Reviewerly/exHarmony huggingface dataset:
An efficient binary social spider algorithm for feature selection problem

The social spider algorithm (SSA) is a heuristic algorithm created on spider behaviors to solve continuous problems. In this paper, firstly a binary version of the social spider algorithm called binary social spider algorithm (BinSSA) is proposed. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is the transfer function. The transfer function is responsible for mapping continuous search space to binary search space. In this study, eight of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. BinSSA is obtained from SSA, by transforming constant search space to binary search space with eight different transfer functions (S-Shapes and V-Shaped). Thus, eight different variations of BinSSA are formed as BinSSA1, BinSSA2, BinSSA3, BinSSA4, BinSSA5, BinSSA6, BinSSA7, and BinSSA8. For increasing, exploration and exploitation capacity of BinSSA, a crossover operator is added as BinSSA-CR. In secondly, the performances of BinSSA variations are tested on feature selection task. The optimal subset of features is a challenging problem in the process of feature selection. In this paper, according to different comparison criteria (mean of fitness values, the standard deviation of fitness values, the best of fitness values, the worst of fitness values, accuracy values, the mean number of the selected features, CPU time), the best BinSSA variation is detected. In the feature selection problem, the K-nearest neighbor (K-NN) and support vector machines (SVM) are used as classifiers. A detailed study is performed for the fixed parameter values used in the fitness function. BinSSA is evaluated on low-scaled, middle-scaled and large-scaled twenty-one well-known UCI datasets and obtained results are compared with state-of-art algorithms in the literature. Obtained results have shown that BinSSA and BinSSA-CR show superior performance and offer quality and stable solutions.
A deep residual compensation extreme learning machine and applications

Abstract The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC‐ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self‐noise prediction. We used the DRC‐ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC‐ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA‐RELM and OS‐ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back‐propagation neural network (BPNN).
Learning From High-Dimensional Biomedical Datasets: The Issue of Class Imbalance

As witnessed by a vast corpus of literature, dimensionality reduction is a fundamental step for biomedical data analysis.Indeed, in this domain, there is often the need for coping with a huge number of data attributes (or features).By removing irrelevant or redundant attributes, feature selection techniques can significantly reduce the complexity of the original problem, with important benefits in terms of domain understanding and knowledge discovery.When learning from biomedical data, however, the dimensionality issue is often addressed without a joint consideration of other critical aspects that may compromise the performance of the induced models.The adverse implications of an imbalanced class distribution, for example, are often neglected in this domain.The aim of this work is to investigate the effectiveness of hybrid learning strategies that incorporate both methods for dimensionality reduction as well as methods for alleviating the issue of class imbalance.Specifically, we combine different feature selection techniques, both univariate and multivariate, with sampling-based class balancing methods and cost-sensitive classification.The performance of the resulting learning schemes is experimentally evaluated on six highdimensional genomic benchmarks, using different classification algorithms, with interesting insight about the best strategies to use based on the characteristics of the data at hand.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification

The graph-based semisupervised label propagation (LP) algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, the available methods typically calculate the weights and estimate the labels in the original input space, which typically contains noise and corruption. Thus, the encoded similarities and manifold smoothness may be inaccurate for label estimation. In this article, we present effective schemes for resolving these issues and propose a novel and robust semisupervised classification algorithm, namely the triple matrix recovery-based robust auto-weighted label propagation framework (ALP-TMR). Our ALP-TMR introduces a TMR mechanism to remove noise or mixed signs from the estimated soft labels and improve the robustness to noise and outliers in the steps of assigning weights and predicting the labels simultaneously. Our method can jointly recover the underlying clean data, clean labels, and clean weighting spaces by decomposing the original data, predicted soft labels, or weights into a clean part plus an error part by fitting noise. In addition, ALP-TMR integrates the auto-weighting process by minimizing the reconstruction errors over the recovered clean data and clean soft labels, which can encode the weights more accurately to improve both data representation and classification. By classifying samples in the recovered clean label and weight spaces, one can potentially improve the label prediction results. Extensive simulations verified the effectiveness of our ALP-TMR.
Virtual Reality Analgesia With Interactive Eye Tracking During Brief Thermal Pain Stimuli: A Randomized Controlled Trial (Crossover Design)

In light of growing concerns about opioid analgesics, developing new non-pharmacologic pain control techniques has become a high priority. Adjunctive virtual reality can help reduce acute pain during painful medical procedures. However, for some especially painful medical procedures such as burn wound cleaning, clinical researchers recommend that more distracting versions of virtual reality are needed, to further amplify the potency of virtual reality analgesia. The current study with healthy volunteers explores for the first time whether interacting with virtual objects in Virtual Reality (VR) via "hands free" eye-tracking technology integrated into the VR helmet makes VR more effective/powerful than non-interactive/passive VR (no eye-tracking) for reducing pain during brief thermal pain stimuli.Forty eight healthy volunteers participated in the main study. Using a within-subject design, each participant received one brief thermal pain stimulus during interactive eye tracked virtual reality, and each participant received another thermal pain stimulus during non-interactive VR (treatment order randomized). After each pain stimulus, participants provided subjective 0-10 ratings of cognitive, sensory and affective components of pain, and rated the amount of fun they had during the pain stimulus.As predicted, interactive eye tracking increased the analgesic effectiveness of immersive virtual reality. Compared to the passive non-interactive VR condition, during the interactive eye tracked VR condition, participants reported significant reductions in worst pain (p < 0.001) and pain unpleasantness (p < 0.001). Participants reported a significantly stronger illusion of presence (p < 0.001), and significantly more fun in VR (p < 0.001) during the interactive condition compared to during passive VR. In summary, as predicted by our primary hypothesis, in the current laboratory acute pain analog study with healthy volunteers, increasing the immersiveness of the VR system via interactive eye tracking significantly increased how effectively VR reduced worst pain during a brief thermal pain stimulus. Although attention was not directly measured, the pattern of pain ratings, presence ratings, and fun ratings are consistent with an attentional mechanism for how VR reduces pain. Whether the current results generalize to clinical patient populations is another important topic for future research. Additional research and development is recommended.
Effects of a multi-level concept mapping-based question-posing approach on students’ ubiquitous learning performance and perceptions

The literature indicates that the use of mobile devices, wireless communication and sensing techniques can enhance students' learning in authentic environments and improve their learning effectiveness. Good learning strategy guides are essential for successful inquiry learning, and the question-posing strategy has been acknowledged as a type of high-level cognitive strategy that plays an important role in facilitating students' knowledge integration in the inquiry learning process. However, question posing seems challenging for many students and may lead to heavy cognitive load when appropriate scaffolding is not available. To solve this problem, the present research imported a concept mapping knowledge representation model as an auxiliary strategy for question-posing activities based on the research finding that visual knowledge representations are conducive to high-order thinking. It also developed a ubiquitous learning system which integrated multilevel concept mapping and question-posing strategy guidance to investigate the application effects of this learning model. A quasi-experimental design was applied, and an experiment was conducted to measure 5th graders’ learning of a plant unit of the natural science curriculum. Students in the experimental group learned with the proposed ubiquitous learning system based on the multilevel concept mapping and question-posing approach, while those in the control group learned with the ubiquitous learning system based on the question-posing approach. The results showed that the students in the experimental group significantly outperformed those in the control group from the perspectives of learning achievement and question-posing performance, but no significance was found in their cognitive load, meta-cognitive awareness, and science learning strategies.
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents

Jalilifard, Amir CaridVinicius Fernandes Mansano, Alex Fernandes Cristo, Rogers S. Da Fonseca, Felipe Penhorate CarvalhoKeyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification. In this paper we propose STF-IDF, a novel semantic method based on TF-IDF, for scoring word importance of informal documents in a corpus. A set of nearly four million documents from health-care social media was collected and was trained in order to draw semantic model and to find the word embeddings. Then, the features of semantic space were utilized to rearrange the original TF-IDF scores through an iterative solution so as to improve the moderate performance of this algorithm on informal texts. After testing the proposed method with 160 randomly chosen documents, our method managed to decrease the TF-IDF mean error rate by a factor of 50% and reaching the mean error of 13.7%, as opposed to 27.2% of the original TF-IDF.
Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources&#x2019; demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.
Brain MRI Super-Resolution Using 3D Dilated Convolutional Encoder–Decoder Network

The spatial resolution of magnetic resonance images (MRI) is limited by the hardware capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep convolutional neural networks (CNN) have achieved impressive success in MRI super-resolution (SR) reconstruction. Increasing network depth or width can enlarge the receptive field to improve SR accuracy, however, it is impractical for MRI reconstruction in clinical applications because of high computational loads. To address this issue, we propose a novel dilated convolutional encoder-decoder (DCED) network to improve the resolution of MRI. We exploit three-dimensional (3D) dilated convolutions as encoders to extract high-frequency features. The dilated encoders capture wider contextual information by exponentially enlarging the receptive field, without introducing additional parameters or layers. Then we decode the features using deconvolution operations to alleviate gridding artifacts and restore fine details. To improve information flow, the encoders and decoders are aggregated into symmetrically connected blocks. The output of each block is passed to the final convolution layer, which facilitates to extract hierarchical features. In addition, we also exploit a geometric self-ensemble 3D wavelet fusion method to improve the potential performance of MRI SR. Experimental results on four public available brain datasets show that our proposed method outperforms NLM (non-local means), LRTV (low-rank and total variation) and current CNN-based SR methods, which demonstrates that our method achieves a new state-of-the-art performance in MRI SR task.
Patch-Level Augmentation for Object Detection in Aerial Images

Object detection in specific views (e.g., top view, road view, and aerial view) suffers from a lack of dataset, which causes class imbalance and difficulties of covering hard examples. In order to handle these issues, we propose a hard chip mining method that makes the ratio of each class balanced and generates hard examples that are efficient for model training. First, we generate multi-scale chips to train object detector. Next, we extract object patches from the dataset to construct an object pool; then those patches are used to augment the dataset. By this augmentation, we can overcome the class imbalance problem. After that, we perform inference with the trained detector on augmented images, then generate hard chips from misclassified regions. Finally, we train the final detector by both normal and hard chips. The proposed method achieves superior results on VisDrone dataset both qualitatively and quantitatively. Also, our model is ranked 3rd in VisDrone-DET2019 challenge (http://aiskyeye.com/).
Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing

Visual Servoing (VS), where images taken from a camera typically attached to the robot end-effector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with sub-millimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm/5 degrees if applied once, or of several cm/tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling low-tolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGA-connector insertion task without any force sensing mechanism.
StartNet: Online Detection of Action Start in Untrimmed Videos

We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos. Previous methods aim to localize action starts by learning feature representations that can directly separate the start point from its preceding background. It is challenging due to the subtle appearance difference near the action starts and the lack of training data. Instead, StartNet decomposes ODAS into two stages: action classification (using ClsNet) and start point localization (using LocNet). ClsNet focuses on per-frame labeling and predicts action score distributions online. Based on the predicted action scores of the past and current frames, LocNet conducts class-agnostic start detection by optimizing long-term localization rewards using policy gradient methods. The proposed framework is validated on two large-scale datasets, THUMOS'14 and ActivityNet. The experimental results show that StartNet significantly outperforms the state-of-the-art by 15%-30% p-mAP under the offset tolerance of 1-10 seconds on THUMOS'14, and achieves comparable performance on ActivityNet with 10 times smaller time offset.
Comparative Evaluation of Credit Card Fraud Detection Using Machine Learning Techniques

Credit card fraud is a serious and growing problem with the increase in e-commerce and online transactions in this modern era. With this identity theft and loss of money, such mischievous practices can affect millions of people around the world. Criminal activity is a rising threat to the financial sector with-reaching implications. Information extraction seemed to have assumed a basic job in recognition of online payment fraud, fraud detection efficiency in credit card purchases is significantly affected by the data set measuring strategy, the choice of variable and the detection techniques used. This publication inspects execution of, Support Vector Machine, Naive Bayes, Logistic Regression and K-Nearest Neighbor on exceptionally distorted data on credit card fraud. The execution of these techniques is assessed dependent on accuracy, sensitivity, precision, specificity. The outcomes show an ideal accuracy for logistic regression, Naive Bayes, k-nearest neighbor and Support vector machine classifiers are 99.07%, 95.98%, 96.91%, and 97.53% respectively. The relative outcomes demonstrate that logistic regression performs superior to other algorithms.
Shearlet Transform-Based Light Field Compression Under Low Bitrates

Light field (LF) acquisition devices capture spatial and angular information of a scene. In contrast with traditional cameras, the additional angular information enables novel postprocessing applications, such as 3D scene reconstruction, the ability to refocus at different depth planes, and synthetic aperture. In this paper, we present a novel compression scheme for LF data captured using multiple traditional cameras. The input LF views were divided into two groups: key views and decimated views. The key views were compressed using the multi-view extension of high-efficiency video coding (MV-HEVC) scheme, and decimated views were predicted using the shearlet-transform-based prediction (STBP) scheme. Additionally, the residual information of predicted views was also encoded and sent along with the coded stream of key views. The proposed scheme was evaluated over a benchmark multi-camera based LF datasets, demonstrating that incorporating the residual information into the compression scheme increased the overall peak signal to noise ratio (PSNR) by 2 dB. The proposed compression scheme performed significantly better at low bit rates compared to anchor schemes, which have a better level of compression efficiency in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts, specifically at low bit rates, favors the proposed compression scheme over anchor schemes.
The Impact of Corporate Social Responsibility Interventions on Female Education Development in the Rural Niger Delta Region of Nigeria

The objective of this investigation was to assess the impact of multinational oil companies’ (MOCs) corporate social responsibility (CSR) interventions in female education programmes in the Niger Delta region of Nigeria. A total of 800 rural women were sampled across the region. The results from the logit model showed that rural women depended on CSR interventions of MOCs to address some of the logistical and cultural challenges associated with women’s access to post-secondary education in local communities. However, despite the significant success in supporting education initiatives generally, none of the scholarships target females specifically, and compared to men, the low level of human capital in rural women has persisted. This implies that, if CSR interventions are not tailored to enhance gender diversity and promote economic opportunities for women alongside education, they may perpetuate the obstruction of women’s participation in economic, political and social development. By extension, this could delay the reduction of poverty and attainment of the Sustainable Development Goals (SDGs) in the Niger Delta region.