Reviewerly/exHarmony huggingface dataset:
Facial expressions classification and false label reduction using LDA and threefold SVM

Representation and classification of multi-dimensional data are current key research areas. The representation of data in two classes is more feasible than multi-class representations because of the inherent quadratic complexity in existing techniques. Erroneous assignment of class labels affects separation boundary and training time complexity. In this paper, multi-dimensional data is handled using linear discriminant analysis (LDA) and threefold support vector machine (SVM) techniques to reduce the complexity and minimize false labeling. A facial expression application is proposed in which six natural expressions are used as multi-class data. Face image is divided into seven triangles on the basis of two focal points. A combined local and global feature descriptor is generated. Discrete Fourier transform is applied and processed with LDA to obtain discriminant features and accurately map an input feature space to an output space. To evaluate the system performance, Japanese Female Facial Expression, FER-2013 and Cohn–Kanade DFAT datasets are used. The obtained results show that multi-class data hyper plane using LDA and threefold SVM approach is effective and simple for quadratic data analysis
Classifying medical relations in clinical text via convolutional neural networks

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.
Toxic Code Snippets on Stack Overflow

Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow's CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects.
Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images

Classifying breast cancer histopathological images automatically is an important task in computer assisted pathology analysis. However, extracting informative and non-redundant features for histopathological image classification is challenging due to the appearance variability caused by the heterogeneity of the disease, the tissue preparation, and staining processes. In this paper, we propose a new feature extractor, called deep manifold preserving autoencoder, to learn discriminative features from unlabeled data. Then, we integrate the proposed feature extractorwith a softmax classifier to classify breast cancer histopathology images. Specifically, it learns hierarchal features from unlabeled image patches by minimizing the distance between its input and output, and simultaneously preserving the geometric structure of the whole input data set. After the unsupervised training, we connect the encoder layers of the trained deep manifold preserving autoencoder with a softmax classifier to construct a cascade model and fine-tune this deep neural network with labeled training data. The proposed method learns discriminative features by preserving the structure of the input datasets from the manifold learning view and minimizing reconstruction errorfrom the deep learning view from a large amount of unlabeled data. Extensive experiments on the public breast cancer dataset (BreaKHis) demonstrate the effectiveness of the proposed method.
Attention-Based Multiview Re-Observation Fusion Network for Skeletal Action Recognition

Action recognition is an important and popular area in computer vision. Because of the helpfulness of action recognition of the skeleton and the development of related pose estimation techniques, action recognition based on skeleton data has drawn considerable attention and has been widely studied in recent years. In this paper, we propose an attention-based multiview re-observation fusion model for skeletal action recognition. The proposed model focuses on the factor of observation view of actions, which greatly influences action recognition. The model utilizes action information from multiple observation views to improve the recognition performance. In this method, we re-observe input skeleton data from several possible viewpoints, process these augmented observation data with a long short-term memory (LSTM) network separately, and, finally, fuse the outputs to generate the final recognition result. In the multiview fusion process, an attention mechanism is applied to regulate the fusion operation according to the helpfulness for the recognition of all views. In this way, the model can fuse information from multiple viewpoints to recognize actions and can learn to evaluate observation views to improve fusion performance. We also propose a multilayer feature attention method to improve the performance of the LSTM in our model. We utilize an attention mechanism to enhance the feature expression by finding and focusing on informative feature dimensions according to contextual action information. Moreover, we propose stacking multiple layers of attention operation in a multilayer LSTM network to further improve network performance. The final model is integrated into an end-to-end trainable network. Experiments conducted on two popular datasets, NTU RGB+D and SBU Kinect interaction, show that our model achieves state-of-the-art performance.
Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection

Compared to conventional saliency detection by handcrafted features, deep convolutional neural networks (CNNs) recently have been successfully applied to saliency detection field with superior performance on locating salient objects. However, due to repeated sub-sampling operations inside CNNs such as pooling and convolution, many CNN-based saliency models fail to maintain fine-grained spatial details and boundary structures of objects. To remedy this issue, this paper proposes a novel end-to-end deep learning-based refinement model named Refinet, which is based on fully convolutional network augmented with segmentation hypotheses. Intermediate saliency maps that are edge-aware are computed from segmentation-based pooling and then feed to a two-tier fully convolutional network for effective fusion and refinement, leading to more precise object details and boundaries. In addition, the resolution of feature maps in the proposed Refinet is carefully designed to guarantee sufficient boundary clarity of the refined saliency output. Compared to widely employed dense conditional random field, Refinet is able to enhance coarse saliency maps generated by existing models with more accurate spatial details, and its effectiveness is demonstrated by experimental results on seven benchmark datasets.
Informational inefficiency of Bitcoin: A study based on high-frequency data

The study reexamines the issue of informational efficiency of Bitcoin using data at different frequencies (15, 30, 60 and 120 min and daily data). In particular, we test the martingale hypothesis in Bitcoin returns using different variance ratio tests. We also examine the evolution of informational efficiency of Bitcoin using non-overlapping and overlapping moving window analysis. The study provides evidence of the presence of informational inefficiency in the Bitcoin market at higher frequency levels. The daily Bitcoin returns which appear to be following a memory-less stochastic process are in fact otherwise when we move to the higher frequencies of Bitcoin prices.
Parked Vehicular Computing for Energy-Efficient Internet of Vehicles: A Contract Theoretic Approach

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by parked vehicular computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized rewards for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.
Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition

Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.
Influence of video content type on users’ virtual reality sickness perception and physiological response

Virtual Reality (VR) sickness (Cybersickness) is an affliction and a challenge, common to users of virtual environments. We therefore asked ourselves this research question: “Can video content type influence users’ VR sickness and physiological response?” We conducted a study with 26 participants, who watched two omnidirectional videos of different content types (neutral and action content) on five distinct video conditions: 2D TV screen, three generations of Oculus Rift VR HMDs (DK1, DK2 and CV1) and on the mobile Samsung GearVR HMD. The Simulator Sickness Questionnaire and the Subjective Units of Distress Scale in combination with the measurement of the physiological parameters (electrodermal activity and skin temperature, respiratory frequency and heart rate) were used to assess the VR sickness effects. The results show that video content type as well as users’ background preferences (preference to adrenaline sports) affected the users’ VR sickness perception. Considering various video conditions, significantly less VR sickness effects were reported with the TV condition than with any VR devices. The results of the subjective questionnaires were correlated with the objective physiological measurements, whereby skin conductance strongly correlated with the VR sickness effects. The effects were also more pronounced in cases of action video content type. Furthermore, we show there is a strong correlation when assessing the VR sickness effects using subjective questionnaire-based methods (the Simulator Sickness Questionnaire and the Subjective Units of Distress Scale) of various complexity, indicating the simple methods (only one question), can effectively be used as well.
Learning for Video Super-Resolution Through HR Optical Flow Estimation

Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. The generation of accurate correspondence plays a significant role in video SR. It is demonstrated by traditional video SR methods that simultaneous SR of both images and optical flows can provide accurate correspondences and better SR results. However, LR optical flows are used in existing deep learning based methods for correspondence generation. In this paper, we propose an end-to-end trainable video SR framework to super-resolve both images and optical flows. Specifically, we first propose an optical flow reconstruction network (OFRnet) to infer HR optical flows in a coarse-to-fine manner. Then, motion compensation is performed according to the HR optical flows. Finally, compensated LR inputs are fed to a super-resolution network (SRnet) to generate the SR results. Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance. Comparative results on the Vid4 and DAVIS-10 datasets show that our framework achieves the state-of-the-art performance.
An intelligent regressive ensemble approach for predicting resource usage in cloud computing

Cloud Computing has become prime infrastructure for scientists to deploy scientific applications as it offers parallel and distributed environment for large-scale computations. During deployment, the significant prediction of resource usage is essential to achieve optimal scheduling for scientific applications. The existing resource prediction models fall short in providing reasonable accuracy because of high variances of cloud metrics. Therefore, to handle the varying cloud resource demands, it is necessary to accurately predict the future resource requirements for automatically provisioning the resources. In this paper, an Intelligent Regressive Ensemble Approach for Prediction (REAP) has been proposed which integrates feature selection and resource usage prediction techniques to achieve high performance. The effectiveness of proposed approach is evaluated in a real cloud environment by conducting a series of experiments. The experimental results show that the proposed approach outperforms the existing models by significantly improving the accuracy rate and reducing the execution time. The results are further validated by comparing the existing Learning Automata (LA) based ensemble approach with the proposed approach on the basis of error rate.
Coin Hopping Attack in Blockchain-Based IoT

With dramatic developments of blockchain technology, a number of blockchain-based applications emerge rapidly, among which the incorporation of blockchain into Internet of Things is one of the most valued research direction. Such powerful incorporation is a double-sided sword, i.e., it can benefit both individuals and society but has the vulnerability to coin hopping attack that is a new type of pool mining attack and hard to happen in traditional blockchain networks. In this paper, we theoretically prove the feasibility of coin hopping attack, deeply analyze the conditions of attack implementation, and comprehensively investigate the impacts of coin hopping attack. Moreover, some defense strategies are addressed. To our best knowledge, this paper is the first work targeting coin hopping attack.
On the Origins and Variations of Blockchain Technologies

We explore the origins of blockchain technologies to better understand the enduring needs they address. We identify the five key elements of a blockchain, show the embodiments of these elements, and examine how these elements come together to yield important properties in selected systems. To facilitate comparing the many variations of blockchains, we also describe the four crucial roles of common blockchain participants. Our historical exploration highlights the 1979 work of David Chaum, whose vault system embodies many of the elements of blockchains.
Inverse kinematics of mobile manipulator using bidirectional particle swarm optimization by manipulator decoupling

This paper presents the solution to inverse kinematics of a mobile manipulator operating in both obstacle free environment and cluttered workspace. This work conceives an optimization problem using bidirectional particle swarm optimization method to obtain the inverse kinematics solution of the mobile manipulator. The bidirectional search algorithm helps to search for the solution faster than conventional unidirectional search. The novel manipulator decoupling technique proposed in this paper enables the system to carry out bidirectional search to find inverse kinematics. Collision detection and obstacle avoidance tasks are undertaken using the algorithm proposed in this work to avoid line type obstacles. A multi-objective optimization problem is formulated with minimum total joint movement as another objective function and collision avoidance as constraint. Numerical are experiments performed on a four degree of freedom manipulator mounted over a mobile base to illustrate the efficacy of the proposed method.
A fast multiple-image encryption algorithm based on hybrid chaotic systems for gray scale images

This paper proposes a fast and efficient multiple-image encryption based on chaotic system. The combination chaotic system is used to produce pseudorandom sequences to replace the subblocks of the plain images and then they are combined together. The subblocks of the combined image are replaced by applying the chaos sequences which are generated with combination chaotic system. Also, to achieve the encrypted image Arnold cat map and some operations including XOR and cyclic shift are used. Simulations and analysis outcomes are shown that the proposed algorithm can resist against the different attacks strongly and efficiently.
Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets

The major bottleneck in microarray gene expression analysis is the lack of techniques required to cope up with the uncertain gene functionality and inherent complex gene interactions. More often the issues of unstructured, unorganized, noisy and incomplete data is confronted with the advent of uncertain gene expression big datasets. Moreover, big data is naturally associated with the uncertainties which are originated from multiple sources. In this paper, such uncertainties in the gene expression dataset are modeled using interval type-2 fuzzy sets (IT2 FSs). For this, the spread of footprint of uncertainty (FOU), which accounts for the all the possible noises in the gene expression dataset, is modeled both symmetrically and asymmetrically. The medical science big dataset of microarray gene expression data of the cancer patients are considered for the experimentations. Empirically, the effects of uncertainty modeling using IT2 FSs on the big data clustering is analyzed and observed. Fuzzy clustering approach allow the genes to belong to multiple clusters and thus allow the genes participation in cellular process, subcell variations, and cellular metabolism. The effect of the induced uncertainty on the big data clustering has been studied using various cluster validity measures. The clustering results, obtained based on the IT2 fuzzy uncertainty modeling, are compared with same obtained with type-1 fuzzy sets based uncertainty modeling. It is demonstrated that our proposed IT2 FS based approach is more efficient in giving better clustering results for uncertain gene expression datasets and is scalable to the large gene expression datasets. Sensitivity analysis of the clustering results considering four different IT2 membership function shapes such as triangular, trapezoidal, semi-elliptic, and Gaussian are performed.
Sky image-based solar irradiance prediction methodologies using artificial neural networks

In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.