Reviewerly/exHarmony huggingface dataset:
Fuzzy c-means clustering based active contour model driven by edge scaled region information

This research article proposes a novel edge scaled method with local and global region based statistical information for inhomogeneous image segmentation. We coordinate region force (local and global) term with geodesic edge term in level set formulation. The vital energy of the proposed model utilizes both global and local terms fused with compacted geodesic edge term in an additive fashion, which uses image gradient information to segment feeble boundaries inside images. The initialization of the level set is always vulnerable and its execution is liable to suitable initialization, which requires manual intercession. In this regard, the proposed method is extended and automated by integrating the proposed method with FCM (fuzzy c-means) clustering. Modified active contour method gets suitable initialization from FCM clustering, which eliminates the initial contour problem existed in the traditional region-based active contour methods. A development in this paper is to interface FCM with level set strategy by morphological tasks. Moreover, the uninterrupted segmentation scheme confirms the effectiveness of the proposed method in modern intelligent and automatic systems. The experimental analysis is performed on synthetic and real inhomogeneous images. The visual and quantitative results confirm the proficiency of the proposed technique and show that proposed method achieves better accuracy results compared to previous methods.
Nature-inspired approach: An enhanced moth swarm algorithm for global optimization

The moth swarm algorithm (MSA) is a recent swarm intelligence optimization algorithm, but its convergence precision and ability can be limited in some applications. To enhance the MSA's exploration abilities, an enhanced MSA called the elite opposition-based MSA (EOMSA) is proposed. For the EOMSA, an elite opposition-based strategy is used to enhance the diversity of the population and its exploration ability. The EOMSA was validated using 23 benchmark functions and three structure engineering design problems. The results show that the EOMSA can find a more accurate solution than other population-based algorithms, and it also has a fast convergence speed and high degree of stability.
Multiagent system for dynamic resource provisioning in cloud computing platforms

Dynamic provisioning of computational resources in cloud platforms is a challenging problem. In this scenario, multiagent system technology may offer noticeable improvements, with intelligent agents to dynamically choose the best computational load distribution reducing execution time and cloud services cost. Thus, this article presents MAS-Cloud, a multiagent system to dynamically monitor, predict and provide computational resources in cloud platforms. Deductive reasoning agents work cooperatively in a three layer architecture to provide transparent horizontal elasticity of virtual machines in public cloud platforms (i.e., Google, Amazon EC2). MAS-Cloud was evaluated with a nondeterministic and CPU intensive application providing a challenging validation case. A multiple linear regression model was used presenting good predictions with reduced mean absolute percent error achieving 3.59% CPU usage and 5.28% of execution time in the Google platform, and 3.97% CPU usage and 5.47% of execution time in the Amazon EC2 platform.
Side scan sonar image segmentation and synthesis based on extreme learning machine

This paper presents side scan sonar (SSS) image segmentation and synthesis methods based on extreme learning machine (ELM). As an algorithm derived from single-hidden layer feedforward neural networks (SLFNs), ELM has superior performance and fast learning speed with randomly generated hidden layer parameters. The SSS image segmentation uses ELM as a classifier with features generated by convolutional neural network (CNN) of multiple pathways. The CNN of multiple pathways can learn local and global features from SSS images adaptively. Taking these features as input, ELM assigns the central pixel of each input image patch of CNN to one class. Moreover, the presented SSS image synthesis method utilizes ELM as a regression algorithm, in which the non-parametric sampling algorithm is used first to synthesize coarse SSS images according to segmentation maps and sample images for each class. Then ELM trained with the coarse synthesis images and their ground truth maps (the Gaussian-filtered SSS images) synthesizes fine SSS images. Furthermore, peak signal to noise ratio (PSNR) of the synthetic SSS images with the Gaussian-filtered SSS images as ref is used as one evaluation metric for segmentation performance. Experimental results demonstrate that the SSS image segmentation method combining convolutional features with ELM outperforms typical CNN and support vector machine (SVM), and the presented SSS image synthesis method and the evaluation metric are applicable.
A center-driven image set partition algorithm for efficient structure from motion

This paper proposes a novel center-driven image set partitioning method dedicated for efficient Structure from Motion (SfM) on unevenly distributed images. First, multiple base clusters are found at places with high image density. Instead of building a small initial model from two images, we build multiple initial base models from these base clusters. This promises that the scene is reconstructed from dense places to sparse areas, which can reduce error accumulation when images have weak overlap. Second, the whole image set is divided into several region clusters to decide which images should be reconstructed from the same base model. In this step, the base models are treated as centers and the affinity between an image with each of them is measured by the reconstruction path length. To enable faster speed, images in each region cluster are further divided into several sub-region clusters so that they could be added to the same base model simultaneously. Based on the above partitioning results, the partial 3D models are reconstructed in parallel and then merged. Experiments show that the proposed method achieves remarkable speedup and better completeness than state-of-the-art methods, without significant accuracy deterioration.
Empirical mode decomposition and adaptive bilateral filter approach for impulse noise removal

In this article, we introduced a novel algorithm to identify and correct images affected by impulse noise. The proposed technique composed of two stages: noisy pixels identification and restoration of them. Here, empirical mode decomposition is used to identify the pixels affected by impulse noise and adaptive bilateral filter is used to restore those noisy pixels. Mean absolute difference of the intrinsic mode functions (IMFs) are compared with the two dimensional cross entropic threshold value in order to identify the pixels affected by the impulse noise. In the next stage of the processing, an adaptive bilateral filter is used to retain the fine details and remove the noisy components in the image. The performance of the proposed scheme is evaluated on different benchmark images. Four performance evaluation measures: Peak signal to noise ratio (PSNR), Image Enhancement Factor (IEF), Mean Structure Similarity Index (MSSIM) and Correlation Factor (CF) are used to test the performance of the proposed algorithm. The simulation results of the proposed algorithm claim better result than the other existing state-of-the-art algorithms.
An Empirical Study on Forensic Analysis of Urdu Text Using LDA-Based Authorship Attribution

In the recent years, text-based digital forensic has evolved into a major research domain that supports digital investigation.A piece of text can be a critical source of information that is written by somebody with respect to writing style, usage of typical vocabulary, and so on.In this paper, we present a unified approach for intelligent association analysis of text of how much a piece of text is related to a person with respect to his stylometric writing features.The latent Dirichlet allocation (LDA)-based approach emphasizes on instance-based and profile-based classification of an author's text.Here, LDA suitably handles the high dimensional and sparse data by allowing more expressive representation of text.The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing styles of authors, and the inherent ambiguity of Urdu language text.A large corpus was collected for performance testing of the presented approach.The results of the experiments show the superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authorship attribution.Manifold contributions of the presented paper are use of improved sqrt-cosine similarity with LDA topics to measure similarity in vectors of text documents for the forensic analysis purpose, construction of a large data set of 6000 documents of articles, and achievement of (92% f1-measure) results on articles without using any labels for authorship attribution task.
Encryption of medical image with most significant bit and high capacity in piecewise linear chaos graphics

Along with the development of cloud computing, data privacy has become the focus of attention. However, the existing methods do not allow large amounts of information to be hidden in reversible ways. In this paper, a MSB prediction-based high-capacity reversible data hiding image encryption algorithm is put forward. Due to the local correlation between the pixels and their adjacent areas in a clear image, the value of adjacent two pixels are very close. For this reason, it seems reasonable to predict pixel values by using previously decrypted images, just as many image encoding and compression methods. However, in some cases, there are some errors. Therefore, the first step of the algorithm includes identifying all prediction errors in the original image and storing the information in a binary map of the error locations. After that, a high-capacity reversible data hiding method (CPHCRDH) is proposed to correct the prediction error. This method first conducts the prediction error correction before encryption, and preprocesses the original image according to the error location map to avoid all prediction errors, and then encrypts the pre-processed image. The clear image can be reconstructed without damage through MSP prediction. Finally, through the simulation experiment on three selected CT image test examples of the eyes, body and brain, it is shown that the proposed method is significantly superior to the selected contrasted algorithm in six indicators such as the horizontal and vertical correlation coefficients.
Image encryption using sequence generated by cyclic group

Image encryption using permutation is gaining its popularity over general encryption schemes like AES, DES, RSA etc., due to its high security, less time complexity using reasonable computational overheads. Mainly chaotic functions are employed in permutation based techniques to define a sequence, based on which the pixels or bits of an image are permuted. In parallel, researches are also carried out to define permutation using non chaotic techniques. In this paper, a novel non-chaotic image encryption technique is proposed. The properties of cyclic group are used as the backbone of the proposed method and using these properties some sequences/permutations are defined. These permutations are used for row/column level permutation of pixels and bit-level permutation. Iterative pixel addition operation with bit shifting using a 'Transform array' transforms the pixel value of the bit permuted image. Experimental results show that the proposed scheme is secure against statistical and differential attacks and provide a secure and efficient way for digital image encryption.
Tc-PEDCKS: Towards time controlled public key encryption with delegatable conjunctive keyword search for Internet of Things

With the widespread adoption of Internet of Things (IoT) and cloud computing, more and more individuals and organizations are outsourcing their IoT data in the cloud server. Under these circumstances, how to protect the privacy and security of the outsourced data becomes a key challenge. Public key encryption with keyword search (PEKS) is a promising technology to solve the problem. In this paper, we present a new kind of PEKS named time controlled public key encryption with delegated conjunctive keyword search (tc-PEDCKS) for IoT deployment. A basic tc-PEDCKS is firstly presented which supports conjunctive keyword search and enables a data user to delegate access right over the data to others in a limited time period. To resist against the keyword guessing attacks, a more secure tc-PEDCKS with designated server construction is also presented. The two constructions are proved secure in the random oracle model. The performance evaluation and experimental results demonstrate that our constructions are practical and feasible for real world IoT scenarios.
Model Predictive Trajectory Planning for Automated Driving

In order to enable automated driving systems on the road, several key challenges need to be solved. One of these issues is real-time maneuver decision and trajectory planning. This paper introduces a general framework for maneuver and trajectory planning with model predictive methods. It discusses several representations of this framework in distinct complexity levels. In general, sophisticated models require to nonquadratic objective functions with nonlinear constraints, leading to increased computational complexities during calculation. Yet, oversimplified models can neither cope with vehicle dynamics in critical maneuvers nor do they represent complex traffic scenes with several maneuver options appropriately. A scheme to partition the trajectory space into homotopy regions is proposed. In each homotopy class, linearization about a trajectory from this class is applied. Demonstrations by simulation and with experimental vehicles show the capability of the proposed method in selecting optimal maneuvers and trajectories. This is even valid during extreme maneuvers, such as in last moment collision avoidance.
Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.
Adversarial autoencoders with constant-curvature latent manifolds

Constant-curvature Riemannian manifolds (CCMs) have been shown to be ideal embedding spaces in many application domains, as their non-Euclidean geometry can naturally account for some relevant properties of data, like hierarchy and circularity. In this work, we introduce the CCM adversarial autoencoder (CCM-AAE), a probabilistic generative model trained to represent a data distribution on a CCM. Our method works by matching the aggregated posterior of the CCM-AAE with a probability distribution defined on a CCM, so that the encoder implicitly learns to represent data on the CCM to fool the discriminator network. The geometric constraint is also explicitly imposed by jointly training the CCM-AAE to maximise the membership degree of the embeddings to the CCM. While a few works in recent literature make use of either hyperspherical or hyperbolic manifolds for different learning tasks, ours is the first unified framework to seamlessly deal with CCMs of different curvatures. We show the effectiveness of our model on three different datasets characterised by non-trivial geometry: semi-supervised classification on MNIST, link prediction on two popular citation datasets, and graph-based molecule generation using the QM9 chemical database. Results show that our method improves upon other autoencoders based on Euclidean and non-Euclidean geometries on all tasks taken into account.
Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters.This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style.The key technologies of the proposed approach include the following:(1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures.Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator.Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories.Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character.Seven common Chinese characters have been used in an experiment for system validation and evaluation.The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style.
Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning

Class imbalanced learning is a well-known issue, which exists in real-world applications. Datasets that have skewed class distribution raise hindrance to the traditional learning algorithms. Traditional classifiers give the same importance to all the samples, which leads to the prediction biased towards the majority classes. To solve this intrinsic deficiency, numerous strategies have been proposed such as weighted extreme learning machine (WELM), weighted support vector machine (WSVM), class-specific extreme learning machine (CS-ELM) and class-specific kernelized extreme learning machine (CSKELM). This work focuses on multiclass imbalance problems, which are more difficult compared to the binary class imbalance problems. Kernelized extreme learning machine (KELM) yields better results compared to the traditional extreme learning machine (ELM), which uses random input parameters. This work presents a generalized CSKELM (GCSKELM), the extension of our recently proposed CSKELM, which addresses the multiclass imbalanced problems more effectively. The proposed GCSKELM can be applied directly to solve the multiclass imbalanced problems. GCSKELM with Gaussian kernel function avoids the non-optimal hidden node problem associated with CS-ELM and other existing variants of ELM. The proposed work also has less computational cost in contrast with kernelized WELM (KWELM) for multiclass imbalanced learning. This work employs class-specific regularization parameters, which are determined by employing class proportion. The extensive experimental analysis shows that the proposed work obtains promising generalization performance in contrast with the other state-of-the-art imbalanced learning methods.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.
Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving

This paper presents an approach, using an anticipatory kinodynamic motion planner, for obtaining the best trajectory and velocity profile for autonomous driving in dynamic complex environments, such as driving in urban scenarios. The planner discretizes the road search space and looks for the best vehicle path and velocity profile at each control period of time, assuming that the static and dynamic objects have been detected. The main contributions of the work are in the anticipatory kinodynamic motion planner, in a fast method for obtaining the G2-splines for path generation, and in a method to compute and select the best velocity profile at each candidate path that fulfills the vehicle kinodynamic constraints, taking into account the passenger comfort. The method has been developed and tested in MATLAB through a set of simulations in different representative scenarios, involving fixed obstacles and moving vehicles. The outcome of the simulations shows that the anticipatory kinodynamic planner performs correctly in diverse dynamic scenarios, maintaining smooth accelerations for passenger comfort.
Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification

The sparse representation-based classification (SRC) has been utilized in many applications and is an effective algorithm in machine learning. However, the performance of SRC highly depends on the data distribution. Some existing works proved that SRC could not obtain satisfactory results on uncontrolled data sets. Except the uncontrolled data sets, SRC cannot deal with imbalanced classification either. In this paper, we proposed a model named sparse supervised representation classifier (SSRC) to solve the above-mentioned issues. The SSRC involves the class label information during the test sample representation phase to deal with the uncontrolled data sets. In SSRC, each class has the opportunity to linearly represent the test sample in its subspace, which can decrease the influences of the uncontrolled data distribution. In order to classify imbalanced data sets, a class weight learning model is proposed and added to SSRC. Each class weight is learned from its corresponding training samples. The experimental results based on the AR face database (uncontrolled) and 15 KEEL data sets (imbalanced) with an imbalanced rate ranging from 1.48 to 61.18 prove SSRC can effectively classify uncontrolled and imbalanced data sets.