Reviewerly/exHarmony huggingface dataset:
Optimizing neural network based on cuckoo search and invasive weed optimization using extreme learning machine approach

Extreme Learning Machine (ELM) is widely known to train feed forward network with high speed and good generalization performance. The only problem associated with ELM is required higher number of hidden neurons due to random selection. In this paper we proposed a new model Cuckoo Search with Invasive weed optimization based Extreme Learning Machine (CSIWO-ELM) to optimize input weight and hidden neurons. This model provides the optimize input to the feedforward network to improve the ELM. The developed model is experimented on three medical datasets to see the data classification. Also, the developed model is compared with different optimize algorithm. The experimental result proves the excellent working of CSIWO-ELM model for classification problem.
Learning Cross-Lingual IR from an English Retriever

We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD).The teacher of DR.DECR relies on a highly effective but computationally expensive two-stage inference process consisting of query translation and monolingual IR, while the student, DR.DECR, executes a single CLIR step.We teach DR.DECR powerful multilingual representations as well as CLIR by optimizing two corresponding KD objectives.Learning useful representations of non-English text from an English-only retriever is accomplished through a cross-lingual token alignment algorithm that relies on the representation capabilities of the underlying multilingual encoders.In both in-domain and zero-shot out-of-domain evaluation, DR.DECR demonstrates far superior accuracy over direct fine-tuning with labeled CLIR data.It is also the best single-model retriever on the XOR-TyDi benchmark at the time of this writing.
Introduction to Interval Type-3 Fuzzy Systems

A review of the basic concepts and their use in the design of interval type-3 fuzzy systemsFuzzy systemType-3, which are relatively new models of uncertainty and imprecision, is considered in this book. The fundamental focus of the work is based on the basic reasons of the need for interval type-3 fuzzy systems for different areas of application.
Multi-Objective GAN-Based Adversarial Attack Technique for Modulation Classifiers

Deep learning is increasingly being used for many tasks in wireless communications, such as modulation classification. However, it has been shown to be vulnerable to adversarial attacks, which introduce specially crafted imperceptible perturbations, inducing models to make mistakes. This letter proposes an input-agnostic adversarial attack technique that is based on generative adversarial networks (GANs) and multi-task loss. Our results show that our technique reduces the accuracy of a modulation classifier more than a jamming attack and other adversarial attack techniques. Furthermore, it generates adversarial samples at least 335 times faster than the other techniques evaluated, which raises serious concerns about using deep learning-based modulation classifiers.
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization

Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks.However, large-scale parallel corpora are expensive and challenging to construct.In this work, we introduce a low-cost and effective strategy, ExtraPhrase, to augment training data for abstractive summarization tasks.Ex-traPhrase constructs pseudo training data in two steps: extractive summarization and paraphrasing.We extract major parts of an input text in the extractive summarization step and obtain its diverse expressions with the paraphrasing step.Through experiments, we show that ExtraPhrase improves the performance of abstractive summarization tasks by more than 0.50 points in ROUGE scores compared to the setting without data augmentation.Ex-traPhrase also outperforms existing methods such as back-translation and self-training.We also show that ExtraPhrase is significantly effective when the amount of genuine training data is remarkably small, i.e., a low-resource setting.Moreover, ExtraPhrase is more costefficient than the existing approaches 1 .
General Expansion-Shifting Model for Reversible Data Hiding: Theoretical Investigation and Practical Algorithm Design

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance.
RF Fingerprinting-Based IoT Node Authentication Using Mahalanobis Distance Correlation Theory

The wireless Internet of Things (IoT) node authentication approaches also used Radio Frequency (RF) fingerprinting or physical unclonable features (PUF) of IoT devices for node authentication. Machine learning based models play vital role in these approaches. In this letter, we introduce an effective and novel IoT node authentication approach using Mahalanobis Distance correlation and Chi-square distribution theories. Further, it has lower computational time to determine node authenticity. The comparative results of our proposal with three recent machine learning based approaches and PUF based approaches are promising which validates the effectiveness and the novelty of our proposal.
The Position of Artificial Intelligence in the Future of Education: An Overview

In the modern era, artificial intelligence applications have become one of the most essential and prominent aspirations of countries in their various organisations and sectors, especially the education sector, due to the ability of these techniques to help this sector to develop rapidly and increase productivity by imparting scientific material in a beautiful way to the learners. This article provides an overview of the significance of artificial intelligence applications and their role in learning, and how they can be employed in the future. All information in this scenario is collected from a set of studies published in the time of COVID-19 pandemic between 2019 and 2021. This scenario concluded that artificial intelligence is the future that is constantly growing and can be benefited from in the field of education and must be properly exploited to build a new world that depends heavily on digital societies.
Collaborative Robots

Collaborative robots have been redefining human-robot relationships across the manufacturing industry. Companies of all types and sizes have found strategic reasons to acquire or invest in collaborative robots. Therefore, as a central precondition, data concerning the underlying manual work processes, including information on automation requirements, needs to be collected within a formal and systematic way. There are many standardization bodies which deal with the safety in human-robot interactions and this article brings some of them.
Analysis of the influence of multimedia network hybrid teaching method on college students' learning ability in physical education

When using the existing methods to analyse college students' physical learning ability, there is no clear pertinence, students' interest is low, and physical learning ability cannot be effectively improved. A multimedia network hybrid teaching method based on multiple linear regression analysis is proposed. The factors that promote the hybrid teaching of multimedia network are analysed, and the multiple regression analysis model of mixed teaching method is obtained. The model is used to analyse the influence of multimedia network hybrid teaching method on college students' learning ability. The simulation results show that the satisfaction of the method is higher than 80%, which is much higher than the existing method; the student learning ability index is between 90 and 100, which is twice the existing method. It proves that the method of this paper has certain practicability and can effectively improve the physical learning ability of college students.
Blockchain Based Secure Solution for Cloud Storage: A Model for Synchronizing Industry 4.0 and IIoT

Industry 4.0 is one of the hot topic of today’s world where everything in the industry will be data driven and technological advancements will take place accordingly. In the fourth phase of industrial revolution, manuf... | Find, read and cite all the research you need on Tech Science Press
Organisational climate and team performance: the mediating role of psychological empowerment at Jordanian pharmaceutical companies

Nowadays, pharmaceutical companies are considered one of the main pillars of the economic development of any country. The number of pharmaceutical companies has increased rapidly in the last decade in Jordan and many have adopted new managerial practices to improve their efficiency by boosting employees' performance. Accordingly, this paper built a conceptual framework that links organisational climate to team performance through psychological empowerment. To do so, 466 questionnaires were distributed to employees and their managers at four pharmaceutical companies; 424 responses were received, from 364 employees and 60 managers. Structural equation modelling (SEM) was employed to test the hypothesis, determine the validity of the model and measure the structural evaluation. Furthermore, a Sobel test was conducted to assess the capacity of psychological empowerment to influence the relationship between organisational climate and team performance. The major findings are that: 1) organisational climate has a significant positive relationship with psychological empowerment; 2) psychological empowerment has a positive relationship with team performance; 3) the significant positive relationship between organisational climate and team performance is mediated through psychological empowerment.
Strengthened Conditionals

This article develops a general method to transfer completeness results from a basic conditional to a defined conditional. As an example, the method is implemented for the so-called neutral conditional.
Activation Functions

In order to learn a nonlinear target function, a neural network uses activation functions which are nonlinear. activation function The choice of each specific activation function defines different types of neural networks.
Breast Cancer Detection by Optimal Classification using GWO Algorithm

This paper intends to develop a novel breast cancer detection model for classifying the normal, benign or malignant patterns in a mammogram.The diagnosis process is done based on three stages such as pre-processing, feature extraction and classification.Initially, the Discrete Fourier Transform (DFT) is applied in the processing stage.Next, to pre-processing, the Gray Level Co-Occurrence Matrix (GLCM) features of the image are extracted.The GLCM-based features are then classified using Support Vector Machine (SVM) for classifying the mammogram.Further, the weights of the SVM are optimized using the Grey Wolf optimization (GWO) model for improving the classification accuracy.This classification mechanism is used to diagnose the benign and malignant patterns in a mammogram.Moreover, the proposed scheme is evaluated over traditional models such as GA, PSO and FF as well as the outcomes is verified.
Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA

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Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting

The gradually increased penetration of photovoltaic (PV) power into electric power systems brings an urgent requirement for accurate and stable PV power forecasting methods. The existing forecasting methods are built to explore the function between weather data and power generation, which ignore the uncertainty of historical PV power. To manage the uncertainty in the forecasting process, a novel ensemble method, named the evidential extreme learning machine (EELM) algorithm, for deterministic and probabilistic PV power forecasting based on the extreme learning machine (ELM) and evidential regression, is proposed in this paper. The proposed EELM algorithm builds ELM models for each neighbor in the k-nearest neighbors initially, and subsequently integrates multiple models through an evidential discounting and combination process. The results can be accessed through forecasting outcomes from corresponding models of nearest neighbors and the mass function determined by the distance between the predicted point and neighbors. The proposed EELM algorithm is verified with the real data series of a rooftop PV plant in Macau. The deterministic forecasting results demonstrate that the proposed EELM algorithm exhibits 15.45% lower nRMSE than ELM. In addition, the forecasting prediction intervals obtain better performance in PICP and CWC than normal distribution.
DANet: Image Deraining via Dynamic Association Learning

Rain streaks and background components in a rainy input are highly correlated, making the deraining task a composition of the rain streak removal and background restoration. However, the correlation of these two components is barely considered, leading to unsatisfied deraining results. To this end, we propose a dynamic associated network (DANet) to achieve the association learning between rain streak removal and background recovery. There are two key aspects to fulfill the association learning: 1) DANet unveils the latent association knowledge between rain streak prediction and background texture recovery, and leverages it as an extra prior via an associated learning module (ALM) to promote the texture recovery. 2) DANet introduces the parametric association constraint for enhancing the compatibility of deraining model with background reconstruction, enabling it to be automatically learned from the training data. Moreover, we observe that the sampled rainy image enjoys the similar distribution to the original one. We thus propose to learn the rain distribution at the sampling space, and exploit super-resolution to reconstruct high-frequency background details for computation and memory reduction. Our proposed DANet achieves the approximate deraining performance to the state-of-the-art MPRNet but only requires 52.6\% and 23\% inference time and computational cost, respectively.
An Improved Denoising Algorithm for Removing Noise in Color Images

Noise has a significant impact on image quality in a variety of applications, including machine vision and object recognition. Denoising is crucial for successful image processing since noisy pictures lead to erroneous findings and segmentation and enhancement mistakes. Existing methods were primarily developed for grayscale image denoising and are unable to detect all damaged pixels in an image effectively. This paper proposes a sequential ROAD-TGM-HT method to suppress impulsive noise in color image denoising. The noisy pixel location is detected using the consecutive method in the first step, and the distorted value of the damaged pixel is reconstructed in the second stage, followed by the Hough transform for the remaining undetected pixels. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were used to analyze the qualitative and quantitative performance. ROAD-TGM-HT excels on color images with noise levels ranging from 0.10 to 0.70, as per PSNR and SSIM qualitative data.
Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines

This paper presents a collaborative neurodynamic approach to Boolean matrix factorization. Based on a binary optimization formulation to minimize the Hamming distance between a given data matrix and its low-rank reconstruction, the proposed approach employs a population of Boltzmann machines operating concurrently for scatter search of factorization solutions. In addition, a particle swarm optimization rule is used to re-initialize the neuronal states of Boltzmann machines upon their local convergence to escape from local minima toward global solutions. Experimental results demonstrate the superior convergence and performance of the proposed approach against six baseline methods on ten benchmark datasets.
Blockchain-Based Collaborative Management of Job Shop Supply Chain

Blockchain supports free and reliable transactions of the production material supply chain (PMSC).However, few scholars have combined the blockchain with supply chain management.Based on blockchain, this paper explores the collaborative management of material supply chain for production and manufacturing job-shops (PMJs).Specifically, a collaborative management model was constructed, and the execution of the smart contract between PMJ and production material supplier (PMS) was explained.Then, the Stackelberg game between PMJ and PMS in the model was analysed, and the calculation method of the total benefit of PMS and PMJ was derived, under the information sharing mode of the blockchain-based PMSC model.Through trust evaluation, the dishonest companies were identified in the PMSC.To ensure the safe and reliable data sharing between the companies in our model, the probability for the dishonest companies to participate in smart contract was reduced, making it less likely for false shared data to appear in the chain.The effectiveness of the proposed blockchain-based model for the collaborative management of PMSC was proved through simulations.
Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/m2/d, and 0.01 kWh/m2/d. The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/m2/d, and 0.01 kWh/m2/d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/m2/d, and 0.13 kWh/m2/d, while the reduction for yearly was 91.6%, 0.11 kWh/m2/d, 0.2 kWh/m2/d. The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/m2/d, and 0.16 kWh/m2/d, while the yearly values were 0.47%, 0.18 kWh/m2/d, and 0.004 kWh/m2/d.
Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.
Deformable Graph Convolutional Networks

Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GNNs have two limitations. Since the graph convolution is performed in a small local neighborhood on the input graph, it is inherently incapable to capture long-range dependencies between distance nodes. In addition, when a node has neighbors that belong to different classes, i.e., heterophily, the aggregated messages from them often negatively affect representation learning. To address the two common problems of graph convolution, in this paper, we propose Deformable Graph Convolutional Networks (Deformable GCNs) that adaptively perform convolution in multiple latent spaces and capture short/long-range dependencies between nodes. Separated from node representations (features), our framework simultaneously learns the node positional embeddings (coordinates) to determine the relations between nodes in an end-to-end fashion. Depending on node position, the convolution kernels are deformed by deformation vectors and apply different transformations to its neighbor nodes. Our extensive experiments demonstrate that Deformable GCNs flexibly handles the heterophily and achieve the best performance in node classification tasks on six heterophilic graph datasets. Our code is publicly available at https://github.com/mlvlab/DeformableGCN.