Reviewerly/exHarmony huggingface dataset:
Hybrid Hadamard and controlled-Hadamard based quantum random number generators in IBM QX

Abstract Designing novel circuits and models for generating truly random numbers is of great importance. Recent attention has been focused on the idea of using real quantum computers to generate random numbers. In order to generate purely random bits and numbers, one method relies on the natural superposition of quantum gates. Here, we design a Quantum Random Number Generator (QRNG) exploiting combinations of Hadamard and controlled-Hadamard gates in IBM Quantum Experience (QX). In order to verify that the generated bit sequences were truly random, the proposed circuit’s outputs were subjected to the National Institute of Standards and Technology (NIST) test suite. In comparison to previous works, we found that not only had the total number of passed tests increased, but the P-values had also improved in many cases. For example, in the simulation style of the IBM QX, the number of passed tests is 14 out of 15 standard NIST tests. For the experimental mood of the IBM 16Q Melbourne, the number of passed tests is six without using any extractions. Furthermore, one more test is passed by adding Barrier operations to the circuits, and the P-values have increased. As a result, we found that the proposed method had more passed tests than previous works. The average of P-values for the six passed tests in former research for the mood of using a combination of extractors is about <?CDATA $0.43.$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.43</mml:mn> <mml:mo>.</mml:mo> </mml:math> However, the best result we can get with our circuits is around 0.60 (the average of P-values for 14 passed tests), which is an improvement of 0.17 compared to former works . Because of this, our circuits are getting ever closer to being a true QRNG.
Infinite technical debt

Code ridden with Technical Debt (TD) has motivated software engineers to keep the quality of systems under control to ease future maintenance tasks. In the last decade, there have been significant advances regarding TD management (TDM). However, research about incorporating TDM into the software development lifecycle remains scarce, and existing approaches aim to control TD through different processes. This proposal leverages the concept of infinite games from game theory to posit a different perspective. We argue that TD cannot be entirely removed and that its effects or consequences cannot be considered “managed” even when an occurrence (i.e., a smell) is repaid. Rather than using a mathematical approach, we present TDM in terms of the four components of infinite games (players, rules, goals, and time), its tradeoffs and relationships, to discuss its potential impact on TDM activities. As this is an incipient area, our goal is to motivate a change of mindset regarding TDM, stimulating reflective thinking and thus, posing a new line of research. We conclude with a series of potential research questions organised into three key areas.
Lightweight Modified Consensus Approach in IoT Blockchain

Nowadays, the decentralized and distributed nature of blockchain in IoT platforms is gaining lot of acceptance. PoW is the most commonly used consensus approach in blockchain. But energy requirements of consensus approaches is very high. Due to which the implementation of blockchain in IoT becomes very difficult. To solve the problem of resource constrained devices, we proposed modified consensus approach for non-cooperative IoT blockchain platform. Our scheme also works like PoW for specific period of time and checks for the count of leading zeroes given by block manager. IoT network is divided into regions and each region contains regional head for interaction with the block manager. Usecase of e-healthcare is also provided to explain the scenario of our scheme. Energy consumption, consensus time, and latency are the performance parameters taken into consideration.
Combination model for day-ahead solar forecasting using local and global model input

Numerical Weather Prediction (NWP) models over limited areas enable the simulation of local atmospheric processes in more detail and with a higher degree of accuracy when compared to global models. Limited-area NWP models can outperform their global counterparts due to higher resolution (ability to explicitly simulate processes) and tailored physics (global models, unless run as a physics ensemble, have one set of parameterization schemes for the whole globe). However, increased accuracy from an NWP model is not guaranteed and can vary based on the location and variable of interest. In this paper, we present a method for combining the output of a limited-area NWP model, the Weather Research and Forecasting model (WRF) and its global model—the European Center for Medium Range Weather Forecasting (ECMWF) deterministic model. We simulate day-ahead global horizontal irradiance for a location in Qinghai, China. WRF model configurations optimized by the type of day (cloud amount) are then implemented based on the ECMWF model forecast of cloud amount. A regression model to combined ECMWF and WRF model forecasts is then trained. The optimized coefficients (weights) of ECMWF and WRF show increasing WRF importance with higher cloud amounts and the combination out-performs the ECMWF input by 5.2% and the best WRF configuration by 7.2% on a 2.5-month testing set. The performance of the combined model increased with observed cloud amount where the combined model out-performed the ECMWF model by 12.6% for cloudy days indicating the relative importance of physical downscaling for the simulation of clouds.
Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.
Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores

In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. Therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations.
Car Damage Detection and Assessment Using CNN

In today's digital world, most businesses are adopting technology in every possible way. Many time it occurs that when the car is damaged insurance claims are done. If the car is insured, a person from the insurance industry visits and takes survey of the customers car and prepares the report. The manual verification is a tedious process. But with the major advancement in field of deep learning algorithms, it can be used in the insurance industry to solve these problems. In the proposed solution we have implemented 2 CNN models. VGG16 is used to detect the damage on the car, location of the damage and its severity. Mask RCNN is used to mask out the exact damaged region. Both the models give a fair idea about the damage caused to the car which can help insurance company to proceed further with the insurance claims without wasting time and resources on manual verification.
Colorizing Grayscale CT images of human lungs using deep learning methods

Image colorization refers to computer-aided rendering technology which transfers colors from a reference color image to grayscale images or video frames. Deep learning elevated notably in the field of image colorization in the past years. In this paper, we formulate image colorization methods relying on exemplar colorization and automatic colorization, respectively. For hybrid colorization, we select appropriate reference images to colorize the grayscale CT images. The colours of meat resemble those of human lungs, so the images of fresh pork, lamb, beef, and even rotten meat are collected as our dataset for model training. Three sets of training data consisting of meat images are analysed to extract the pixelar features for colorizing lung CT images by using an automatic approach. Pertaining to the results, we consider numerous methods (i.e., loss functions, visual analysis, PSNR, and SSIM) to evaluate the proposed deep learning models. Moreover, compared with other methods of colorizing lung CT images, the results of rendering the images by using deep learning methods are significantly genuine and promising. The metrics for measuring image similarity such as SSIM and PSNR have satisfactory performance, up to 0.55 and 28.0, respectively. Additionally, the methods may provide novel ideas for rendering grayscale X-ray images in airports, ferries, and railway stations.
Nurse Perceptions of the Usability of Augmented Reality to Support Clinical Decision Making: Results of a Pilot Study

Clinically, nurses must rapidly identify deteriorating patients and escalate patient care to adverse events. Novices, however, can easily succumb to cognitive overload. Augmented-reality (AR) devices, such as the Microsoft HoloLens 2, may help nurses attend to task-relevant information more effectively. The aim of this pilot study was to assess experienced nurses&#x0027; perceptions on the usability of AR. Practicing nurses were recruited for this study. Following a brief tutorial, demonstration and hands-on use of the HoloLens, nurses completed the system-usability scale (SUS) to rate usability. Additionally, interviews were conducted after the simulated use session. Experienced nurses (n&#x003D; 11) rated the usability of AR as 62.5&#x00B1;7.8. Themes that emerged from our open-ended interviews included the need for AR in nursing education and the potential benefit of a patient care checklist. Use of AR to support nurse decision making may reduce cognitive workload and focus attention on critical areas to recognize patient deterioration.
A Tangible Augmented Reality Programming Learning Environment for Textual Languages

We present a novel Tangible Augmented Reality Programming Learning Environment system that uses a head-mounted display (HMD) and physical manipulatives for teaching an Object-Oriented Programming (OOP) language. The system supports student understanding/recollection of terms, and construction of statements by enabling access to code components, terminology, and programming hints. It is designed to use the affordances of Augmented Reality (AR) and Tangible User Interfaces (TUIs) to provide a virtual workspace encouraging natural interaction with learning material. An interactive AR code template for physical manipulatives provides a building and testing environment for learners to practice statement construction and computational skills. The system bolsters active learning with a localised AR program visualisation and HMD-anchored AR glossary.
How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rank

Learning to Rank (L2R) improves ranking quality but relies on the existence of manually labeled training sets, which are expensive and cumbersome to generate. Using automated labeling (e.g., clickthrough data) imposes its own challenges. Active learning (AL) can be used to gather high-quality training data by producing very informative yet small training sets. Cover, a method we have previously developed, allows for unsupervised sampling of training sets as good as those created using AL. In this paper we provide an extensive analysis of how and why Cover works. We revisit the method in a more formal way, with theorems and proofs, and provide additional empirical evidence of its practicality. We answer questions related to why Cover works so well and how its properties are related to AL methods. We show how certain characteristics of Cover’s clustering step allows it to more thoroughly explore the feature space by selecting query-document pairs that are representative and diverse, allowing L2R methods to produce effective models. The main novel contribution is a detailed analysis of the method’s inner workings and information-theoretic properties, allowing us to advance the understanding of L2R fundamentals through the lens of training set building.
Tilt correction to maximize energy yield from bifacial PV modules

Abstract The ability of bifacial photovoltaic (PV) modules to generate additional energy from the rear side makes the selection of a tilt angle more challenging than its counterpart monofacial PV. Multiple factors such as site conditions (albedo, average sun-hour ambient temperature, elevation from sea level, diffuse fraction, global horizontal irradiance, and latitude) and PV module specifications (such as bifaciality, power temperature coefficient, and efficiency) can influence the optimum tilt that ensures maximum annual yield of the bifacial PV modules. System Advisor Model (SAM) is used to generate a training dataset of optimal tilt that covers a global range of site conditions and module specifications. Furthermore, we used a Ranker-based feature selection approach to evaluate features and generate a rank-list based on their R-squared score to the dependent variable. From all the available features, the absolute latitude value, sun hour ambient temperature, global horizontal irradiance, and albedo are the most significant features. The best results offer a mathematical model to predict the optimum tilt with the lowest RMSE of 2.135° and an R 2 of 0.96 given 8 site and module features.
Hardware routed quantum key distribution networks

Quantum communication networks pose immense potential for revolutionising secure communications for several applications such as banking, defence, etc. The majority literature on quantum networks deals with the problems of networking and resource allocation using Software Defined Networks (SDNs). SDNs, however, introduce several issues, such as app manipulation attacks and scalability issues. We propose a novel scheme of implementing quantum communication networks that are hardware routed rather than software defined by labelling qubit photons using laser communications. We provide a comprehensive implementation of the new scheme and propose two novel algorithms—Bandwidth sharing and Equitable bandwidth sharing to implement the hardware routed quantum network. The algorithms result in a key rate increase of 118% and improved network resource utilization of 147% as compared to the First Come First Serve algorithm.
A genetic programming-based convolutional neural network for image quality evaluations

Abstract Monitoring the perceptual quality of digital images is fundamentally important since digital image transmissions through the Internet continue to increase exponentially. Many automatic image quality evaluation (IQE) metrics have been developed based on image features correlated to image distortions; however, those metrics are only effective on particular image distortion types. In recent years, convolutional neural network (CNNs) have been developed for IQEs. These CNNs first capture image features from distorted images; image qualities are predicted based on the captured image features. Since the CNN weights are randomly initialized and are updated with respect to a loss function, image features which are strongly correlated to image quality are not guaranteed to be captured. In this paper, a hybrid deep neural network (DNN) is proposed by integrating image quality metrics to capture image features which are correlated to image quality; the approach guarantees that significant image features are included to predict image quality. Also, a tree-based classifier namely geometric semantic genetic programming is proposed to perform the overall predictions by incorporating CNN predictions and image features; the approach is simpler than the fully connected network but is able to model the nonlinear image qualities. The performance of the proposed hybrid DNN is evaluated by an image quality database with 3000 distorted images. The mean correlation achieved by the proposed hybrid DNN is 0.57 which is higher than the other tested methods. Experimental results with the t - test, F -test and Tueky’s range tests show that the proposed hybrid DNN achieves more accurate image predictions with a 99.9% confidence level, compared to the state-of-the-art IQE metics and the most recently developed CNN for IQEs.
An improved Criminisi method for image inpainting

Abstract The Criminisi method is a classical image inpainting method, which has flaw of unreasonable priority calculation. To overcome this flaw, we propose an improved Criminisi method. To improve the accuracy of a repaired block’s priority, we propose a novel method by considering the continuity of structure and color information between the repaired block and its adjacent blocks. When searching the matching block for a repaired block, we compute the similar between two blocks as the sum of squared differences (SSD). Since different blocks within an image may contain different levels of information, we set block to different sizes to determine the best matching block. Experimental results demonstrate the effectiveness of the proposed method.
Virtual Reality in Small and Medium-Sized Enterprises: A Systematic Literature Review

The industry is currently operating in the fourth industrial revolution, also called Industry 4.0, the central element of which is the digitization of companies. One promising implementation of Industry 4.0 is the use of virtual reality (VR). Due to its development in terms of better affordability and accessibility in recent years, VR has gained increasing popularity. This creates new opportunities especially for small and medium-sized enterprises (SMEs) to use VR to boost business and optimize their product development and processes. In this paper, we aim to identify the potentials and challenges of using VR in SMEs by means of a systematic literature review. Therefore, we focus on analyzing the current state of the art, including use cases, used technology, opportunities, and challenges faced by the companies.
MeasVRe: Measurement Tools for Unity VR Applications

An indispensable facility that is often missing in VR applications for studying complex 3D models, is the ability to take measurements. This paper describes a toolkit for Unity VR applications that allows for taking distance, angle, trace, surface area, and bounding box volume measurements by placing markers. We show that markers can be efficiently snapped to a model by ray casting. Moreover, we validated taking distance measurements with our toolkit, by performing an experiment where we measure inter-branch distances of corals. Lastly, the toolkit can save measurements locally or upload them to a logging server.
An Edge Detection Based Reversible Data Hiding Scheme

Edge detection based embedding techniques are famous for data security and image quality preservation. These techniques use diverse edge detectors to classify edge and non-edge pixels in an image and then implant secrets in one or both of these classes. Image with conceived data is called stego image. It is noticeable that none of such researches tries to reform the original image from the stego one. Rather, they devote their concentration to extract the hidden message only. This research presents a solution to the raised reversibility problem. Like the others, our research, first, applies an edge detector e.g., canny, in a cover image. The scheme next collects <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -LSBs of each of edge pixels and finally, concatenates them with encrypted message stream. This method applies a lossless compression algorithm to that processed stream. Compression factor is taken such a way that the length of compressed stream does not exceed the length of collected LSBs. The compressed message stream is then implanted only in the edge pixels by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -LSB substitution method. As the scheme does not destroy the originality of non-edge pixels, it presents better stego quality. By incorporation the mechanisms of encryption, concatenation, compression and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -LSB, the method has enriched the security of implanted data. The research shows its effectiveness while implanting a small sized message.
Answering With Bow and Arrow: Questionnaires and VR Blend Without Distorting the Outcome

Psychological factors to be assessed by questionnaires, such as presence, may be influenced by variables like a break in presence induced by transitioning between physical and virtual reality - or time elapsed between the actual sensation and its measurement when questionnaires are used. Moreover, participants of previous experiments stated discomfort due to changing repeatedly between VR and non-VR. These aspects motivate the investigation of new ways of administering questionnaires in VR research. Previous attempts to integrate questionnaires into VR as world- or user-anchored canvases still lead to a clear separation between interaction for task and data acquisition in the virtual environment, which has been shown as problematic due to interruption of experience. Therefore, this work goes one step further by integrating the answering of the questionnaire into the actual task using the same interaction technique and metaphor. We implemented a bow and arrow game in which the player had to shoot at randomly spawning targets as fast and accurate as possible. Subsequently, the player had to answer each item of a questionnaire by actually shooting at a target that represented the rating with the same type of interaction method. No difference in presence (SUS-PQ), satisfaction (ASQ) or workload (SMEQ) ratings was found between questionnaires presented either embedded in VR (as described above), as text panel in VR or as desktop PC version. In conclusion, the embedded questionnaires may render less negative effects of transitioning between VR and non-VR without impairing the questionnaire results.