Reviewerly/exHarmony huggingface dataset:
A Micro-Extended Belief Rule-Based System for Big Data Multiclass Classification Problems

Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.
Nonlinear filtering in unknown measurement noise and target tracking system by variational Bayesian inference

This paper considers a class of nonlinear filtering algorithms based on variational Bayesian (VB) theory to settle the unknown measurement noise problem in target tracking system. When the unknown measurement noise is conditionally independent of states, based on the variational idea, estimate of probability density function of state is converted into approximation two probability density functions for both unknown noise and nonlinear states. Then, an iterative algorithm is established to jointly estimate the state and the unknown measurement noise using variational Bayesian inference. Thus, the unknown measurement noise could be estimated as hidden state. The convergence result of the proposed nonlinear probability density function approximation algorithm is also given. The simulation results of typical examples show that the proposed VB based methods have superior performance to these classic algorithms in target tracking problems.
ContourGAN: Image contour detection with generative adversarial network

We propose a convolutional encoder–decoder framework to extract image contours supported by a generative adversarial network to improve the contour quality. Traditional image-to-image models only consider the loss between prediction and ground truth, neglecting the similarity between the data distribution of the outcomes and ground truth. Based on this observation, the proposed generative adversarial network aims to increase the detection accuracy. The resulting method contains two models, namely an encoder–decoder model, whose weights are updated using binary cross-entropy loss with fine-tuning from the VGG16 pre-trained model, and a discriminator network that employs ground truths and predicted contours as input for discrimination. We evaluate the method on the Berkeley Segmentation DataSetand Benchmarks 500 and NYUD dataset, achieving state-of-the-art performance with an ODS F-measure of 0.810 and 0.715.
Understanding the Role of Interactive Machine Learning in Movement Interaction Design

Interaction based on human movement has the potential to become an important new paradigm of human-computer interaction. However, high quality, mainstream movement interaction requires effective tools and techniques to support designers. A promising approach to movement interaction design is Interactive Machine Learning, in which designing is done by physically performing an action. This article brings together many different perspectives on understanding human movement knowledge and movement interaction. This understanding shows that the embodied knowledge involved in movement interaction is very different from the representational knowledge involved in a traditional interface, so a very different approach to design is needed. We apply this knowledge to understand why interactive machine learning is an effective tool for motion interaction designers and to make a number of suggestions for future development of the technique.
An Effective Ensemble Framework for Multiobjective Optimization

This paper proposes an effective ensemble framework (EF) for tackling multiobjective optimization problems, by combining the advantages of various evolutionary operators and selection criteria that are run on multiple populations. A simple ensemble algorithm is realized as a prototype to demonstrate our proposed framework. Two mechanisms, namely competition and cooperation, are employed to drive the running of the ensembles. Competition is designed by adaptively running different evolutionary operators on multiple populations. The operator that better fits the problem’s characteristics will receive more computational resources, being rewarded by a decomposition-based credit assignment strategy. Cooperation is achieved by a cooperative selection of the offspring generated by different populations. In this way, the promising offspring from one population have chances to migrate into the other populations to enhance their convergence or diversity. Moreover, the population update information is further exploited to build an evolutionary potentiality model, which is used to guide the evolutionary process. Our experimental results show the superior performance of our proposed ensemble algorithms in solving most cases of a set of 31 test problems, which corroborates the advantages of our EF.
Zero-Shot Neural Transfer for Cross-Lingual Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-basedentity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario.1 Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.
MSST-ResNet: Deep multi-scale spatiotemporal features for robust visual object tracking

The performance of the tracking task directly depends on target object appearance features. Therefore, a robust method for constructing appearance features is crucial for avoiding tracking failure. The tracking methods based on Convolution Neural Network (CNN) have exhibited excellent performance in the past years. However, the features from each original convolutional layer are not robust to the size change of target object. Once the size of the target object has significant changes, the tracker drifts away from the target object. In this paper, we present a novel tracker based on multi-scale feature, spatiotemporal features and deep residual network to accurately estimate the size of the target object. Our tracker can successfully locate the target object in the consecutive video frames. To solve the multi-scale change issue in visual object tracking, we sample each input image with 67 different size templates and resize the samples to a fixed size. And then these samples are used to offline train deep residual network model with multi-scale feature that we have built up. After that spatial feature and temporal feature are fused into the deep residual network model with multi-scale feature, so that we can get deep multi-scale spatiotemporal features model, which is named MSST-ResNet feature model. Finally, MSST-ResNet feature model is transferred into the tracking tasks and combined with three different Kernelized Correlation Filters (KCFs) to accurately locate target object in the consecutive video frames. Unlike the previous trackers, we directly learn various change of the target appearance by building up a MSST-ResNet feature model. The experimental results demonstrate that the proposed tracking method outperforms the state-of-the-art tracking methods.
A QoE and Simulator Sickness Evaluation of a Smart-Exercise-Bike Virtual Reality System via User Feedback and Physiological Signals

In this paper, we proposed, designed, and evaluated a smart-exercise-bike virtual reality system. The proposed system uses a custom microcontroller-based board in order to transmit signals from a stationary exercise bike to a computer, while a commercially available head mounted display is used in order to immerse the users into a virtual reality environment. Various quality settings related to texture resolution and to the frame rate of the rendered environment were evaluated by the users, while simulator sickness was assessed using the Simulator Sickness Questionnaire. Furthermore, physiological responses in terms of electrocardiography and galvanic skin response signals were captured for the whole duration of the experiments in order to better assess the effects of rendering quality and simulator sickness on the quality of experience of the proposed smart-exercise-bike virtual reality system. The effect and correlation between rendering quality and simulator sickness is then discussed in this paper.
Gradual typing: a new perspective

We define a new, more semantic interpretation of gradual types and use it to ``gradualize'' two forms of polymorphism: subtyping polymorphism and implicit parametric polymorphism. In particular, we use the new interpretation to define three gradual type systems ---Hindley-Milner, with subtyping, and with union and intersection types--- in terms of two preorders, subtyping and materialization. We define these systems both declaratively ---by adding two subsumption-like rules--- which yields clearer, more intelligible, and streamlined definitions, and algorithmically by reusing existing techniques such as unification and tallying.
Weighted locality collaborative representation based on sparse subspace

This paper takes into account both unlabeled data and their local neighbors to learn their sparse representations, and proposes a face recognition approach named Weighted Locality Collaborative Representation Classifier based on sparse subspace (WLCRC). WLCRC firstly learns a subset of the original training data to build a much correlated dictionary, and then combines linear regression techniques together with weighted collaborative representation techniques to optimize the linear reconstruction of unlabeled data. It uses the newly built dictionary to learn the reconstruction coefficients for each unlabeled datum while considering the influence of its local neighbors. Classifications are performed according to the reconstruction residuals, and experimental results on benchmark datasets demonstrate that WLCRC is effective.
Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression

Generalization ability, which reflects the prediction ability of a learned model, is an important property in genetic programming (GP) for symbolic regression. Structural risk minimization (SRM) is a framework providing a reliable estimation of the generalization performance of prediction models. Introducing the framework into GP has the potential to drive the evolutionary process toward models with good generalization performance. However, this is tough due to the difficulty in obtaining the Vapnik-Chervonenkis (VC) dimension of nonlinear models. To address this difficulty, this paper proposes an SRM-driven GP approach, which uses an experimental method (instead of theoretical estimation) to measure the VC dimension of a mixture of linear and nonlinear regression models for the first time. The experimental method has been conducted using uniform and nonuniform settings. The results show that our method has impressive generalization gains over standard GP and GP with the 0.632 bootstrap, and that the proposed method using the nonuniform setting has further improvement than its counterpart using the uniform setting. Further analyzes reveal that the proposed method can evolve more compact models, and that the behavioral difference between these compact models and the target models is much smaller than their counterparts evolved by the other GP methods.
Joint image encryption and compression schemes based on 16 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.gif" overflow="scroll"><mml:mrow><mml:mo>×</mml:mo></mml:mrow></mml:math> 16 DCT

Joint image encryption and compression schemes have shown their great potential values in protecting compressed images. To achieve the protection, a trade-off between encryption power and compression ability needs to be considered. In this paper, we propose two new joint encryption and compression schemes, where one scheme emphasizes compression performance, another highlights protection performance. For a given plain-image, we first raster scan it into non-overlapping 16 × 16 blocks, then apply various encryption techniques to it. In the first scheme, encryption operations are conducted at the transformation stage and quantization stage of JPEG. As for the second scheme, we add the run/size and value (RSV) pairs' shuffling operation at JPEG's entropy coding stage after first scheme's encryption operations. Performance evaluations using various criteria are conducted to show that the first scheme has better compression efficiency, while the second scheme has better defense ability against the statistical attack.
Learning to recognise 3D human action from a new skeleton‐based representation using deep convolutional neural networks

Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.
Novelty search for global optimization

Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization.
Pedotransfer functions for predicting heavy metals in natural soils using magnetic measures and soil properties

In this study, pedotransfer functions were developed using magnetic measures and soil properties to evaluate the concentrations of some heavy metals (Cr, Co, Fe, Cu, Ni, Mn, and Zn) in soils developed on some igneous rocks (gabbro, diorite-gabbro, gabbro-diorite, monzodiorite, spilite basalt, granite, and porphyritic-granite) in Kurdistan province, western Iran. A total number of 105 samples from both rocks and the associated surface soils were taken; magnetic susceptibility at two frequencies, concentrations of selected metals, and physcio-chemical properties of soils were then measured. The highest concentrations of metals were obtained for Co, Ni, Fe, Mn, and Cu in soils developed on gabbro and for Zn and Cr in soils developed on porphyritic-granite and spilite basalt, respectively. Magnetic measurements in soils and associated rocks could explain 78, 74, 77, 72, 75, 68, and 69% of the total variability of Zn, Cu, Ni, Fe, Mn, Co, and Cr concentrations in the studied soils. Inclusion of soil physcio-chemical properties did not significantly improve the prediction. Therefore, it seems that magnetic measurements, as fast, non-destructive, and cost-effective tools, could solely be used to successfully predict heavy metals in natural ecosystems, especially in reconnaissance scale studies.
Hybrid secure and robust image watermarking scheme based on SVD and sharp frequency localized contourlet transform

In this paper, using singular value decomposition (SVD) and sharp frequency localized contourlet transform (SFLCT) a secure and robust image watermarking procedure is introduced. The SVD and SFLCT are applied on both watermark and original images and using the properties of the SVD and utilizing the advantages of the SFLCT, noticeable results of the watermarking requirements are obtained. Since most of the SVD based watermarking schemes are not resistant against ambiguity attacks and suffer from the false positive problem, this objection is resolved without adding extra steps to the watermarking algorithm and the suggested scheme is secure and resistant against ambiguity attacks. The simulation of the scheme is implemented and its robustness against various types of attacks is experimented. In comparison with some of the recent schemes this procedure shows high imperceptibility, capacity and robustness and these features make the scheme a suitable choice for the image processing applications.
Recognizing geochemical anomalies via stochastic simulation-based local singularity analysis

The complexity of geochemical variation patterns and the limited number of samples that can be collected jointly make uncertainty an element that could not be ignored in modeling the geochemical distributions and further identifying geochemical anomalies. Local singularity analysis (LSA) of stochastically simulated geochemical distributions is carried out in this paper to identify geochemical anomalies in southwestern Fujian province of China, with the uncertainty at unsampled locations and its propagation being considered. A supervised way by analyzing the sensitivity of area under curve (AUC) to threshold values to determine an appropriate threshold is also presented. The case study further illustrated and validated the procedure, and several insights can be obtained thereby: (1) the uncertainty of values at unsampled locations could significantly affect the grid-based LSA result, especially for those locations with weak singularities. An explanation from the LSA algorithm is that the estimation of singularity exponent involves multiple local neighborhoods of the location to study, uncertain values at any location within the neighborhood would affect the result. Locations with weak singularities are quite sensitive to such uncertainties in that enrichment or depletion patterns identified are likely to change from one to the other. (2) Sensitivity analysis of AUC to threshold values provides a feasible way to determine an appropriate threshold to derive the anomaly probability map, which can enhance the geochemical signatures with respect to the interpolation-based singularity map. The anomaly probability accounts for the local distribution structure of singularity exponents, hence providing a more discriminative measure to enhance geochemical signatures with respect to the single singularity exponent from interpolation-based LSA, which mainly reveals the trend of local singularities. (3) Several targets have been preliminarily delineated based on the anomaly probabilities and favorable geological conditions in the study area, which should be validated by other types of information available in near future.
Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem

In our state-of-the-art study, we improve neural network-based models for predicting energy consumption in buildings by parallelizing the CHC adaptive search algorithm. We compared the sequential implementation of the evolutionary algorithm with the new parallel version to obtain predictors and found that this new version of our software tool halved the execution time of the sequential version. New predictors based on various classes of neural networks have been developed and the obtained results support the validity of the proposed approaches with an average improvement of 75% of the average execution time in relation to previous sequential implementations.
Enhancing energy management at district and building levels via an EM-KPI ontology

The use of information and communication technologies facilitates energy management (EM) at both district and building levels, but also generates a considerable amount of data. To gain insights into such data, it is essential to resolve the cross-domain data interoperability problem and determine an approach to exchange performance information and insightful data among various stakeholders. This paper developed an EM-KPI (key performance indicator) ontology to exchange key performance information and data for districts and buildings. The ontology contains two components: namely KPIs and EM master data; these, respectively, represent the multi-level performance information for energy performance tracking and the key data for data exploitation. Through a demonstration, a sample linked dataset generated using the data correlation predefined in the ontology is presented. The linked data analysis proves the feasibility of the ontology for exchanging data among different stakeholders and for exploring insights in relation to performance improvement.