Reviewerly/exHarmony huggingface dataset:
ELECTROFACIES DEFINITION AND ZONATION OF THE LOWER CRETACEOUS BARRA VELHA FORMATION CARBONATE RESERVOIR IN THE PRE‐SALT SEQUENCE OF THE SANTOS BASIN, SE BRAZIL

Lower Cretaceous carbonates in the pre‐salt succession in the Santos Basin, eastern Brazil, are highly heterogeneous in terms of their reservoir characteristics as a result of depositional and diagenetic factors. Electrofacies have widely been used for reservoir zonation and, when allied with computer‐based methods such as neural networks, may help with the study of such complex reservoir rocks and with the identification of high‐quality reservoir zones. In this work, an unsupervised artificial neural network known as a self‐organizing map (SOM) was used to carry out a zonation of the pre‐salt carbonates in the Aptian Barra Velha Formation, the main reservoir unit in the Santos Basin. Available data included gramma‐ray, neutron porosity, resistivity deep, sonic, density, photoelectric factor, total porosity and effective porosity profiles from 21 wells together with mineralogical models. Core descriptions and thin section images were used as additional data for the lithological characterization of the electrofacies and consequently for reservoir zonation. A total of four electrofacies were defined from the SOM application, and five reservoir zones were identified. The characterization of the reservoir zones also considered the structural locations of the wells based on the relative depth to top‐ Barra Velha Formation; well locations were classified as structurally high, intermediate or low. Based on the reservoir zone characteristics, the results could be correlated with zonations in previous studies. A general tendency was noted for there to be an increase of finer‐grained sediments in the formation in wells located in structural lows; packstone and mudstone facies were prevalent in these wells and were in general characterized as poor‐quality reservoir rocks. By contrast, the shrubstones and grainstones which were more frequent in structurally high wells comprised higher quality reservoir rocks. The basal reservoir zone showed wide lithological variation compared to the overlying reservoir zones. Grainstone‐dominated facies were identified in the middle of the formation, and the uppermost reservoir zones were characterized by an upward increase in shrubstones and reworked grainstones which in general pointed to better quality reservoirs.
Automatic analysis of caregiver input and child production

Abstract The present study explores the applicability of Natural Language Processing (NLP) techniques to investigate child corpora in Korean. We employ caregiver input and child production data in the CHILDES database, currently the largest and open-access Korean child corpus data, and apply NLP techniques to the data in two ways: automatic Part-of-Speech tagging by adapting a machine learning algorithm, and (semi-)automatic extraction of constructional patterns expressing a transitive event (active transitive and suffixal passive). As the first empirical report on NLP-assisted analysis of Korean child corpora, this study is expected to reveal its advantages and drawbacks, thereby opening the window to furthering corpus-mediated research on child language development in Korean. Implications of this study’s findings will also contribute to research practice regarding developmental studies on Korean through child corpora, ensuring the reproducibility of procedures and results, which is often lacking in previous corpus-based research on child language development in Korean.
Blockchain for Internet of Things (IoT): Research Issues, Challenges, and Future Directions

The Internet of Things (IoT) is continuing to expand its growth and has become a principal device when it comes to making things smart, like smart homes, smart cities, and smart devices. It is reachable to a level that can connect billions of devices at a time, permitting the growth of information sharing needs that makes daily life better. Traditional electronics become clever and independent as a result of seeing things on the Internet. Whereas this era known as blockchain appears a first-rate deliver of presenting safety to this new age of technological programs, the speedy evolvement of smart devices demands trusted security for the maintenance of the IoT containing application’s knowledge safety, security, and authentication. This research inspects the IoT protection answers by using integrating IoT programs with blockchain. The evaluation consists of the blockchain basics, kinds, and layout for the Internet of Things. It will also look at the hurdles and problems of IoT in blockchain adoption, particularly in terms of security, privacy, throughput, block size, multichain management, and social control. Finally, we look at the long-term implications of blockchain technology for IoT. According to the literature, the marriage of blockchain and IoT delivers critical security and privacy solutions in its principally based workouts.
Tools adapted to Ethical Analysis of Data Bias

Data Bias, a bias embedded in data during collection, storing, and use, and in the apps used by a human, is an emerging issue of data privacy exemplified by Artificial Intelligence bias (AI bias). This issue is becoming gradually an added vulnerability to data ethics, an added threat to data security, and an added burden to data protection. It has an effect to induce a reduction in data protection expenditure, and is crucial to the success of any creative endeavours in the data-driven technology-intensive era, including Engineering, exemplified by AI bias, and AI bias is bias created when biased data creeps in during design, development, and training of AI algorithms. AI indeed culminates in a phenomenon in which the populace jumps, yet a sober minority steers away from because of the pervasive cyber-threats that AI bias raises. At issue is not data bias per se, nor the multi-dimensional issues induced by human bias, which are usually complex and slippery, but a need for a method to enable a holistic view covering the technical, financial, legal, social, ethical, and ecological aspects of a given problem, action, policy, or decision. Recommendable is a method composed of the Ethical Matrix Algorithm and Hexa-dimension Metric Algorithm (Lee, forthcoming) based respectively on the Ethical Matrix and Hexa-dimension Metric (Lee, 2021).
Remote sensing image semantic segmentation network based on ENet

The current image semantic segmentation methods cannot meet the requirements of high precision and high speed for remote sensing image analysis. The ENet network model builds a semantic segmentation network, which has the characteristics of few network parameters and fast operation speed. The attention mechanism module is integrated with the ENet network model, which can deeply mine image features in remote sensing datasets and ensure the accuracy of semantic segmentation. The author combines the ENet network with the attention mechanism to construct a new semantic segmentation network model. The model first constructed a remote sensing image semantic segmentation network model based on the ENet network, and simplified the model to further improve the speed of image segmentation and recognition. Then, the attention mechanism module is fused with the ENet network model, which can conduct deep and orderly mining of the image features of the remote sensing image data set. It can meet the accuracy requirements of remote sensing image semantic analysis. Simulations are performed based on three general datasets, and the experimental results show high accuracy and high speed.
Comparative analysis of reactive and imperative approach in Java web application development

The subject of this study was to compare web applications created using the imperative and reactive approaches in Java. For this purpose, two applications with the same functionalities were developed using both approaches. The study looked at the performance, stability and time-consumption of implementation of each application. Based on the obtained results, it was found that the reactive application processes queries faster, uses less CPU, and is more stable in the case of handling many simultaneous requests, where the processing time is greater than 10 seconds. No significant differences were observed in using the computer's RAM by the applications. In addition, the study showed that reactive application takes more time to create.
ArbGaze: Gaze Estimation from Arbitrary-Sized Low-Resolution Images

The goal of gaze estimation is to estimate a gaze vector from an image containing a face or eye(s). Most existing studies use pre-defined fixed-resolution images to estimate the gaze vector. However, images captured from in-the-wild environments may have various resolutions, and variation in resolution can degrade gaze estimation performance. To address this problem, a gaze estimation method from arbitrary-sized low-resolution images is proposed. The basic idea of the proposed method is to combine knowledge distillation and feature adaptation. Knowledge distillation helps the gaze estimator for arbitrary-sized images generate a feature map similar to that from a high-resolution image. Feature adaptation makes creating a feature map adaptive to various resolutions of an input image possible by using a low-resolution image and its scale information together. It is shown that combining these two ideas improves gaze estimation performance substantially in the ablation study. It is also demonstrated that the proposed method can be generalized to other popularly used gaze estimation models through experiments using various backbones.
Machine-Learning-Based Suitability Prediction for Mobile Applications for Kids

Digital media has a massive presence in the modern world, and it significantly impacts kids’ intellectual, cognitive, ethical, and social development. It is nearly impossible to isolate kids from digital media. Therefore, adult content on mobile applications should be avoided by children. Although mobile operating systems provide parental controls, handling such rules is impossible for illiterate people. Consequently, kids may download and use adults’ mobile applications. Mobile applications for adults often publish age group information to distinguish user segments that can be used to automate the downloading process. Sustainable Development Goal (SDG) #4 emphasizes inclusivity and equitability in terms of quality of education and the facilitation of conditions for the promotion of lifelong learning for everyone. The current study can be counted as being in line with SDG#4, as it proposes a machine-learning-based approach to the prediction of the suitability of mobile applications for kids. The approach first leverages natural language processing (NLP) techniques to preprocess user reviews of mobile applications. Second, it performs feature engineering based on the given bag of words (BOW), e.g., abusive words, and constructs a feature vector for each mobile app. Finally, it trains and tests a machine learning algorithm on the given feature vectors. To evaluate the proposed approach, we leverage the 10-fold cross-validation technique. The results of the 10-fold cross-validation indicate that the proposed solution is significant. The average results of the exploited metrics (precision, recall, and F1-score) are 92.76%, 99.33%, and 95.93%, respectively.
A Systematic Literature Review of the Emergence of Blockchain Technology in of Human Resources Management

In the age of the fourth industrial revolution (or the industry 4.0), scientific progress is continually being made in different areas such as artificial intelligence and the blockchain. Among these areas, blockchain is emergingEmerging as an open distributed ledger that records transactions between two parties efficiently and permanently. Everything of value can be programmed to be recorded on the distributed ledger, and trust is created through a consensus of all authorized parties agreeing to any changes or additions to the blockchain to establish validityValidity. In this new age, there is an emergingEmerging trend of using blockchain to validate transactions or information with accuracy. To understand the relationship between blockchain and human resource management, this paper conducted a systematic literature review of blockchain and human resources management using keywords “blockchain”, “human resource management”, and “recruitment.” The trend analyses were performed using HarzingHarzing’s Publish and Perish, Mendeley, and VOSviewer. Articles were extracted from Web-of-Science, Google Scholar, and SpringerLink to further investigate the integration of blockchain into human resource management and recruiting processes. A co-citationCo-citations analysis was conducted through MaxQDAMaxqda to determine the most important articles in the literature, and it was discovered that there is an emergenceEmergence of blockchain-based recruitment in the human resource management field.
The Krypteia ensemble: Designing classifier ensembles using an ancient Spartan military tradition

In this work we propose a new algorithm to train and optimize an ensemble of classifiers. We call this algorithm the Krypteia ensemble, based on an ancient Spartan tradition designed to convert their most promising individuals into future leaders of their society. We show how to adapt this ancient custom to optimize classifiers by generating different variations of the same task, each one offering different hardships according to distinct stochastic variables. This is thus applied to induce diversity in the set of individual weak learners. Then, we use a set of agents designed to select those subjects who excel in their assignments, and whose interaction minimizes excessive redundancies in the resulting population. We also study how different Krypteia ensembles can be stacked together, so that more complex classifiers can be built using the same procedure. Besides, we consider a wide range of different aggregation functions in the decision making phase to find the optimal performance for the different Krypteia ensemble variations tested. Finally, we study how different Krypteia ensembles perform for a wide range of classification datasets and we compare them with other state-of-the-art design techniques of classifier ensembles, obtaining favourable results to our proposal.
Fine-grained neural architecture search for image super-resolution

Designing efficient deep neural networks has achieved great interest in image super-resolution (SR). However, exploring diverse network structures is computationally expensive. More importantly, each layer in a network has a distinct role that leads to the design of a specialized structure. In this work, we present a novel neural architecture search (NAS) algorithm that efficiently explores layer-wise structures. Specifically, we construct a supernet allowing flexibility in choosing the number of channels and per-channel activation functions according to the role of each layer. The search process runs efficiently via channel pruning since gradient descent jointly optimizes the Mult-Adds and the accuracy of the searched models. We facilitate estimating the model Mult-Adds in a differentiable manner using relaxations in the backward pass. The searched model, named FGNAS, outperforms the state-of-the-art NAS-based SR methods by a large margin. • A new search space that allows for channel-level architectural changes. • A new algorithm that automatically finds multiple heterogeneous activation functions. • The state-of-the-art lightweight and efficient networks for image super-resolution. • Efficiency improvement of the ClassSR framework using multiple searched networks.
Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression

Semi-supervised learning (SSL) has drawn much attention since it can alleviate the predicament in which only limited labels can be accessed, with the help of numerous unlabeled data. Many deep neural networks (NNs) based SSL methods have been proposed recently, which mostly focus on image classification tasks, while fewer efforts have been taken to tabular data related regression scenarios. In this paper, to handle the semi-supervised regression problem for tabular data with NNs based methods, we empirically observe the demerits of previous methods and present a novel framework, to explore the combination of self teaching and mutual teaching. For one thing, self teaching is employed to each base learner by using consistency regularization so that the model is driven to be more stable and also robust to local perturbations. For another, the knowledge of other base learners is extracted and filtered to perform mutual teaching to avoid the confirmation bias problem and boost the training of each base learner. Here, we employ an uncertainty-based strategy for the filtering of knowledge in mutual teaching. Extensive experiments are performed on multiple real-world datasets to demonstrate the effectiveness of previous methods and the proposed framework, and further analyses are conducted to understand the influence of each factor in the proposed framework.
The novel characteristics for training Ridge Polynomial neural network based on Lagrange multiplier

In this paper, the Lagrange multiplier is presented to train the Ridge Polynomial neural network, for improving the convergence rate and avoiding the ill-conditioned problem (penalty factor must go to infinity). In the process of training this network, with this method, the constrained problem is successfully transformed into an unconstrained problem. Based on the two assumptions, we propose the necessity theorem, the linear convergence theorem and the stability theorem for the property of weights iteration. Finally, the experimental results demonstrate that the proposed theorems are valid, and the effectiveness of convergence speed is confirmed.
Development and Performance Evaluation of a Blockchain Associated E-Voting System in IoT-Enabled Smart-Cities

An atmosphere is referred by the smart city, in which every available asset and current improvements are conveyed in an orderly and shrewd manner. Insightful detectors (Internet-of-Things (IoT) components) functioning in tandem along 5G innovation have been continually proving to be more inevitable and successful in achieving clients' desires. In the various IoT application conditions, e-voting a ballot has been the key use to IoT, which propels this to the advanced level to the development of smart city innovations. During generalized usages, each of the components have been estimated as pleasant as well as trustworthy. Anyhow, during the practice, uninvited guests can disrupt gadgets in order to operate perniciously to the point of corruption of the organization's administrations. As a result, the protection and security flaws in e-casting a ballot framework, in particular, create a huge concern where gate crashers might use numerous hacks to manipulate the surveys. As a result, the prospective test in distinguishing appropriate IoT components by spiteful with recording the certain parameters over a friendly streamlining agent to establish a real correspondence atmosphere. Furthermore, to prevent future changes to information collected by intelligence gadgets, a BC has been assisted, where squares about entire genuine IoT components have been documented. This paper describes a safe as well as generalized method of e-voting, a ballot using BC IoT, having a goal of identifying also the solution of different risks posed with a gate crasher at different stages. Furthermore, in accepting the scheduled instrument, this has been tested counter to several safety boundaries like information adjustment, DoS as well as DDoS attacks, along with the validation time.
A Object Detection Algorithm Incorporates Lightweight Network and Dual Attention Mechanism

To solve the problems of the existing object detection algorithms, such as insufficient feature extraction ability and slow detection speed, we propose a object detection algorithm which incorporates lightweight network and dual attention mechanism. Firstly, the YOLOv3 object detection network was used as the basic detection network and EfficientNet was used to replace backbone network to reduce the number of parameters and computation. Secondly, the CBAM attention mechanism was added to improve the sensitivity of the model to object features, suppress interference information and improve the object recognition accuracy. Finally, the Soft-NMS is introduced to replace the traditional NMS, which not only improves the accuracy of localization but also reduces the missing rate of the model. Experimental results show that compared with the original model, the weight file of the object detection model is reduced by 74.22%, the detection speed is improved by 16.93%, and the mAP on the object data set reaches 93.27%, which is improved by 2.85% compared with the original YOLOv3 algorithm. The detection ability of the algorithm in this paper is significantly improved, which well promotes the requirements of real-time detection of complex scenes on mobile terminals.
Text Classification in Python

Text classification is an ML technique that assigns a set of predefined tags to open text, and text classifiers have been used to structure, organize, and categorize any sort of text data ranging from documents, files, medical studies, to the overall web. This chapter will provide you with the text classification techniques in python. In addition, it focuses on training a supervised learning model to do text classification with a real-time case study.
Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm

Data clustering is a data mining task, and it means finding clusters from among data whose labels are not predetermined. It is a popular analytics tool for statistical data in various domains. The k-means algorithm is a basic algorithm for data clustering, which has initial problems such as dependence on the cluster centers' initial value, sensitivity to outliers, and non-guaranteed optimal solutions to unbalanced cluster formation. This book chapter uses Farmland Fertility Algorithm (FFA) for the data clustering algorithm. Ten standard data sets used to evaluate the effectiveness of FFA are compared Harmony Search (HS), Monarch Butterfly Optimization (MBO), Artificial Bee Colony (ABC), Symbiotic Organism Search (SOS), Differential Evolution (DE), and Crow Search Algorithm (CSA) in terms of statistical criteria such as analysis of variance (ANOVA) and the convergence rate. Experimental results demonstrate that FFA has better performance than other optimization algorithms and is more stable than these algorithms.
The Processing goes far beyond "the app" – Privacy issues of decentralized Digital Contact Tracing using the example of the German Corona-Warn-App

Since SARS-CoV-2 started spreading in Europe in early 2020, there has been a strong call for technical solutions to combat or contain the pandemic, with contact tracing apps at the heart of the debates. The EU&#x2019;s General Data Protection Regulation (GDPR) requires controllers to carry out a data protection impact assessment (DPIA) where their data processing is likely to result in a high risk to the rights and freedoms (Art. 35 GDPR). A DPIA is a structured risk analysis that identifies and evaluates possible consequences of data processing relevant to fundamental rights in advance and describes the measures envisaged to address these risks or expresses the inability to do so.Based on the Standard Data Protection Model (SDM), we present the results of a scientific and methodologically clear DPIA. It shows that even a decentralized architecture involves numerous serious weaknesses and risks, including larger ones still left unaddressed in current implementations. It also found that none of the proposed designs operates on anonymous data or ensures proper anonymisation. It also showed that informed consent would not be a legitimate legal ground for the processing. For all points where data subjects&#x2019; rights are still not sufficiently safeguarded, we briefly outline solutions.