Reviewerly/exHarmony huggingface dataset:
The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R2) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.
Maturity level assessments of information security controls: An empirical analysis of practitioners assessment capabilities

Maturity models are a widely used concept for measuring information security. The idea is to systematically evaluate the maturity of security-relevant processes in an organisation. This enables decision-makers to get an overview of the implementation status of relevant processes to identify neuralgic points. Maturity models thus play a central role in the conception of information security management systems (ISMS). Some industries, for instance, the German automotive industry, have even established security maturity levels as the de facto standard for measuring information security. However, the quality of security maturity level assessments has not been sufficiently investigated yet. Therefore, we have analysed to what extent security managers can accurately assess the maturity levels of security controls. To verify the quality of maturity level assessments a case study was conducted where security experts assessed a subset of the ISO/IEC 27002 security controls for a hypothetical scenario using the COBIT maturity levels. Additionally, ex-post interviews have been conducted with several participants of the study to verify some of the hypotheses developed during the previous analyses. Our results show that many security experts struggled with the task and did not perform well. However, we discovered professional characteristics that have a strong significant effect on the assessment capabilities. We also identified various types of additional support that can help practitioners to make more reliable assessments in practice. Moreover, the experts' self-perception was overly optimistic when asked to assess their performance. We even found a weak inverted correlation for more experienced experts, also known as Dunning-Kruger effect. Our results have a strong impact on practice since they indicate that practitioners need support to carry out high-quality assessments and they also show what kind of support addresses the identified challenges.
Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.
Optimizing Decoy‐State Protocols for Practical Quantum Key Distribution Systems

Abstract The decoy‐state protocol enables quantum key distribution (QKD) systems to achieve high performance without using the single‐photon source. In the dozen years since the decoy‐state protocol has been proposed, a series of advances in scheme design, finite‐size analysis, system modelling, and parameter estimation has developed. Unfortunately, most advances are based on different starting points and lack a synthesis to figure out the optimal protocol of decoy‐state method. Here, the advances in decoy‐state method are reviewed and they are synthesized to compare the key‐rate performance of 38 decoy‐state protocols using the particle swarm optimization (PSO) algorithm. To form a guideline for practical QKD systems, the optimal decoy‐state protocols are found with the change of five system parameters (the transmission loss, the block size of postprocessing, the misalignment‐error probability, the dark‐count probability, and the afterpulse rate). It is expected that this work can answer the questions of what the actual performance is for different decoy‐state protocol in various scenarios and which decoy‐state protocol is the optimal choice for various QKD systems.
Whole Heart Segmentation Using 3D FM-Pre-ResNet Encoder–Decoder Based Architecture with Variational Autoencoder Regularization

An accurate whole heart segmentation (WHS) on medical images, including computed tomography (CT) and magnetic resonance (MR) images, plays a crucial role in many clinical applications, such as cardiovascular disease diagnosis, pre-surgical planning, and intraoperative treatment. Manual whole-heart segmentation is a time-consuming process, prone to subjectivity and error. Therefore, there is a need to develop a quick, automatic, and accurate whole heart segmentation systems. Nowadays, convolutional neural networks (CNNs) emerged as a robust approach for medical image segmentation. In this paper, we first introduce a novel connectivity structure of residual unit that we refer to as a feature merge residual unit (FM-Pre-ResNet). The proposed connectivity allows the creation of distinctly deep models without an increase in the number of parameters compared to the pre-activation residual units. Second, we propose a three-dimensional (3D) encoder–decoder based architecture that successfully incorporates FM-Pre-ResNet units and variational autoencoder (VAE). In an encoding stage, FM-Pre-ResNet units are used for learning a low-dimensional representation of the input. After that, the variational autoencoder (VAE) reconstructs the input image from the low-dimensional latent space to provide a strong regularization of all model weights, simultaneously preventing overfitting on the training data. Finally, the decoding stage creates the final whole heart segmentation. We evaluate our method on the 40 test subjects of the MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge. The average dice values of whole heart segmentation are 90.39% (CT images) and 89.50% (MRI images), which are both highly comparable to the state-of-the-art.
Analysis of VM migration scheduling as moving target defense against insider attacks

As cybersecurity threats evolve, cloud computing defenses must adapt to face new challenges. Unfortunately, due to resource sharing, cloud computing platforms open the door for insider attacks, which consist of malicious actions from cloud authorized users (e.g., clients of an Infrastructure-as-a-Service (IaaS) cloud) targeting the co-hosted users or the underlying provider environment. Virtual machine (VM) migration is a Moving Target Defense (MTD) technique to mitigate insider attacks effects, as it provides VMs positioning manageability. However, there is a clear demand for studies quantifying the security benefits of VM migration-based MTD considering different system architecture configurations. This paper tries to fill such a gap by presenting a Stochastic Reward Net model for the security evaluation of a VM migration-based MTD. The security metric of interest is the probability of attack success. We consider multiple architectures, ranging from one physical machine pool (without MTD) up to four physical machine pools. The evaluation also considers the unavailability due to VM migration. The key contributions are i) a set of results highlighting the probability of insider attacks success over time in different architectures and VM migration schedules, and ii) suggestions for selecting VMs as candidates for MTD deployment based on the tolerance levels of the attack success probability. The results are validated against simulation results to confirm the accuracy of the model.
Centerness-Aware Network for Temporal Action Proposal

Temporal action proposal generation aims at localizing the temporal segments containing human actions in a video. This work proposes a centerness-aware network (CAN), which is a novel one-stage approach intended to generate action proposals as keypoint triplets. A keypoint triplet contains two boundary points (starting and ending) and one center point. Specifically, we evaluate the probabilities of each temporal location in the video whether it is at the boundaries or the center region of ground truth action proposals. CAN optimizes the predicted boundary points interactively in a bidirectional adaptation form by exploiting the dependencies among them. Furthermore, to accurately locate the center points of action proposals with different time spans, temporal feature pyramids are utilized to incorporate multi-scale information explicitly. Using the generated three keypoints, CAN efficiently retrieves temporal proposals by grouping keypoints into triplets if they are geometrically aligned. Experiments show that CAN achieves the state-of-the-art performance on the public THUMOS-14 and ActivityNet-1.3 datasets. Moreover, further experiments demonstrate that by applying action classifiers on proposals generated by CAN, our method achieves the state-of-the-art performance in temporal action localization.
Incentivizing research &amp; innovation with agrobiodiversity conserved in situ: Possibilities and limitations of a blockchain-based solution

This article explores the use of blockchain for agrobiodiversity (B4A) with a specific focus on (i) providing an overview of the existing regulatory challenges that result in sub-optimal research and innovation with agrobiodiversity conserved in situ, (ii) investigating how a blockchain-based solution may help overcome these challenges, and (iii) illustrating how incentive mechanisms can help to overcome shortcomings in existing intellectual property regimes that prevent effective conservation, research and innovation (CRI). Our research identifies (i) lack of channels for transparent and equitable sharing of know-how and materials (ii) lack of incentives, (iii) lack of trust among stakeholders, and (iv) lack of traceability options as main hindering reasons for in situ CRI with agrobiodiversity. Further, we find that blockchain solutions may empower data providers, including small farmers, to collectively track, control and monetize the use of data and assets shared while minimizing fraudulent activities. Transaction costs may also be lowered by removing complex and expensive interaction processes. However, further research and development are necessary to design an ethical and sustainable blockchain-based solution to incentivize in situ conservation, research and innovation with agrobiodiversity. Some future directions of research are recommended.
A Preliminary Study of Knowledge Graphs and Their Construction

Knowledge graphs are developed to extend and organize the Web of data application. It helps to store the data in structured form and also present the entities and their relations explicitly. An entity is considered as a knowledge graph unit such as a person, movie, and city or else that need to describe. All entities of knowledge graphs are presented with their attributes like name, age, nationality, birthdates, etc., and further connected with other entities during the construction of knowledge graph. Knowledge graph construction approaches and techniques play an important role to develop the knowledge graphs. This paper initially presents the study of some existing knowledge graphs like YAGO, DBPedia, ConceptNet, FrameNet, etc., and also focused on knowledge graph construction approaches and techniques. There are 21 knowledge graphs are analysed and presented at one place with their applications.
Modelling and analysis of the influence of solar spectrum on the efficiency of photovoltaic modules

The article presents the influence of changes in the solar radiation spectrum distribution on the properties of various photovoltaic modules, with particular emphasis on the scattered component. We compared the relative efficiency of the photovoltaic modules based on various semiconductor absorbers during bright and sunny, and cloudy summer days. Additionally, we presented the impact of the module tilt angle on the magnitude of the surface incident scattered component and on the efficiency of the module. The solar spectra for various weather conditions were estimated using specialised computer programmes,such as SolarSpectrum or SMARTS2, and we present here the validation results for the former. The obtained results confirm the published experimental data and the conclusions drawn show the applicability of the tested modules with various absorbers for the specific photovoltaic systems. The theoretical analysis of the a-Si structures, both single-, as well as multi-junction shows better performance of these structures at low-irradiance conditions (in comparison with e.g., c-Si, CIS structures), during days characterised by a spectral distribution common for cloudy days with high air water vapour content. This type of device should show unaltered efficiency both at mid-day, as well as during the morning and late afternoon.
Extracting and Predicting Word-Level Style Variations for Speech Synthesis

This paper proposes a speech synthesis method based on unsupervisedly-learned fine-grained style representations, named word-level style variations (WSVs), in order to improve the naturalness of synthetic speech. The whole model contains a WSV extractor and a WSV predictor. The WSV extractor is jointly trained with a sequence-to-sequence (Seq2seq) synthesizer and learns a WSV vector from the mel-spectrogram of each prosodic word in the training set by extending the global style token (GST) framework. In contrast to GST weights which describe the global styles of utterances, WSVs operate at word-level and are expected to describe local style properties, such as stresses. Besides, Gumbel softmax is adopted and the extracted WSVs are close to one-hot vectors which facilitate the subsequent prediction task. The WSV predictor is a deterministic model which generates the sequence of WSV vectors from input text using an autoregressive LSTM network. In addition to phonetic information, e.g., phoneme sequences, Bidirectional Encoder Representation from Transformers (BERT) model is employed by the predictor to obtain the semantic descriptions of input text for better predicting the latent speech representation, i.e., WSVs. The WSV predictor is trained by considering both the accuracy of WSV prediction and the distortion of mel-spectrograms recovered from the predicted WSVs. Experimental results show that our proposed method can achieve better naturalness of synthetic speech than baseline Tacotron2, text-predicted global style token (TP-GST) and BERT-Tacotron2 models.
Remote Sensing Image Aircraft Target Detection Based on GIoU-YOLO v3

Based on the YOLO v3 target detection framework, this paper trains and learns the public remote sensing image aircraft target data, and optimizes the defects of the YOLO v3 loss function, and introduces the IoU (intersection ratio) between the ground-true box and the prediction box, experimental results show that the precision, recall ratio and F1 value of the YOLO v3 model for aircraft target detection in remote sensing images are 95.12%, 86.21% and 0.9045, respectively. Compared with the previous ones, the network precision, recall rate and F1 value of the optimized loss function have been improved by 12.57%, 5.11% and 0.0863 respectively.
DOF: A Demand-Oriented Framework for Image Denoising

Most existing image denoising methods focus on improving denoising quality. However, when applying denoising methods to practical tasks, in addition to the denoising quality, the number of parameters, and the computational complexity should be fully considered. In this article, we propose a demand-oriented framework (DOF) for image denoising, which can give preference to the number of parameters, the computational complexity, and the denoising quality or balance these three performance metrics. To perform the demand-oriented denoising, we first design a scale encoder to help the denoising model extract fewer but more representative features. Then, the split-flow module is introduced to fully exploit the input features by sharing the information of one network branch with other network branches. Finally, the scale decoder is utilized to reconstruct the final noise map without using any parameters. Through extensive experiments, we demonstrate that the proposed framework can be applied to several existing methods to help them achieve a more competitive denoising performance in terms of the number of parameters, and computational complexity.
Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.
ADS-B Based Air Traffic Management System Using Ethereum Blockchain Technology

This study represents a performance model of Automatic Dependent Surveillance-Broadcast (ADS-B) based ATM system using the security features of Ethereum blockchain technology. ADS-B based modernization of ATM is taking place in different parts of the world. ADS-B and other components of upcoming ATM exchange information through an unencrypted data link. More reliability of ATM on data link services caused fear of cyber threat to many aviation specialist and entities. To overcome the security-related concern of ADS-B based ATM, blockchain can be a promising technology. This paper systematically reviews the key concepts and process of Ethereum and Hyperledger blockchain technology and proposed a model using Ethereum blockchain in ATM comparing different key features of blockchain technology which ensures privacy, authenticity and confidentiality to overcome the cyber threat of ADS-B based ATM system. It is found that the proposed Ethereum based blockchain technique provides the opportunity of establishing secured ADS-B based ATM.
Testing the tests

Null Hypothesis Significance Testing (NHST) has been recurrently employed as the reference framework to assess the difference in performance between Information Retrieval (IR) systems. IR practitioners customarily apply significance tests, such as the t-test, the Wilcoxon Signed Rank test, the Permutation test, the Sign test or the Bootstrap test. However, the question of which of these tests is the most reliable in IR experimentation is still controversial. Different authors have tried to shed light on this issue, but their conclusions are not in agreement. In this paper, we present a new methodology for assessing the behavior of significance tests in typical ranking tasks. Our method creates models from the search systems and uses those models to simulate different inputs to the significance tests. With such an approach, we can control the experimental conditions and run experiments with full knowledge about the truth or falseness of the null hypothesis. Following our methodology, we computed a series of simulations that estimate the proportion of Type I and Type II errors made by different tests. Results conclusively suggest that the Wilcoxon test is the most reliable test and, thus, IR practitioners should adopt it as the reference tool to assess differences between IR systems.
Deep transformation models: Tackling complex regression problems with neural network based transformation models

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.