Reviewerly/exHarmony huggingface dataset:
From Exploration to Re-Enactment: Instructional Uses of a Desktop Virtual Environment for Training Nuclear Plant Field Operators

Abstract Despite the growing use of virtual environments for training in complex industrial settings, we have little understanding of how these innovative settings transform training and trainers everyday work. This study investigates the instructional use of an industrial building’s virtual environment by expert trainers during a training session for nuclear power plant field operators. Drawing from the course-of-action theoretical and methodological framework, field notes, continuous video-recording of the training sessions and verbalisations during post-training self-confrontation interviews with the trainers were collected and analysed. The results point out four typical instructional uses of the virtual environment in authentic settings: (a) showing the material elements and spatial layout of certain areas of the reactor building, (b) displaying safe and typical paths through the building, (c) explaining functional aspects to help trainees develop an operating model of the nuclear building, and (d) sharing salient experience through real-life anecdotes. These typical uses and their related learning dimensions are anchored in the re-enactments of expert trainer’s embodied past events. The discussion develops the counterintuitive idea that from an instructional point of view, the intensified immersion of trainers afforded by the virtual environment seems less influential than emerging practice-based learning experiences. We conclude with new possibilities for improving learning through and for work thanks to re-enactment of expert trainers’ past work practices.
The intellectual property road to the knowledge economy: remarks on the readiness of the UAE Copyright Act to drive AI innovation

Copyright law in the United Arab Emirates (UAE) has the capacity to address the challenges associated with artificial intelligence (AI)-generated literary, artistic and scientific works. Under UAE copyright law, AI-generated works may qualify as copyright subject matter despite the non-human nature of both the expressions and the innovative character they embody. Also, users of the AI systems generating works may qualify as the authors of these works and, in most cases, bear the responsibility for their copyright infringing activities. These conclusions rely on: (1) the premise that the notions of ‘work’ and ‘author’ for the purpose of copyright law are legal constructs, impacted by both socio-economic and technological factors; (2) the wording of the UAE Copyright Act, reflecting an underlying reconciliation between the economic and moral dimensions of copyright; (3) the potential utility of the UAE Copyright Act’s notion of ‘collective works’, which echoes the work-for-hire doctrine in other national copyright laws; and (4) the overarching knowledge economy-oriented policy in the UAE.
Non-Prismatic Beam-Like Structures with 3D Cross-Sectional Warping

Many complex engineering structures, e.g.blades of wind turbines and helicopters, are beamlike and non-prismatic.They may be tapered, twisted and curved in their unstressed state, undergo large displacements of the centre-line, and cross-sectional warping in and out of plane.For their structural modeling, an approach based on beam elements can be the best compromise between computational efficiency and accuracy, but classical beam models (see, for example, the monumental Love's treatise) may not be sufficient.Better results may be obtained by exploiting geometrically exact and asymptotic approaches.This paper proposes a physical-mathematical model for the aforementioned non-prismatic structures.Analytical results obtained for small warping and strain fields are presented and compared to the results obtainable from nonlinear 3D-FEM analyses.
Incorporating Transaction Lifecycle Information in Blockchain Process Discovery

Blockchain process discovery aims to exploit event log emitted by smart contracts to understand the underlying business processes that are enacted within it. Current existing methods perform event log extraction by decoding the event signature of blockchain smart contract in which intra-activities were considered. However, as blockchain transactions are subject to an inclusion time, the challenge in understanding the temporal constraint presented within blockchain architectural design remains open as existing techniques do not incorporate such information. In this paper, a framework to acquire a business process model from historical event data of digital assets stored in the Ethereum blockchain is proposed. Departs from the existing methods, this framework takes into account the inclusion time when a transaction is being submitted to the blockchain. Upon the incorporation of inclusion time into an event log, sequential analysis towards the stay time of a process instance arrived at a particular state, could be carried accordingly. The framework is demonstrated against CryptoKitties ERC-721 token event log, the most popular digital asset in Ethereum known to date. Furthermore, the way on how the framework relates to a blockchain-IoT scenario is described.
Monitoring and controlling the speed and direction of a DC motor through FPGA and comparison of FPGA for speed and performance optimization

&lt;span lang="EN-US"&gt;We are living in the 21&lt;sup&gt;st&lt;/sup&gt; century, an era of acquiring necessity in one click. As we, all know that technology is continuously reviving to stay ahead of advancements taking place in this world of making things easier for mankind. Technology has been putting his part in introducing different projects as we have used the field programmable gate arrays (FPGAs) development board of low cost and programmable logic done by the new evolvable cyclone software is optimized for specific energy based on Altera Cyclone II (EP2C5T144) through which we can control the speed of any electronic device or any Motor Control IP product targeted for the fan and pump. Altera Cyclone FPGAs’ is a board through which we can monitor the speed and direction of the DC motor. As we know how to make understand, dynamic analog input using an A-to-D convertor and we know how to create pulse width modulation (PWM) output with FPGA. Therefore, by combining these two functions we can create an FPGA DC motor controller. Our paper is divided into three parts: First, all of us will attempt to imitate the issue and can try to look for its answer. Secondly, we will try to verify the solution for real-time. In addition, in the last step, we will verify the solution on the real-time measurements.&lt;/span&gt;
Web Content Strategy in Practice within Academic Libraries

Web content strategy is a relatively new area of practice in industry, in higher education, and, correspondingly, within academic and research libraries. The authors conducted a web-based survey of academic and research library professionals in order to identify present trends in this area of professional practice by academic librarians and to establish an understanding of the degree of institutional engagement in web content strategy within academic and research libraries. This article presents the findings of that survey. Based on analysis of the results, we propose a web content strategy maturity model specific to academic libraries.
An Overview of Existing Publications and Most Relevant Projects/Platforms on the Use of Blockchain in Medicine and Neurology

Background Blockchain is a new methodology involving a data structure with list of records, called blocks, which are linked using cryptography. The aim of the review is to overview the existing publication, projects, and platforms on the use of blockchain in Medicine and Neurology. Methods We searched the bibliographic database of MEDLINE and BASE. We also accessed ICObench, Coinmarketcap, and Mobihealthnews databases to explore upcoming, ongoing, and ended projects. Results In medicine, there are many projects related to health care, disease prevention, and promotion of healthy life style. In neurology, only one project looks promising: Neuro, an ongoing scientific-technical project uniting scientists, engineers, and programmers for development of new architectures and algorithms of neural networks. Bibliographic searches found 117 publications on Medline and 203 publications on BASE referring to the use of blockchain technology in medicine. Most of them are presented as reviews (narrative, systematic, or minireview), opinions and hypotheses, commentaries, or perspectives. As for Neurology, only one publication refers to the use of blockchain, specifically to Parkinson’s disease. Discussion Among the problems related to medicine, there is the lack of information on the patient’s clinical history that could allow accurate diagnosis and treatment. The possibility of having a register based on blockchain technology could help doctors in many ways, including patient management, choosing and monitoring treatments, and standardization of clinical trials. Conclusion The use of the blockchain technology in medicine has been repetitively proposed to solve different problems. In this article, we highlight the possible benefits of this technology, with attention to Neurology. Blockchain use can lead to quantifiable benefits in the treatment of neurodegenerative diseases, especially in clinical trials that can fail because of an incorrect patient recruitment.
Modeling solar potential in Semarang, Indonesia using artificial neural networks

Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result was presented in a monthly map of solar potential within the geographical information system (GIS) environment. The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance map can be used by the government of Semarang City to provide information about the solar energy profile for the implementation of the solar energy system.
Occluded pedestrian detection combined with semantic features

The task of pedestrian detection is to identify the location and size of pedestrians in images or videos. However, occlusions are very common in real-life scenarios, which make pedestrian detection more difficult. In order to solve the occlusion problem in pedestrian detection, a semantic feature enhancement module to acquire more informative and richer semantic features is proposed. The detector enhances semantic features by fusing feature maps of different layers, and detects pedestrians based on their locations and scales. The Experiments performed on Caltech and CityPersons datasets show that the algorithm achieves superior performance for detecting occluded pedestrians, especially heavily occluded ones. 30.6% and 47.9% log-average missing rates are achieved in the heavily occluded subsets of Caltech and Cityperons, respectively. Moreover, this method is robust to the detection of heavily occluded pedestrians, and the module can be easily used by other detection frameworks.
Facial Expression Recognition With Confidence Guided Refined Horizontal Pyramid Network

Facial expression recognition has become one of the most studied applications in computer vision and human-computer interaction. Part-level features constitute one the most appealing breakthrough to offer fine-grained information and have been intensively studied consequently. The main prerequisite is accurate location of facial parts with additional cues, which is turned to be another difficulty. Rather than directly locating parts, this paper proposes a confidence guided Refined Horizontal Pyramid Network (RHPN) to fully exploit various partial information of a given facial image. It can learn discriminative features with multiple facial granularities. Inconsistencies within each granularity are efficiently limited. Specifically, we first design a Horizontal Pyramid Network (HPN) for classification using the uniform partial feature representations at different horizontal pyramid scales. It successfully enhances the discriminative capabilities of various facial parts. Then, a refinement mechanism is added to HPN due to the fact that the uniform partition inevitably incurs outliers in each part and introduces unreasonable similarity between different parts. It re-assigns these outliers to the parts they are closest to, resulting in the refined parts with enhanced within-stripe consistency. Due to the lack of explicit supervisory information, we design an induced training strategy additionally for efficiently training of RHPN. For avoiding the finagle of prediction based on the separate feature vector, we prefer the confidence guided prediction as the final classification result. Experiments verify the effectiveness of our network for increasing the performance on facial expression recognition. More importantly, it surpasses the state-of-the-art by a large margin on the lab-controlled Oulu-CASIA dataset and the real world RAF-DB dataset.
Assessing The Cost, Legal Fallout Of Capital One Data Breach

This article sheds light on the potential cost that could be incurred by Capital One in the wake of the data breach, as part of our efforts to help professionals and the general public fathom the magnitude of the impact. As the investigation continues, further information will be revealed. We hope this article will also offer a framework for those monitoring the investigation to organize and better digest the additional information that will be revealed.
Analysis of Conditions for Reliable Predictions by Moodle Machine Learning Models

In this paper the issue of bias-variance trade-off in building and operating Moodle Machine Learning (ML) models are discussed to avoid traps of get-ting unreliable predictions. Moodle is one of the world’s most popular open source Learning Management System (LMS) with millions of users. Although since Moodle 3.4 release it is possible to create ML models within the LMS system very few studies have been published so far about the conditions of its proper application. Using these models as black boxes hold serious risks to get unreliable predictions and false alarms. From a comprehensive study of differently built machine learning models elaborated at the University of Dunaújváros in Hungary, one specific issue is addressed here, namely the in-fluence of the size and the row-column ratio of the predictor matrix on the goodness of the predictions. In the so-called Time Splitting Method in Moo-dle Learning Analytics the effect of varying numbers of time splits and of predictors has also been studied to see their influence on the bias and the variance of the models. An Applied Statistics course is used to demonstrate the consequences of the different model set up.
FIXMYPOSE: Pose Correctional Captioning and Retrieval

Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend, and people globally enjoy such exercises at home/office via video streaming platforms. However, such exercises are hard to follow without expert guidance. Even if experts can help, it is almost impossible to give personalized feedback to every trainee remotely. Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect natural language descriptions of correcting a “current” pose to look like a “target” pose. To support a multilingual setup, we collect descriptions in both English and Hindi. The collected descriptions have interesting linguistic properties such as egocentric relations to the environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our FixMyPose dataset, we introduce two tasks: the pose-correctional-captioning task and its reverse, the target-pose-retrieval task. During the correctional-captioning task, models must generate the descriptions of how to move from the current to the target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and the correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. Finally, to verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images. Data and code are available: https://fixmypose-unc.github.io.
A Practical Analysis of the Fermat Factorization and Pollard Rho Method for Factoring Integers

The development of public-key cryptography generation using the factoring method is very important in practical cryptography applications. In cryptographic applications, the urgency of factoring is very risky because factoring can crack public and private keys, even though the strength in cryptographic algorithms is determined mainly by the key strength generated by the algorithm. However, solving the composite number to find the prime factors is still very rarely done. Therefore, this study will compare the Fermat factorization algorithm and Pollard rho by finding the key generator public key algorithm's prime factor value. Based on the series of test and analysis factoring integer algorithm using Fermat's Factorization and Pollards' Rho methods, it could be concluded that both methods could be used to factorize the public key which specifically aimed to identify the prime factors. During the public key factorizing process within 16 bytes – 64 bytes, Pollards' Rho's average duration was significantly faster than Fermat's Factorization.
Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.
Dalgacık Aşırı Öğrenme Makinesi Otomatik Kodlayıcılarına Dayalı Öğretmenlerde EEG, EMG ve EKG Tabanlı Psikososyal Risk Düzeylerinin Belirlenmesi

Teachers who perform a sacred work are faced with many psychosocial risks. These risks can often be caused by the school administration, the students, and environmental factors. Machine learning and data mining approaches have recently gained much attention in social and educational researches. In this study, a novel approach, which is based on data augmentation and data classification, is proposed for the prediction of the psychosocial risk levels of the teachers. The data augmentation is carried out by using an extreme learning machine autoencoders (ELM-AE). More specifically, the wavelet activation function is incorporated into the ELM-AE to develop a novel approach called WELM-AE. After data augmentation, a traditional ELM classifier is used in the prediction of the psychosocial risk levels of teachers. A dataset, which contains physiological factors, namely Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG), is used to evaluate the performance of the proposed method. Classification accuracy is used as the evaluation metric. All coding is carried out in MATLAB, and a 99.9% accuracy score is obtained with the proposed method. A performance comparison is also carried out with some machine learning techniques, namely decision trees (DT), support vector machines (SVM), and K-nearest neighbour (KNN). The results show that the proposed WELM-AE and ELM classifier outperform the compared methods.
Design of a Fractional Pseudo-Chaotic Random Number Generator

In this paper, we design a pseudo-chaotic random number generator using fractional chaotic systems.A non-uniform grid calculation method is proposed and employed to numerically solve the fractional systems by introducing a skew-tent map to vary the step size of the grid.Greater chaoticity in terms of Lyapunov exponent has been achieved by applying the proposed approach to the solution calculation of the fractional chaotic Chen's and Lu's systems.By adopting the piecewise constant argument method, one 1D fractional generalized double-humped logistic system (FGDHL) is discretized.A fractional pseudo-chaotic random number generator (FPCRNG) has been proposed by performing XOR (exclusive-or) operations to the states of the fractional Chen's system, fractional Lu's system, and the FGDHL systems.The security analysis of the generator and the statistical experiment of a stream cipher implementing the proposed FPCRNG prove that the proposed structure is efficient and can be used in the cryptosystem design.