Reviewerly/exHarmony huggingface dataset:
An Ensemble and Iterative Recovery Strategy Based kGNN Method to Edit Data with Label Noise

Learning label noise is gaining increasing attention from a variety of disciplines, particularly in supervised machine learning for classification tasks. The k nearest neighbors (kNN) classifier is often used as a natural way to edit the training sets due to its sensitivity to label noise. However, the kNN-based editor may remove too many instances if not designed to take care of the label noise. In addition, the one-sided nearest neighbor (NN) rule is unconvincing, as it just considers the nearest neighbors from the perspective of the query sample. In this paper, we propose an ensemble and iterative recovery strategy-based kGNN method (EIRS-kGNN) to edit data with label noise. EIRS-kGNN first uses the general nearest neighbors (GNN) to expand the one-sided NN rule to a binary-sided NN rule, taking the neighborhood of the queried samples into account. Then, it ensembles the prediction results of a finite set of ks in the kGNN to prudently judge the noise levels for each sample. Finally, two loops, i.e., the inner loop and the outer loop, are leveraged to iteratively detect label noise. A frequency indicator is derived from the iterative processes to guide the mixture approaches, including relabeling and removing, to deal with the detected label noise. The goal of EIRS-kGNN is to recover the distribution of the data set as if it were not corrupted. Experimental results on both synthetic data sets and UCI benchmarks, including binary data sets and multi-class data sets, demonstrate the effectiveness of the proposed EIRS-kGNN method.
Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout <scp>XLM</scp> learning framework for software defect prediction

Summary Software testing is the process of improving software quality by classifying and removing defects in the software development. Previously, several methods were used for software defect prediction, but any one method did not provide sufficient accuracy. To overcome this issue, an improved may fly optimization with deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout extreme learning machine framework (SDP‐IMFOFS‐GDDDXLMC) is proposed in this article for software defect prediction. Here, an IMFO method is considered for feature selection techniques. In feature selection technique, features are selected, such as PC1, PC4, and MC1 for selecting the probable minimal attribute. Then, GDDDXLMC approach is used to classify buggy and clean software detection. The proposed approach is implemented in MATLAB platform. The performance metrics, such as accuracy, precision, F‐measure, sensitivity, specificity, Mathew correlation coefficient (MCC) is examined to examine the performance of the proposed method. The simulation performance of the proposed SDP‐IMFOFS‐GDDDXLMC method attains higher accuracy 99.75%, 97.85%, 95.13%, 14.89%, 16.34%, 17.89%, and 98.79, higher sensitivity 96.34%, 91.23%, 89.12%, 12.67%, 17.56%, 18.90%, and 87.25% higher specificity 14.89%, 16.89%, 20.67%, 93.67%, 92.37%, 98.47%, and 94.78% compared with the existing methods, like SDP‐MLP‐PSO, SDP‐BPNN‐RBFNN, SDP‐CNN‐RNN‐LSTM, SDP‐KBN‐LIME, SDP‐SLDeep‐LSTM, SDP‐K‐PCA‐ELM, and SDP‐CNN‐PHI forest, respectively.
LIS Professionals on Open Educational Resources in Modern Information Era

The developing capacities of the Internet, combined with OER, offer extraordinary open doors for essentially expanding access to quality educational resources for various divisions.Open educational resources (OER) have as of late emerged as a response to the requirement for open, reusable and educational material, unreservedly accessible on the web.Influencing educational resource 'to open' means it is accessible at no cost to others for adjustment and reuse in various settings.Libraries can add to the open educational center by making OERs themselves.There are numerous libraries and library consortium that have led the pack in delivering resources for all.With the changing time LIS professionals need to remain side by side and stays open to the changing patterns in educational world, and furthermore pick up involvement with changing technologies, turn into a basic accomplice in the cooperative educational endeavors of both educator and student.The main establishments have made their educational contents open to all with no cost.This paper delineates a review about the open educational resources and involvement, open door for library and LIS professionals.
Stochastic Embedding of Empirical Mode Decomposition with Application in Parkinson's Disease Speech Diagnostics

The Empirical Mode Decomposition (EMD) time series decomposition method is widely used within an array of different application areas in signal processing primarily due to its ability to adapt to non-stationary and non-linear time-frequency analysis settings more effectively than many other existing time series decomposition approaches. The EMD identifies a set of time domain basis functions, the Intrinsic Mode Functions (IMFs), which can also be transformed into a set of time varying frequency domain basis functions, the Instantaneous Frequencies (IFs). Like many time series decomposition methods, the EMD is considered an empirical path-wise decomposition approach applied to a time series realisation. However, to date, no statistical framework exists to encompass the study of this decomposition method from a stochastic representation or embedding perspective. This is essential to undertake statistical analysis tasks such as estimation and inference and to accurately incorporate statistical uncertainty quantification in out-of-sample predictions and forecasts (distinct from extrapolation). Hence, this work proposes a stochastic version of the EMD compatible with the path-wise features of the basis characteristics. The developed stochastic representations provide tractable statistical models, admitting flexible structures consistent with the characterising properties of the IMFs in their probabilistic representation when going from path-wise understanding to process embedding. In designing this novel stochastic embedding, we will focus on two examples of statistical EMD model constructions, which will be used to perform inference in detecting Parkinson's disease from speech signals. The proposed methods will be shown to outperform current state-of-the-art methods in Parkinson's disease based on speech analysis. This validates the exciting possibilities of developing a stochastic representation of the EMD decomposition methodology for time-frequency analysis.The Supplementary Appendix for this paper is given at http://ssrn.com/abstract=4173858.The repository for the code is available at https://github.com/mcampi111/EMD-Stochastic-Embedding-for-PD-Speech.
Dynamics of Crypto-Prices: Equilibrium Implications for Professional and Casual Miners

We design an equilibrium model for the mining market in the presence of professional and casual miners. We apply our setup to the Bitcoin and Ethereum market and find that the (recent) dynamics of crypto prices are well approximated by those of their mining technological enhancements. Technological enhancements have a negative impact on the activities of casual miners and result in professional miners' monopolies. Fixed mining costs paid by professional miners are inversely related to technological enhancements, decrease at an exponential rate (which is twice as big for Bitcoin), and are the smallest during periods of professional miners' monopolies.
Dual Attention Interactive Machine

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item via the user experience and item features, plays a critical role in online advertising and marking. Although CTR prediction is developing rapidly in capturing interactive information of samples or feature, there is not yet a CTR model to use both information. In this paper, we propose a novel Wide\&Deep-like model named Dual Attention Interactive Machine (DAIM) to capture both interactive information. We use an external-attention mechanism which captures interactive information for samples as "deep" part. As for "wide" part, we propose inner-attention from the point of view of sample, which regards the sample as a whole to learn feature interaction. Extensive experiments on two real-world datasets shows DAIM outperforms state-of-the-art models.
Development of Deep-Learning Natural-Language-Processing Models to Automatically Identify Cardiovascular Disease Hospitalisations of Diabetic Patients Using Routine Visits’ Free-Form Text

Writing notes is still the most widespread method to report clinical events, even within electronic health records (EHRs). Therefore, especially for chronic diseases such as diabetes, most of the information about the disease history and complications of a patient remains locked behind free-form text. This can complicate both the extraction of clinically meaningful patterns from the data, and the making of clinical decisions. Natural language processing (NLP) provides a solution to automatically transform free-form text into structured data that can be easily re-used for several purposes connected to research and clinical practice. In the present work, a typical EHR dataset of 16,876 diabetic patients, paired with a subset of a hospital discharge registry, was used to develop deep-learning based NLP models to automatically identify, within free-form text describing routine visits, the occurrence of hospitalisations related to cardiovascular disease (CVDs), an outcome of diabetes. Four possible time windows of increasing level of expected difficulty were considered: infinite (i.e., the hospitalisation occurred at any time before the visit), 24 months (i.e., the hospitalisation occurred in the 24 months before the visit’s date), 12 months, and 6 months. Two alternative settings were considered: by-visit (i.e., every visit in the dataset was considered independently of the others) and by-patient (i.e., visits were grouped according to the patient to whom they belonged). Model performance was evaluated by means of the area under the precision recall curve, as well as precision, recall, and F1-score after thresholding. Two thresholding schemes were compared: a single threshold approach to distinguish between positive and negative model predictions; and an innovative, more sophisticated, double-threshold approach to distinguish between positive, negative, and uncertain predictions, thus allowing for a variable amount of uncertainty (5% to 20%), to be resolved by human intervention. Results showed that the proposed NLP approach was successful for both the infinite and 24-month windows, while, as expected, performance deteriorated with shorter time windows. Possible clinical applications of tools based on the proposed NLP approach include the retrospective filling of medical records with respect to a patient’s CVD history for epidemiological and research purposes as well as for clinical decision making.
Dependency-Based Feature and Pairwise Classifier for Cross-Domain Aspect Term Extraction

Aspect extraction is one of the most critical tasks in aspect-based sentiment analysis (ABSA). Previous studies mostly employed dependency rules and/or utilized training data to learn extraction models. Despite its applicability to other domains, the dependency rule-based approach is rigid, while learning extraction models usually assumes that target and source domains have the same distribution, limiting its applicability to other domains. Our goal in this research is to develop an extraction strategy that is flexible such as in learning extraction models but also has wider applicability in other domains. Our extraction strategy is first generating all possible pairs of aspect-opinion candidates, and then selecting the best ones. Aspect-opinion pairs are represented by dependency features, and three new dependency features are introduced: relation probability, focus node, and sentence clause. Pairwise classifier is employed to learn and vote the better aspect-opinion candidates. The Pairwise classifier inputs are fed with comparison features, transformed using comparison operators from two aspect-opinion pairs to be compared. The aspect terms are extracted from the n-top voted aspect-opinion candidates. Experiments were carried out and tested on review sentences from 6 benchmark product domains with mid and high levels of inconsistency from the training data domains. The experimental results revealed that the proposed method improved the average F-measure by 23% - 58% in cross-domain settings while still competitive to state-of-the-art techniques in same-domain settings.
Time management. Challenges in the pandemic period

The COVID 19 Pandemic has changed life to everybody. In current social context, marked by regulations and restrictions, each person, daily, shapes his/her time-table and approaches “time” from a new perspective.The current paper tries to reveal the time management’ difficulties that teachers faced in on-line activities with pupils with deficiencies.The conclusions that we found, based on a survey on 28 teachers first didactic degree, give the experts in special education the opportunity to a better: understanding of time – as educational resource; planning the educational activities; center on the utility, for pupils with deficiencies, of the taught information.
Professional stress in special integrated education

The professional activity carried out by a school counselor teacher may seem like an easy job for some specialists, but for most of them, their job involves many difficulties. The professional stress of school counselors is experienced because their work requires face-to-face communication with people, activities that involve knowing, assessing and addressing various types of problems of all categories of students, who can be described as stable signs of maladaptation to school requirements, problems related to the control of their conduct.
The effects of the Covid-19 pandemic on special education

The Covid-19 pandemic has affected children's education in all respects. After the closure of schools and the transition to online education, social differences and shortcomings in the education system highlighted several problems, including lack of teaching materials adapted for students with special educational needs, lack of digital infrastructure, lack of support teachers, inability to adapt to online school for children diagnosed with ADHD, epilepsy, autism. However, teachers, support teachers, parents and students sought and found solutions to meet the challenges, managing to lessen the impact of these completely new and unexpected situations.
Association Rule Mining in Big Datasets Using Improved Cuckoo Search Algorithm

This work intends to discover diversified association rules efficiently using a cluster computing model. At first, the input data is pre-processed for data transformation. Then, the pre-processed data is given to k-means clustering to cluster the data. Since the initialization of the centroid is the key feature of k-means clustering, it is taken as the major challenge here. The randomly assigned centroid is optimally tuned by the new Fitness based Probability for Cuckoo Search (FP-CS) model. By exploiting adopted FP-CS, the best k-means centroid is determined. Thus, the optimal centroids are further processed for k-means clustering, and the optimal clustered data is attained. The clustered data is then given as input to the apriori algorithm, and rule mining data is attained in a proficient manner. Moreover, the adopted FP-CS model is evaluated with conservative methods, and the relevant outcomes are verified.
Geomorphic indices, machine learning and osl-palynology chronology to assess neotectonic deformation in the continental margin – Northeastern Brazil

The continental margin of Northeast Brazil (NE Brazil) is the most seismogenic of the passive margin of South America and has several evidence of neotectonics. However, the duration and magnitude of neotectonic events cannot be uniformly replicated for a scale of ∼3000 km of extension. Therefore, this study aimed to understand the tectonic dynamics, especially from Neogene to recent, for a region little studied in this scope. We conducted multiscale analysis: landforms, sub-basins, drainage systems, and sedimentary outcrops. Applying a multi-technical approach: geomorphic indices (Stream Length-Gradient-Sl, Channel Steepness ksn, Basin Asymmetry Factor); use of Machine Learning Algorithms (ML) for spatialization of Sl and ksn indices; and OSL-palynology chronology. The results indicate multiphase tectonic control occurring by reactivation of geological faults from the Cretaceous to the recent, affecting shear zones, Cretaceous Basins, and Miocene deposits (Barreiras Formation). In these areas, predictions by ML (Sl and ksn indices) showed high values associated with fault zones, which confine and direct drainage, and landforms conditioning ungraded river long-profiles and tilted sub-basins. Tectonic deformations also manifest at the outcrop scale, with strata showing deformations (folds, joints, and atypical dips). Furthermore, strata with marine palynomorphs (Miocene) and Pleistocene-Holocene sediments (Post-Barreiras) in an altitudinal position above ∼38 m above sea level suggest post-depositional movement. The multiscale and multi-technical approaches showed that tectonic activity acted in different periods. Notably, the spatial prediction of geomorphic indices with ML is potentially competent in regional analyzes of neotectonics.
A novel adaptive noise reduction method for field natural gas pipeline defect detection signals

To suppress noise interference and improve defect identification accuracy, we propose a transfer learning-based adaptive wavelet neural network (AWNN) method for pipeline defect detection signal denoising. Simulated noisy signals and field detection signals are used to determine the network's initial structure and fine-tune its parameters. The Meyer wavelet basis function is chosen as an excitation function based on the defect signal waveform. The pretrained structure is determined by a search algorithm. The network parameters are adaptively fine-tuned according to the sample entropy of the noise. The new genetic beetle antennae search strategy (GBA) optimization algorithm is proposed to improve the AWNN to avoid local optima. Compared with traditional noise reduction methods, the AWNN has excellent ability to recovery the pure denoised signal with a higher SNR and smaller RMSE. The AWNN can realize adaptive noise reduction of different field signals, providing reliable input for subsequent defect diagnosis.
Face recognition based banking system using machine learning

The main ideal of this design is to recognize human face and extracting corresponding bank details to bank account users. It helps people to know about their regular transactions and it saves more time in banks. Face is the pivotal part of the human body that uniquely identifies a person. Using the face characteristics as biometric, the face recognition system can be enforced. The most demanding task in all banks is facing unwanted crowds for simple conditions. In traditional banking system, the bank users are called out by the bank employee commemorative wise to know the details of bank users. By enter the name or account number of the user they come to know about the full details of corresponding user. It takes further time for both users and workers. This model integrates a camera that captures an input image, an algorithm for detecting face from an input image, encoding and identifying the face, delivering the banking details directly to the bank users.
The Basic Building Blocks of Quantum Computing

The tremendous potential of quantum computers lies hidden in their smallest components, the so-called quantum bits (qubits). Their quantum mechanical properties allow them to enter a common state and become entangled with each other. A quantum computer cleverly exploits these properties for calculations. This type of information processing is fundamentally different from that of today’s computers and reveals unprecedented possibilities. It could help solve unanswered research questions and pave the way for new technologies, such as quantum encryption.
Performance Analysis of AES on CPU-GPU Heterogeneous Systems

AES is one of the most widely used encryption algorithms. With the ever-increasing amount of sensitive data that need protection, it is natural to turn to parallel AES solutions that exploit emerging architectures and reduce encryption time. In this paper, we present a collaborative implementation of AES (PAES-CPU-MultiGPU) for CPU-GPU heterogeneous systems. PAES-CPU-MultiGPU takes advantage of the hardware support for AES provided by modern CPU cores and, at the same time, benefits from the GPUs available on these systems. We compare the performance of our proposal with that of two other solutions that use only the CPU cores (PAES-CPU) and only the GPUs of the system (PAES-MultiGPU). The results reveal that PAES-CPU-MultiGPU achieves an overall performance similar to that of PAES-CPU, but using fewer CPU cores, outperforming by far PAES-MultiGPU. Also, PAES-CPU-MultiGPU outperforms PAES-CPU when an amount of CPU cores similar to that of commodity multicore machines is used.
Multi-class E-mail Classification with a Semi-Supervised Approach Based on Automatic Feature Selection and Information Retrieval

Nowadays, millions of data are generated every day, and their use and interpretation have become fundamental in all fields. This is particularly true in the area of e-mails classification where, beyond its key role in organizing huge amounts of incoming information, it presents several challenging aspects to be solved. To the well-known problems presented by textual data (as its ambiguity) e-mails are usually characterized by their short lenght and informal language. These difficulties are increased when a relatively large number of highly imbalanced classes need to be considered and manual labeling is expensive and must be carried out by specialized personnel. Those are the main issues addressed in the present work, where Spanish-language e-mails sent by students of an Argentinian university needs to be categorized in 16 different classes. Our proposal to address this problem consists of a semi-supervised approach based on an automatic feature selection process complemented with an information retrieval strategy. From an initial data set of manually labeled e-mails, the main features are selected for each class, using three techniques: logistic regression, TF-IDF, and SS3. Then, the remaining (non labeled) instances are indexed with a general-purpose search engine (Elasticsearch) and documents of each class are retrieved based on the selected features identified by each technique. Our very simple approach shows that classifiers trained with labeled documents plus those retrieved in an automatic way obtain an improvement in performance (up to 6%) regarding classifiers trained only with manually labeled instances. Those improvements are observed in both, traditional learning algorithms like SVM, but also in more recent, state of the arts, transformer-based models (BERT).