Reviewerly/exHarmony huggingface dataset:
Robust Visual Tracking via Hierarchical Particle Filter and Ensemble Deep Features

Particle filter algorithms are a very important branch for visual object tracking in the past decades, showing strong robustness to challenging scenarios with partial occlusion and large-scale variations. However, since a large number of particles need to be extracted for the accurate target state estimation, their tracking efficiency typically suffers especially when meeting deep convolutional features, which have been developed for handling significant variations of the target appearance in the visual tracking community. In this paper, we propose to elegantly exploit deep convolutional features with few particles in a novel hierarchical particle filter, which formulates correlation filters as observation models and breaks the standard particle filter framework down into two constituent particle layers, namely, particle translation layer and particle scale layer. The particle translation layer focuses on the object location with the deep convolutional features capturing semantics but failing to precisely estimate the object scale, while the particle scale layer pays attention to large-scale variations with the lightweight hand-crafted features handling spatial details of the object size. Moreover, an efficient ensemble method is proposed to help explore deeper convolutional features with more semantics in the particle translation layer. Extensive experiments on four challenging tracking datasets, including OTB-2013, OTB-2015, VOT2014, and VOT2015 demonstrate that the proposed method performs favorably against a number of state-of-the-art trackers.
A study of metal-bearing nanoparticles from the Kangjiawan Pb-Zn deposit and their prospecting significance

Over the years, using novel methods to find deep-seated ore resources has always been a hot research topic. In this paper, we present a new method to investigate deep-seated orebodies by characterizing metal-bearing nanoparticles (e.g., morphological features and chemical compositions) in various media (deep/shallow groundwater, ascending gas flow, and soil) via transmission electron microscopy (TEM). From the Kangjiawan Pb-Zn deposit (the study area), the metal-bearing nanoparticles collected from different media contain similar contents of ore-forming elements (e.g., Cu, Zn, Pb, Fe and S) and are strongly related to the presence of deep-seated orebodies. These ore-related nanoparticles are grouped into four main categories: (i) amorphous, (ii) single-crystal, (iii) poly-crystal and (iv) complex poly-crystal. In particular, many amorphous nanoparticles in different media can transfer ore-forming elements and provide mineralization information. We propose that the ore-forming elements exist and migrate in the form of these four types of nanoparticles. The results can explain the geochemical anomalies and provide insights on the role of nanoparticles in post-mineralization ore-related elemental dispersion, and could be useful in exploration for concealed orebodies.
Quantum Pyragas control: Selective control of individual photon probabilities

Pyragas control allows us to stabilize unstable states in applied nonlinear science. We propose to apply a quantum version of the Pyragas protocol to control individual photon probabilities in an otherwise only globally accessible photon-probability distribution of a quantum light emitter. The versatility of quantum Pyragas control is demonstrated for the case of a two-level emitter in a pulsed laser-driven half cavity. We show that one- and two-photon events respond in a qualitatively different way to the half-cavity-induced feedback signal. One-photon events are either enhanced or suppressed, depending on the choice of parameters. In contrast, two-photon events undergo exclusively an enhancement up to $50%$ for the chosen pulse areas. We hereby propose an implementation of quantum Pyragas control via a time-delayed feedback setup.
Screen Content Image Quality Assessment With Edge Features in Gradient Domain

Objective visual quality assessment specific for screen content images (SCIs) has been increasingly investigated over the years. In this paper, an effective full-reference quality evaluation model for SCIs is proposed, in which edge features in gradient domain (EFGD) are extracted for better visual perceptual representation. Unlike traditional edge feature extraction directly in the image pixel domain, all edge features in the proposed EFGD model are extracted based on the gradient map of input SCIs, including edge sharpness, edge brightness/contrast, and edge chrominance. Specifically, the gradient profile model that can well represent the spatial layout of edges is adopted to measure the edge sharpness degree. A novel computation way is reported to measure the edge brightness and contrast change between the reference and distorted SCIs, while color moments are used to account for the color chrominance variation. In addition, an adaptive weighting strategy is designed to adjust the effects of these three kinds of edge features, according to the statistical distributions of the input SCIs. Moreover, the maximum value of edge sharpness features is extracted from the test SCIs as the pooling weight to get the final image quality assessment (IQA) score. The experimental results on two commonly used SCIs databases have verified the superiorities of the EFGD model and show that the EFGD model is in more conformity with the subjective assessment results than most of the existing IQA models.
Color-Guided Depth Image Recovery With Adaptive Data Fidelity and Transferred Graph Laplacian Regularization

Depth images play an important role and are prevalently used in many computer vision and computational imaging tasks. However, due to the limitation of active sensing technology, the captured depth images in practice usually suffer from low resolution and noise, which prevents its further applications. To remedy this problem, in this paper, we first propose an adaptive data fidelity formulation to optimally generate each depth pixel from a mixture probability distribution, characterizing the similarity both in the depth map and the corresponding high-resolution guided color image. The proposed method is able to fit the distribution of the input depth signal as an optimization problem by maximizing the mixture probability. Furthermore, to promote the piecewise property that depth images exhibit, we propose a transferred graph Laplacian model as a regularization term, which is general and able to handle various depth recovery tasks such as super-resolution and denoising well. Specifically, each pixel within the recovered depth image is represented as a vertex in a graph with weights in connected edges representing the similarity between vertices. By minimizing the squared variations of the image signal, the task of depth image recovery can be converted to the problem of graph-based image filtering. Since the proposed graph Laplacian regularization model is able to fully exploit a priori information about the depth image, a much more accurate and robust estimation of the underlying depth can be obtained. Extensive experiment evaluations verify that the proposed method obtains recovered depth with higher quality in terms of both objective and subjective criteria, compared with most of the state-of-the-art methods.
Recurrent Recommendation with Local Coherence

We propose a new time-dependent predictive model of user-item ratings centered around local coherence -- that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences.
Fracture behavior of carbon fiber reinforced polymer composites: An optical study of loading rate effects

Crack initiation and growth in single-edge notched unidirectional T800s/3900-2 CFRP laminates are studied under stress wave and quasi-static loading conditions. An optical technique called reflection-mode Digital Gradient Sensing is also extended to study fracture mechanics of CFRP by using it in conjunction with ultrahigh-speed photography to perform full-field measurement of crack-tip deformations in the pre- and post-crack initiation regimes. DGS is capable of measuring two orthogonal surface slopes in the crack-tip vicinity as angular deflection of light rays. A method for extracting crack-tip parameters – the instantaneous crack speed and stress intensity factor (SIF) histories – associated with the stationary and propagating cracks using measured surface slopes is presented. The effect of fiber orientation in the range 0°–60° relative to the initial notch and two loading rates are investigated. Nominally mode-I fracture occurs when the fiber orientation is 0° whereas mixed-mode fractures ensue in others. Besides crack initiation occurrence at higher loads as fiber orientation increases, the SIF histories imply strong fiber bridging at low fiber orientations under quasi-static conditions. Furthermore, this CFRP shows significant loading rate dependence during crack growth. Unlike stress wave loading conditions, an increasing crack growth resistance immediately after crack initiation is seen under quasi-static conditions.
Impedance-Based Gaussian Processes for Modeling Human Motor Behavior in Physical and Non-Physical Interaction

Modeling of human motor intention plays an essential role in predictively controlling a robotic system in human-robot interaction tasks. In most machine learning techniques, human motor behavior is modeled as a generic stochastic process. However, the integration of a priori knowledge about underlying system structures can provide insights on otherwise unobservable intrinsic states that yield the superior prediction performance and increased generalization capabilities.We present a novel method for modeling human motor behavior that explicitly includes a neuroscientifically supported model of human motor control, in which the dynamics of the human arm are modeled by a mechanical impedance that tracks a latent desired trajectory. We adopt a Bayesian setting by defining Gaussian process (GP) priors for the impedance elements and the latent desired trajectory. This enables exploitation of a priori human arm impedance knowledge for regression of interaction forces through inference of a latent desired human trajectory.The method is validated using simulated data, with particular focus on effects of GP prior parameterization and intention estimation capabilities. The superior prediction performance is shown with respect to a naive GP prior. An experiment with human participants evaluates generalization capabilities and effects of training data sparsity.We derive the correlations of an impedance-based GP model of human motor behavior that exploits a priori knowledge.The model effectively predicts interaction forces by inferring a latent desired human trajectory in previously observed as well as unobserved regions of the input space.
Optical Character Recognition System for Nastalique Urdu-Like Script Languages Using Supervised Learning

There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.
Dynamic defense strategy against advanced persistent threat under heterogeneous networks

Advanced persistent threats (APTs) pose a grave threat in cyberspace because of their long latency and concealment. In this paper, we propose a hybrid strategy game-based dynamic defense model to optimally allocate constrained secure resources for the target network. In addition, values of profits of players in this game are computed by a novel data-fusion method called NetF. Based on network protocols and log documents, the NetF deciphers data packets collected from different networks to natural language to make them comparable. Using this algorithm, data observed from the Internet and wireless sensor networks (WSNs) can be fused to calculate the comprehensive payoff of every node precisely. The Nash equilibrium can be computed using the value to detect the possibility of a node being a malicious node. Using this method, the dynamic optimal defense strategy can be allocated to every node at different times, which enhances the security of the target network obviously. In experiments, we illustrate the obtained results via case studies of a cluster of heterogeneous networks. The results guide planning of optimal defense strategies for different kinds of nodes at different times.
Robust detection of median filtering based on combined features of difference image

Median filtering is a widely used method for denoising and smoothing regions of an image; it has drawn much attention from researchers of image forensic. A new detection scheme of median filtering based on combined features of difference image (CFDI) is proposed in this paper. In the proposed scheme, the combined features consist of joint conditional probability density functions (JCPDFs) of first-order and second-order difference image (DI), the principal component analysis (PCA) is used to reduce the dimensionality of JCPDFs, and thus, the final features are obtained for the given threshold. A large number of experiments on single database and compound databases show that, the proposed scheme achieves superior performance on the uncompressed image datasets, and it also achieves better performance compared with state-of-the-art methods, especially for strong JPEG compression and low resolution images.
Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps

Developing a comprehensive explanation of complex social phenomena is a difficult task that analysts often have to perform using vast collections of text documents. On the one hand, solutions exist to assist analysts in creating causal maps from text documents, but these can only articulate the relationships at work in a problem. On the other hand, Fuzzy Cognitive Maps (FCMs) can articulate these relationships and perform simulations, but no environment exists to help analysts in iteratively developing FCMs from text. In this paper, we detail the design and implementation of the first tool that allows analysts to develop FCMs from text collections, using interactive visualizations. We make three contributions: (i) we combine text mining and FCMs, (ii) we implement the first visual analytics environment built on FCMs, and (iii) we promote a strong feedback loop between interactive data exploration and model building. We provide two case studies exemplifying how to create a model from the ground-up or improve an existing one. Limitations include the increase in display complexity when working with large collection of files, and the reliance on KL-divergence for ad-hoc retrieval. Several improvements are discussed to further support analysts in creating high-quality models through interactive visualizations.
Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories

A large number of spatio-temporal trajectory data is being generated from GPS enabled devices such as cars, smartphones, and sensors. These trajectory datasets representing objects' movements provide new opportunities for enhanced spatio-temporal periodic pattern mining. These GPS collected trajectory datasets represent real-world movement phenomena and thus they are spatially placed, temporally recorded, aspatial semantically meaningful, hierarchically structured, and irregularly sampled. Periodic pattern mining from spatio-temporal trajectories is to find temporal regularities from these spatio-temporal trajectories, and thus must take into these five characterisics into account in order not to miss any spatio-temporally, semantically and hierarchically meaningful patterns from irregularly sampled spatio-temporal trajectories. Traditional periodic pattern mining fails to consider these five conditions simultaneously, and in this paper, we propose a hierarchical clustering based semantic periodic pattern mining to consider the five aspects: spatiality, temporality, semantics, hierarchy, and irregularity. Experimental results demonstrate the effectiveness of our proposed method against traditional periodic pattern mining approaches.
FPGA based acceleration of game theory algorithm in edge computing for autonomous driving

For autonomous driving area, the communication among autonomous vehicles is quite frequent and inevitable to perceive the surrounding environment. Massive data needs to be processed in time on the edge to control vehicles to make suitable strategies. For a group of vehicles, collaborative strategies are necessary to avoid unintentional congestions. However, less attention is paid to competition and cooperation between vehicle groups. In this paper, we explore the possibility of adopting game theory for decision making to obtain win-win between autonomous vehicles. The Lemke-Howson algorithm in game theory is the best known combinatorial algorithm that computes a Nash equilibrium of a bimatrix game. More importantly, we implement the Lemke-Howson algorithm on FPGA to accelerate the computation process. We explain the design challenges of solving the performance bottleneck and how to make optimizations. We implement the Lemke-Howson accelerator on a KCU116 board and obtain a speedup of about 2.4 times versus that running on a CPU.
Hiding clinical information in medical images: An encrypted dual-image reversible data hiding algorithm with base-3 numeral framework

In current scenario, dual-image reversible data hiding techniques (RDH) have become very popular due to their ability to give more embedding rate and improved visual quality of stego images than conventional RDH algorithms. All these methods have been observed to exhibit superior results, but only on natural images. In case of the medical images, there is an inherent risk of underflow, since a lot of pixels tend to have low intensity value. So, a novel encrypted dual image reversible data hiding algorithm has been presented here that gives higher embedding rate than all the existing dual-image reversible data hiding algorithms by changing over secret binary message into base-3 numeral framework without any underflow issue. The reported results reveal that the proposed algorithm gave very high embedding rates on both natural and medical images. The embedding rates on medical images have been significantly higher than all the compared approaches. The proposed approach yielded an embedding rate of 1.53 bits per pixel for medical images, almost double than most of the existing methods.
A Virtual Reality and Retailing Literature Review: Current Focus, Underlying Themes and Future Directions

This literature review reveals the current research focus, underlying themes and prominent research gaps in the Virtual Reality (VR) literature. 89 journal articles from the 22 years are thematically analysed in order to non-obvious reveal interconnections and themes, including research focus over time and underlying themes by research discipline. Over half of all papers focus on the need to understand the VR shopping consumer, yet no consensus exists as to what the optimal experience is or how to design effective v-Commerce stores. The most prominent research gaps are related to the unique HCI aspects in v-Commerce that influence shopping behaviours. The impact of this review is establishing the current challenges and future directions for academia in order to make v-Commerce a viable reality. Specifically, future research should mainly focus on develop human factor theory in VR shop design (i.e. social dimension, eye-tracking etc.).
High-Performance Self-Synchronous Blind Audio Watermarking in a Unified FFT Framework

This paper presents a blind audio watermarking method that uses two different schemes to hide binary bits and auxiliary information within separate ranges of a fast Fourier transform (FFT) sequence. An adaptive vector norm modulation (AVNM) scheme is introduced to achieve a satisfactory balance of imperceptibility, robustness, and payload capacity. An improved spread spectrum (ISS) scheme is developed to produce a striking correlation peak, which facilitates the detection of synchronization codes in the FFT domain. The combination of robust audio segment extraction and recursive FFT makes it possible to execute these two FFT-based schemes in tandem on a sample-by-sample basis. The experiment results confirm that watermark embedding causes merely a negligible degradation in perceptual quality. A detectability test proved the effectiveness of the ISS scheme in self-synchronization as well as hiding auxiliary data. Three versions of AVNM with capacities ranging from 344.53 to 1033.59 bits per second were demonstrated. Compared with six recently developed schemes, AVNM exhibited advantages in terms of negligible quality distortion, flexible payload capacity, and excellent robustness against a variety of common signal processing attacks.