Reviewerly/exHarmony huggingface dataset:
Inferring geological structural features from geophysical and geological mapping data using machine learning algorithms

Abstract We present an automated approach for inferring surface geological structures from geophysical survey data. Our method employs machine learning, using mapped geological structures as labels and filtered geophysical surveys as reference maps. We compared the performance of the eight main machine learning algorithms and their 32 branches. Applied to the Geological Survey of Victoria's database for the Bendigo Zone, following an appropriate choice of geological features, the 3‐class classification model using subspace K ‐nearest neighbour methods achieves a stable and validated 92% accuracy in around 1 min. The fault‐only classification model achieves a stable and validated 97% accuracy in around 6 min. This shows that geological structural features on the surface may be inferred from between one and three of the following geophysical data types: gravity, airborne total magnetic intensity and first vertical derivative of total magnetic intensity. It shows the prospect of machine learning in geological research and suggests that geophysical data combined with machine learning may be useful and efficient in determining the existence of geological structural features.
Information technology, business sustainability and female economic participation in sub-Saharan Africa

This study assesses, from the perspective of financial stability, how business/financial sustainability, moderates the influence of information technology on female economic participation in 49 countries in Sub-Saharan Africa for the period 2008-2018. The empirical evidence is based on Tobit regression, which accounted for the censored nature of the outcome variables. The following important findings were obtained: First, ICT (information and communications technology) dynamics (mobile phone penetration, internet penetration, and fixed broadband subscriptions) are consistently moderated by business sustainability to positively affect female employment. Second, business sustainability scores must exceed certain thresholds before moderating fixed broadband subscriptions to induce favorable overall effects on female employment, female labor force participation, and female unemployment rates. The positive effects on female employment and labor force participation is ascertained by the thresholds of 18.742 and 19.505 Z-scores, respectively and a Z-score of 17.300 for the negative impact on female unemployment.The thresholds that should be exceeded are within policy reach, make economic sense, and are policy-relevant. This study contributes to the extant literature by providing actionable thresholds for business sustainability that can be employed by policy makers such that information technology positively influences female economic inclusion in Sub-Saharan Africa.
A novel secure aware privacy preserved data sharing framework in an E-government system based on a consortium blockchain

AbstractElectronic government (E-government) utilises information and communication techniques for delivering services to individuals. It is termed a complicated model that requires to be distributed, privacy-preserved and secured. The failure of these can lead to economic and social loss. The blockchain method facilitates the execution of a secured and privacy-preserved model wherein transactions are done securely. This paper presents a privacy-protected model for sharing of data in an e-governance system considering consortium blockchain. The proposed privacy-protected model involves eight different phases, namely initialisation, new node creation, delegates and witnesses voting, user registration, authentication, data protection, validation and data sharing. This method helps to facilitate the security of information and privacy and concurrently increases trust in the public sector. The proposed privacy-protected model provided enhanced performance with a small computational time of 40.422s and memory usage of 38.800MB.KEYWORDS: Privacy preservationdata sharingE-government systemconsortium blockchainauthentication Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsS. SuganthiS. Suganthi is currently a Ph.D. Research Scholar in the Department of Computer Science at Vels University and Advanced Studies (Vistas), Chennai, India. She received her MPhil degree in Computer Science from Vinayaka Mission University, Salem, India. She received her M.Sc. degree in Information Technology from the University of Madras, Chennai, India and received her B.Sc in Mathematics from Bharathy Womens’ College, Chennai, India. She has been qualified for SET (State Level Eligibility Test) for Assistant Professor conducted by UGC. Her research interests include cryptography and information security to apply deep theoretical work in the mathematics of cryptography to real-world problems.T. Sree KalaT. Sree Kala is currently working as Associate Professor in the Department of Computer Science at Vels Institute of Science and Technology (VISTAS). She has received her M.Sc Degree in Computer Science from St.Joseph’s College, Trichy, her M.Phil Degree from MK University, Madurai and her Ph.D from Bharathiar University, Coimbatore. She has been qualified for the National Eligibility Test for Assistant Professor conducted by UGC. She has 14 years of experience in teaching at UG and PG levels. She has 3 years of experience in Testing in the Software Industry. She has published more than 25 papers in various International and Scopus-indexed journals. Her research interest includes Intrusion Detection Systems, Network Security, Data mining and Big Data.
Cybersecurity Risks Unique to Open Source and What Communities Are Doing to Reduce Them

This article delineates four categories of risk unique to open source projects and communities. We also discuss the communities actively seeking to mitigate these risk areas and suggest areas to elevate cybersecurity risk in open source.
Algebraic Reduction of Hidden Markov Models

The problem of reducing a Hidden Markov Model (HMM) to one of smaller dimension that exactly reproduces the same marginals is tackled by using a system-theoretic approach. Realization theory tools are extended to HMMs by leveraging suitable algebraic representations of probability spaces. We propose two algorithms that return coarse-grained equivalent HMMs obtained by stochastic projection operators: the first returns models that exactly reproduce the single-time distribution of a given output process, while in the second the full (multi-time) distribution is preserved. The reduction method exploits not only the structure of the observed output, but also its initial condition, whenever the latter is known or belongs to a given subclass. Optimal algorithms are derived for a class of HMM, namely observable ones.
Generative Adversarial Network-Based Signal Inpainting for Automatic Modulation Classification

Automatic modulation classification (AMC) aims to automatically identify the modulation type of a detected signal in an intelligent wireless receiver, such as software-defined radio (SDR).Recently, deep learning-based methods such as convolutional neural networks have been applied to AMC, showing high-accuracy performance.However, the earlier studies do not consider various signal degradations that can possibly occur during the transmission and reception of wireless signals.Particularly, the signal reception can be often unstable, and the signal can be partially received due to the dynamic spectrum sensing or signal sensing in the intelligent wireless systems.The corrupted signal with missing samples considerably degrades the accuracy of modulation classification of the deep learning-based models, because it is very different from the training datasets.To address this issue, the preprocessing process of restoring the corrupted signal, called signal inpainting, is essential.Although it is significant for the modulation classification, no studies have been performed to investigate the effect of signal inpainting on AMC.To that end, this study proposes a generative adversarial network(GAN)-based signal inpainting method that fills in the missing samples in a wireless signal.The proposed inpainting method can restore the time-domain signal with up to 50% missing samples while maintaining the global structure of each modulation type.The correct recovery of the global structure enables the extraction of distinctive features that play a key role in the modulation classification.To investigate this effect of signal inpainting on AMC, we perform intensive experiments on the RadioML dataset that has been widely used in the AMC studies.We compare the accuracy performance of the two state-of-the-art AMC models with and without the proposed signal inpainting, respectively.Through the analysis of the results, we show that the proposed GAN-based inpainting method significantly improves the accuracy of AMC.
Preventing Over-Enhancement Using Modified ICSO Algorithm

This paper proposes an Image Contrast Enhancement (ICE) method based on using an Improved Chicken Swarm Optimization (ICSO) algorithm to enhance images while at the same time preventing over-enhancement. In the optimization process, a new practical objective function is employed to reach three main goals, preserving the main details, generating an image with a uniform histogram, and reducing the spikes in the modified histogram. In the proposed approach, the RGB color channels are optimized individually. The performance of the proposed method is suitable for enhancing the contrast of low- and high-contrast images. A subjective experiment is designed to visually evaluate and compare the results with other ICE methods. The simulation results on the CSIQ, TID2013, and SEID datasets show that the proposed method outperforms numerous traditional and state-of-the-art ICE techniques both subjectively and objectively. The most important advantage of the newly proposed technique is that there is an agreement among observers on when over-enhancement occurs regardless of whether the Initial processed image was of low or high contrast.
An active foveated gaze prediction algorithm based on a Bayesian ideal observer

Predicting human eye movements is a crucial task for understanding human behavior and has numerous applications in machine vision. Most current models for predicting eye movements are data-driven and require large datasets of recorded eye movements, which can be expensive and time-consuming to collect. In this paper, we present a novel theory-based model for predicting eye movements in a foveated visual system that maximizes information gain at each fixation. Our model uses a region-proposal network and eccentricity-based max pooling to account for the loss of detail in peripheral vision. We apply our model to predict human fixations in a visual search task for objects in real-world scenes. Unlike data-driven models, our model does not require training on large eye movement datasets and can generalize to any set of natural images and targets. We evaluate the generalization capability of our model by demonstrating its results on two publicly available visual search datasets, Ehinger and COCO-search18, without any further training on those datasets. Our model outperforms or performs comparably to data-driven models that are directly trained on human eye movement datasets.
Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches

The accuracy of the solar forecasting model depends upon various factors such as attributes of the data, time horizon, time interval, length of the data, etc. This means attributes of data selection are an important feature of any solar forecasting model. A lot of research has been done on solar forecasting using deep learning and machine learning approaches, providing better accuracy for a particular type of model and data. However, how to accurately apply the multi-model techniques in the data-driven approaches of the solar radiation prediction model is still a challenging issue due to the complexity of the temporal resolution, length, and attribute selection of the data. This paper proposes a multi-model technique using data-driven approaches to get an improvised solar forecasting model with respect to the attributes of data. In this research, a multi-model method based on data-driven approaches is developed for short-term solar radiation forecasting. Three years of weather data with different temporal resolutions taken from reliable online sources were used for training and testing in this work. The weather data considered various input parameters such as temperature, humidity, pressure, wind speed, and wind direction; solar radiation data is considered the model's target variable. This work is a two-stage process. In the first stage, multi-model methods are analyzed based on solar forecasts at different temporal resolutions, and best-performing models are selected. The optimization technique is applied to the best model in the second stage to obtain an improvised solar forecasting model. It is observed that the multi-model forecasts provide better accuracy in terms of R2, nRMSE, and MAE than the conventional forecasting model. In this work, the performance parameters of nRMSE = 0.34, MAE = 4.71, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.25, MAE = 1.39 and R2 = 0.96 are obtained after optimization in the testing stage for an hourly dataset. For daily datasets, the performance parameters nRMSE = 0.24, MAE = 3.19, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.16, MAE = 2.61, and R2 = 0.95 are obtained after optimization in the testing stage. As a result, the proposed algorithm focused on an efficient model to predict solar radiation. Accurate solar radiation prediction models are highly in demand in various smart grid applications. Therefore, the proposed work may help in suggesting a suitable model for solar energy forecasting.
CDText: Scene text detector based on context-aware deformable transformer

Scene text detection task aims to precisely locate text regions in natural scenes. However, the existing methods still face challenges in detecting arbitrary-shaped text, due to their limited feature representation capability. To alleviate this problem, we propose a scene text detector, i.e., CDText, based on structure of context-aware deformable transformer. Specifically, CDText firstly adopts different convolution kernel designs for feature extraction, which designs receptive fields with different size for multi-scale feature perception and fusion. Meanwhile, multi-head self-attention mechanism is used to strengthen the reasoning ability of CDText in a global sense, thus enhancing feature maps with abundant context information by extracting implicit relationship between multi-scale text features. Moreover, CDText designs a segmentation head to segment text instances of arbitrary shapes from rectangular detection boxes. Experiments show that CDText is superior to comparative methods in detection accuracy, achieving F-scores of 92.7, 81.9, and 82.9 on ICDAR2013, Total Text, and CTW-1500 datasets, respectively.
An empirical evaluation of scrum training’s suitability for the model-driven development of knowledge-intensive software systems

A Product Configuration System (PCS) is a software system that facilitates the sales and production processes of defined customizable products. PCS are specific software developments in the sense that they are knowledge-intensive so that they require models to formalize the complex knowledge inherent to product configurations also leading to dependencies between software functionalities. Scrum is a widely used agile method, but its training has been the subject of little research. Model-driven development implicitly impacts the way development is conducted especially when adopting an agile method as Scrum. This paper, as exploratory research, evaluates Scrum training for PCS projects through a qualitative case study. The goal is to identify the elements that should be focused on within Scrum background training. This research first studies and assesses the training experiences at the case company. Then, it reports on respondents' opinions about the strengths and challenges of applying Scrum in the mentioned context. The latter is based on multiple data sources: documentation, interviews, participant observations, and workshops. Issues in applicability lead to enhanced training support for learning how to (i) combine Scrum with the model-driven approach inherently required within PCS development, (ii) manage time and effort estimation on the basis of accurate artifacts and (iii) access key employees possessing domain or specific technical knowledge indispensable for pursuing the development.
Multiscale features integration based multiple-in-single-out network for object detection

The single-level feature map-based object detection has been a challenging task due to the feature scale limitation. Therefore, enriching multiscale information of single-level features is considered a promising approach to deal with this challenge. Although most existing methods have attempted to augment the feature scale of single-level features, the detection performance is still unsatisfactory because these methods mine multiscale features only based on a one-level feature map. To address this problem, we propose a multiple-in-single-out network (MiSoNet) to integrate multiscale information from multilevel feature maps into a single-level feature map. To achieve this, MiSoNet’s key component is equipped with two cascaded modules: a multilevel feature integration module (MFIM) and a depthwise convolutional residual encoder (DWEncoder). Specifically, MFIM adaptively fuses features of inconsistent semantics and scales from multilevel feature maps. DWEncoder stacks several residual blocks with depthwise convolutions to extract multiscale contexts in the single feature map, which can further extend the scale range of the receptive fields. Extensive experiments are conducted on the Common Objects in Context (COCO) dataset, where the MiSoNet achieves a 41.0AP, which surpasses the YOLOF by 1.4AP with negligible computational overhead. Moreover, the MiSoNet, with fewer parameters and FLOPs, outperforms some advanced detectors based on the feature pyramid network.
Bio-inspired Multiobjective Optimization Approach for Total Harmonic Distortion Reduction in a DC-AC Power Converter

The incorporation of power electronics devices into electrical networks can result in significant total harmonic distortion. This occurs in DC power converters that convert to AC due to the imperfect reconstruction of an alternating sine wave signal, resulting in the presence of harmonics in the generated AC signal. A signal with high harmonic content can negatively impact the quality of energy and damage devices connected to the electrical network. One method to reduce harmonic content in an AC signal is through the addition of filters to the power converter. However, designing these filters can be a complex task, particularly when seeking an optimal design beyond traditional techniques. This paper presents a method for the optimal design of filters in a DC-AC power converter using multi-objective optimization. The performance of the filters is evaluated by analyzing the total harmonic content of the converter's output signal, using permissible values from Mexico's network code for energy quality as a reference. The results demonstrate that the use of meta-heuristic techniques for the design of power electronics devices is a viable alternative in electronic design.
Autograding of Programming Skills

To evaluate a learner’s knowledge of programming language skills, assessments are given. Grading of these is usually done manually which not only is tedious but prone to error due to repetition and fatigue. In this work, we employ pre-trained language models to perform automated grading of "C" programming language. Embeddings from different transformers on pre-assessed codes are used as feature vectors to train a wide range of regressors for the scoring task. Root-mean-square error (RMSE) is the metric utilized to compare the scores of these regressors. It’s observed that embeddings from T5-model with CatBoost regressor gives the least error around 15%.
Implementation of stochastic computing in activation functions using stochastic arithmetic components

A new computing method using stochastic-based numbers is gaining importance as an approximate computing method to save area, energy, and computation time based on the accuracy required. This works uses stochastic computing, which is suitable for enhancing the efficiency of neural network. Herein we focus on developing activation functions that are essential parameters in the design of neural networks. The activation function in stochastic computing is typically a threshold function that maps the input bits to a binary output based on a probability distribution. This paper presents the development of modified activation functions tanh and COS using SC-based arithmetic components. Two different types of stochastic number generators (SNGs) have been used. Error analysis has been done based on the computation using two SNGs. Also, accuracy measurement is performed using error analysis for these complex functions mentioned above.
Implementing Federated Learning based on RainForest Model

Federated Learning (FL) is a new concept in machine learning that trains predictive models across multiple nodes and machines holding local training data without sharing that data with other nodes. It’s also known as collaborative learning. In FL, instead of sending the training data, only parameter values are uploaded to the master node or server. On the contrary, Rainforest is a concept for dealing with big data using a decision tree (DT) classifier. DT is one of the popular machine learning algorithms, which is a top-down recursive divide and conquer method. In this paper, we utilized the concept of federated learning by applying the Rainforest algorithm, where datasets are divided into several subsets of data using the clustering technique, from which scalable decision trees are constructed. From each subset of data, we have an AVC (attribute-value, class-label) table, which is sent to the central master node or server to create the full decision tree by using matrix addition. To train the model, we used ten different datasets and evaluated the proposed model. The experimental analysis shows very good accuracy and precision in overall performance.
Smart Contract Vulnerability Detection Techniques for Hyperledger Fabric

Hyperledger Fabric is becoming the most popular blockchain framework used by enterprises and medium-sized companies. Blockchain technology is used for many different domains like finance, voting service, land record etc. Blockchain innovation is a self-executing program called Smart Contract. Hyperledger fabric uses programming languages like Node.js, Java, and GO to implement smart contracts also called chaincode. Smart contracts are programmed to moderate critical transactions between individuals or organizations because terms and conditions embedded in the contract will be executed when certain conditions are met. So smart contract needs to be analyzed before deployment to ensure zero vulnerabilities and flaws on the blockchain application, their execution is critical against attacks which aim at stealing assets. Hence security auditing is an essential part of smart contract development. This paper proposes a static code analysis tool for smart contracts named as UBF-ChaincodeScan which helps to identify security risks in the smart contract code written in Node.js and GO language.