporting mobility to those people who are not able to move freely. Typical electric powered wheelchairs are usually controlled by the traditional joysticks which cannot fulfill the needs of a person who has motor disabilities and some specific types of disabilities like paralysis who can only move their eyes. This paper aims to develop a multi-modal human machine interface for the larger domain of disabled persons to control the wheelchair efficiently. The interface comprises joystick, smart hand-glove, head movement tracker and eye tracker. The system presented in this paper can support a wide variety of users with different types of disabilities.
Design and Implementation of a Web-Based Collaborative Authoring Tool for the Virtual Reality

Recent advances in the field of Virtual Reality (VR) using Head Mounted Display (HMD) has presented a new medium for immersive experiences. Designing for this medium does have its challenges, and designers and developers must adopt new design processes and pipeline for creating and manipulating virtual spaces in VR. There have been several works of research within similar fields of 3D manipulation with an HMD, but recent technological strides demand new efforts to translate prior knowledge toward the development of new and innovative tools. In this paper, we propose the design and implementation of a web-based authoring tool that exploits VR technology in the creation of the virtual spaces using an immersive Collaborative Virtual Environment (CVE). The purpose is a multi-user collaboration platform for the web browser adaptable to a wide variety of cases and purposes and which allows multiple and dissimilar stakeholders to create and manipulate virtual spaces in collaboration, either recreate real-world scenes or construct new digital environments using their imagination.
Assessment of Photovoltaic Capabilities in Urban Environments: A Case Study of Islamabad, Pakistan

Abstract Photovoltaic (PV) estimation in an urban environment requires detection of rooftop area, design of PV system based on optimization on PV placement distance and the study of additional benefit of lower cooling load of building by shading provided by PV panels. The study is aimed at policymakers to introduce renewable energy policy toward net-zero energy buildings in urban areas. In this research, the capital city of Pakistan, Islamabad, is analyzed for rooftop PV capabilities using deep learning algorithms. The area of the rooftop is calculated by extracting buildings in high-resolution satellite imagery using a deep learning algorithm. The site location is analyzed for available solar energy resources. The distance between the rooftop-PV array is optimized based on self-shading losses, coefficient of performance, energy yield, net-zero energy analysis, and reduction of cooling load of the building provided by PV arrays as shading devices. The 40-km2 area of Islamabad considered in this research can generate 1038 GWh of solar energy annually from its 4.3-km2 rooftop area by installed capacity of 447 MW PV panels rows placed at 0.75 m apart. The electricity generated by Islamabad can curtail residential load from the national grid and form a near net-zero energy zone while the electrical energy from the grid can be provided to the industries to enhance the economy and reduce unemployment in Pakistan.
Semi-supervised image depth prediction with deep learning and binocular algorithms

Combining the advantages and disadvantages of supervised learning and unsupervised learning strategies in convolution neural networks, this paper proposes a semi-supervised single-image depth prediction model based on binocular information and sparse laser data. The model improves the depth prediction accuracy by introducing sparse depth monitoring information, which provides a better convergence of the model with a local optimal solution. In the experiment, we validate the effectiveness of the model on the KITTI data set. Compared to the supervised algorithm, the root mean square error is reduced by 41.6% and, compared to the unsupervised algorithm, the root mean square error is reduced by 26.9%.
Closer to far away: transcending the spatial in transnational families’ online video calling

This paper studies how transnational children and their distantly located but emotionally close family members recreate their relationship using applications for online video calling. The focus is on the interaction of bodies and language, and if/how proximity of any kind is enabled. A critical posthumanist applied linguistics is embraced and communication is viewed as a bodily coordination ocurring in real time. This includes a material and dynamic view of language in constant transformation. Video captures are produced with three transnational, multilingual families in China and their adult relatives residing abroad (Europe). Moment analysis informs the processing of data. The analysis includes multipart semiotic assemblages of critical/creative moments and applies the Deleuzian concept of sense. The results suggest, the multi-local analogue/digital situation in online calling transcends conventionally imagined spatial ‘boundaries’. Furthermore, a bodily, multisensory proximity emerge as simultaneously critical to and created by this transcending spatiality. Multi-local communicative practices shed light on the multiple, material and semiotic components of the human senses, and how a rational understanding of proximity might be twisted. Proximity constantly emerges ‘in new copies' transcending the far away and close.
Real-Time Embedded Vision System for the Watchfulness Analysis of Train Drivers

Railways safety regulations require the drivers acting on a pedal to signal that they are awake and vigilant. This implies a noticeable biomechanical load on the driver. In this work, we propose an embedded computer-vision system that, using a single camera placed on the cockpit of a train, analyzes the driver's watchfulness by monitoring gaze and eyelid blinking. The proposed system provides a consensus to the control logic of the train, which replaces the action on the pedal. The system copes with the peculiar conditions of a train's cabin, such as variable illumination, the variability of the driver's face image and presence of more than one people in the cabin. At the same time, it accounts for the constraints posed by the international regulation (Safety Integrity Level IV) and customer specifications, which poses limitations on the hardware selection. The paper presents the design and evaluation of this system. The former includes the hardware selection and the algorithm development, whereas the latter includes preliminary tests for tuning the algorithm and final tests in real operating conditions for the evaluation of the system. The results show that the systems always correctly detects the driver's watchfulness and, importantly, it does not report false positives. As such, it can be used to avoid the driver's action on the pedal and reducing the biomechanical load.
Emoticons, memes and cyberbullying: gender equality in Colombia

In this paper, I want to analyze bullying and cyber bullying for the Colombian case. I want to show the role social media plays in the dissemination of hurtful words and symbols, especially emoticons and memes. This paper analyzes the long history of social mobilization where Courts have played an important role to advance their rights, and how, in some cases, social media, memes and emoticons have been used to express agreement or disagreement with this struggle. At the end, I want to analyze whether these emoticons, symbols, pictures or expressions fall within the reach of freedom of expression, according to Colombian Constitutional Court and the Inter-American Court of Human Rights.
Detection of Copy-Move Forgery Using Hybrid approach of DCT and BRISK

With the advancement in the image editing tools day by day and their increased usage, image forgery has become a serious problem. Copy-move forgery (CMF) is one of the most common image tampering techniques being used. There are many post-processing techniques applied on forged images such as scaling, blurring, JPEG compression and rotation to hide forgery traces. Single image might contain multiple forged regions. In this paper, we have proposed a hybrid method involving DCT (Discrete cosine Transform) and BRISK (Binary Robust Invariant Scalable Keypoints) features, for copy-move forgery detection. DCT is a feature set that would provide invariance to blurring, while BRISK would provide invariance to rotation and scaling. Features are extracted using DCT and BRISK keypoints and descriptors. For matching BRISK descriptors FLANN matcher is used, and for removing the false matches Euclidean distance-based clustering technique is used. The experimental result shows that our method is not only robust to blurring but also robust to transformations like rotation and scaling. It is also able to detect multiple forged regions. The method is tested on CoMoFoD dataset. Results of the proposed work are also compared with two standard approaches mentioned in experiment and result section.
Implementing User Rights for Research in the Field of Artificial Intelligence: A Call for International Action

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A Mixture IRTree Model for Performance Decline and Nonignorable Missing Data

In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior-such as performance decline, dropping out beyond a certain point, and skipping certain items over the course of the test-is inevitable, especially for low-stakes assessments and speeded tests due to low motivation and time limits, respectively. In this study, test-takers are classified as normal or aberrant using a mixture item response theory (IRT) modeling approach, and aberrant response behavior is described and modeled using item response trees (IRTrees). Simulations are conducted to evaluate the efficiency and quality of the new class of mixture IRTree model using WinBUGS with Bayesian estimation. The results show that the parameter recovery is satisfactory for the proposed mixture IRTree model and that treating missing values as ignorable or incorrect and ignoring possible performance decline results in biased estimation. Finally, the applicability of the new model is illustrated by means of an empirical example based on the Program for International Student Assessment.
Neural Network Modeling for Fuel Consumption Base on Least Computational Cost Parameters

Fuel consumption are important in every vehicle. This study investigates the performance of fuel usage in an engine. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. Back propagation neural network was used to determine the optimized fuel consumption. There is a lot of factor which has an effect to fuel consumption in conventional drive procedure, however in this study the factors affecting the fuel consumption are the distance, time, acceleration, and velocity of a car. These parameters are used as input information for the neural network training and fuel consumption prediction as output. This study shows the ANN capability to predict the fuel consumption using MATLAB neural fitting tool. The result demonstrated that the system using neural network is efficient for predicting fuel consumption of an engine.
An Extended Visual Cryptography Technique for Medical Image Security

In today's world, the internet is the fastest growing mode of communication. Hence, it is important to secure the data transmitted in this medium. The shared data could be in the forms of image, audio, video, etc. To provide secrecy of data many techniques have been proposed in the literature review. Visual cryptography is one among them. This paper proposes an Extended Visual Cryptography Technique (EVCT) for medical image security. Visual cryptography is a technique of sharing secret information in forms like images, text, etc. The technique proposed in the paper first encrypts the medical image and then embeds it into 3 cover images. Later on the receiver side, the secret image will be reconstructed from three shares (meaningful) followed by its decryption. The meaningful shares used in the proposed technique uses a 3X3 block size for each pixel in the secret image. No pixel expansion approach for encryption is proposed in the paper. The encryption and the embedding approaches are used in this process is lossless and less complex.
Long-distance continuous-variable measurement-device-independent quantum key distribution with postselection

We introduce a robust scheme for long-distance continuous-variable (CV) measurement-device-independent (MDI) quantum key distribution (QKD) in which we employ post-selection between distant parties communicating through the medium of an untrusted relay. We perform a security analysis that allows for general transmissivity and thermal noise variance of each link, in which we assume an eavesdropper performs a collective attack and controls the excess thermal noise in the channels. The introduction of post-selection enables the parties to sustain a secret key rate over distances exceeding those of existing CV MDI protocols. In the worst-case scenario in which the relay is positioned equidistant between them, we find that the parties may communicate securely over a range of 14 km in standard optical fiber. Our protocol helps to overcome the rate-distance limitations of previously proposed CV MDI protocols while maintaining many of their advantages.
Improving Information Sharing in Major Construction Projects through OC and POC: RDT Perspective

A vast amount of information is produced in each phase of major construction projects. However, extensive project lifespans and complex organizational relationships may contribute to significant information loss, attenuation, and separation during the transmission process. Consequently, some important information (i.e., regarding costs, schedules, materials, facilities, properties, etc.) may be suspended or interrupted when being passed to corresponding organizations; this seriously affects project management performance. One effective solution is to increase the amount of information sharing (IS). This study was aimed at exploring the mechanism of how organization change (OC) and process operation capability (POC) influence IS in major construction projects from the perspective of resource dependence theory (RDT). The relationships between IS, OC, and POC were examined using 444 questionnaires collected from managers working in major construction projects across Jiangsu, China. On this basis, indicators to measure OC, POC, and IS in the context of major construction projects were established. In addition, the effects of strategies that improve IS from OC and POC were quantified by combining the structural equation model (SEM) with a fuzzy cognitive map (FCM). The results showed that (1) both OC and POC have a positive impact on IS, and POC serves to mediate the influence of OC; (2) the indicators for measuring IS, OC, and POC demonstrate good reliability and validity; and (3) with mediation of POC, the effects of improvements to IS can be doubled when OC is increased, relative to implementing POC alone. This research contributes to the IS literature by demonstrating and quantifying the positive effects of OC and POC on IS from the perspective of RDT. Moreover, the identified indicators provide major construction projects with a promising tool for assessing IS, OC, and POC. Practical strategies for improving IS and managerial implications of the indicators are also discussed in the context of major construction projects.
Open CV based Information Extraction from Cheques

In general, bank cheques are used extensively for financial transactions in various organizations. Cheques are always verified manually. The traditional verification process will always include date, signature, legal information, and payment written on the cheques. In this paper, extracting the legal information from captured cheque image is obtained by preprocessing the image, extracting required information and then recognizing and verifying the handwritten fields. Image processing techniques like thinning, median filtering, dilation, and verification techniques are also employed in this approach.
Post-quantum digital signature scheme based on multivariate cubic problem

Today, with the advent of internet technology, we are looking for e-mechanisms such as e-voting, e-commerce, e-learning, etc., where electronic information are transferred between the entities via the public network. However, e-mechanisms require the support of integrity, authenticity and non-repudiability of the transmitted electronic information. The digital signature is a technique that allows users to attain these parameters during the transmission of information via the public channel. The existing number-theoretic assumption based digital signature schemes is vulnerable to quantum attacks due to the development in a quantum computer. Thus, there is a necessity of quantum computer resistant digital signature scheme, i.e., post-quantum digital signature. Multivariate Public Key Cryptography (MPKC) is one of the most promising candidates of post-quantum cryptography as the MPKC based constructions are computationally fast and need only modest computational resources. In the literature, there are few multivariate digital signature schemes based on Multivariate Quadratic (MQ) problem. However, the design of efficient constructions of digital signature schemes based on higher degree ( > 2) multivariate polynomials is still an open problem. Generally, the question relating to the multivariate polynomials of degree > 2 is expected to be equally or harder than the quadratic one. In this paper, we have designed a digital signature framework based on Multivariate Cubic (MC) problem to address the issue. The signature size in our scheme is less than all the existing MPKC based signature schemes under the same security assumptions.