Reviewerly/exHarmony huggingface dataset:
An LP Approach for Solving Two-Player Zero-Sum Repeated Bayesian Games

This paper studies two-player zero-sum repeated Bayesian games in which every player has a private type that is unknown to the other player, and the initial probability of the type of every player is publicly known. The types of players are independently chosen according to the initial probabilities, and are kept the same all through the game. At every stage, players simultaneously choose actions, and announce their actions publicly. For finite horizon cases, an explicit linear program is provided to compute players' security strategies. Moreover, this paper shows that a player's sufficient statistics, which is independent of the strategy of the other player, consists of the belief over the player's own type, the regret over the other player's type, and the stage. Explicit linear programs, whose size is linear in the size of the game tree, are provided to compute the initial regrets, and the security strategies that only depends on the sufficient statistics. For discounted cases, following the same idea in the finite horizon, this paper shows that a player's sufficient statistics consists of the belief of the player's own type and the antidiscounted regret with respect to the other player's type. Besides, an approximated security strategy depending on the sufficient statistics is provided, and an explicit linear program to compute the approximated security strategy is given. This paper also obtains a bound on the performance difference between the approximated security strategy and the security strategy, and shows that the bound converges to 0 exponentially fast.
A New Probabilistic Representation of Color Image Pixels and Its Applications

This paper proposes a novel probabilistic representation of color image (PRCI) pixels and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them, respectively, in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that: 1) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem and 2) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.
A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction

State-of-the-art methods for 3D reconstruction of faces from a single image require 2D-3D pairs of ground-truth data for supervision. Such data is costly to acquire, and most datasets available in the literature are restricted to pairs for which the input 2D images depict faces in a near fronto-parallel pose. Therefore, many data-driven methods for single-image 3D facial reconstruction perform poorly on profile and near-profile faces. We propose a method to improve the performance of single-image 3D facial reconstruction networks by utilizing the network to synthesize its own training data for fine-tuning, comprising: (i) single-image 3D reconstruction of faces in near-frontal images without ground-truth 3D shape; (ii) application of a rigid-body transformation to the reconstructed face model; (iii) rendering of the face model from new viewpoints; and (iv) use of the rendered image and corresponding 3D reconstruction as additional data for supervised fine-tuning. The new 2D-3D pairs thus produced have the same high-quality observed for near fronto-parallel reconstructions, thereby nudging the network towards more uniform performance as a function of the viewing angle of input faces. Application of the proposed technique to the fine-tuning of a state-of-the-art single-image 3D-reconstruction network for faces demonstrates the usefulness of the method, with particularly significant gains for profile or near-profile views.
Continuous dynamical decoupling of spin chains: Modulating the spin-environment and spin-spin interactions

For spins chains to be useful for quantum information processing tasks, the interaction between the spin chain and its environment generally needs to be suppressed. In this paper, we propose the use of strong static and oscillating control fields in order to effectively remove the spin chain-environment interaction. We find that our control fields can also effectively transform the spin chain Hamiltonian. In particular, interaction terms which are absent in the original spin chain Hamiltonian appear in the time-averaged effective Hamiltonian once the control fields are applied, implying that spin-spin interactions can be engineered via the application of static and oscillating control fields. This transformation of the spin chain can then potentially be used to improve the performance of the spin chain for quantum information processing tasks. For example, our control fields can be used to achieve almost perfect quantum state transfer across a spin chain even in the presence of noise. As another example, we show how the use of particular static and oscillating control fields not only suppresses the effect of the environment, but can also improve the generation of two-spin entanglement in the spin chain.
Super Resolution of Car Plate Images Using Generative Adversarial Networks

Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. In practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio (PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this paper, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images is compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods.
Example-based image super-resolution via blur kernel estimation and variational reconstruction

Single image super-resolution aims at generating clear high-resolution image from one low-resolution image. Due to the limited low-resolution information, it is a challenging task to restore clear, artifacts-free image, meanwhile preserving finer structures and textures. This paper proposes an effective example-based image super-resolution method while making clear image and no compromise on quality. Firstly, the image prior is imposed on the anchor neighborhood regression model to optimize mapping coefficient for interim latent image construction. In order to remove its blur, kernel estimation iteration optimization algorithm is proposed based on the salient edges which are extracted through texture-structure discriminate minimum energy function and fractional order mask enhancement. Finally, an accurate reconstruction constraint combined with a simple gradient regularization is applied to reconstruct the super-resolution image. The proposed method is able to produce clear high-frequency texture details and maintain clean edges even under large scaling factors. Experimental results show that the proposed method performs well in visual effects and similarities. Furthermore, we test our algorithm in multi-texture images for robust evaluation. It is demonstrated that our algorithm is robust under complicated textures condition.
Robust multi-view continuous subspace clustering

This paper proposed a novel Robust Multi-View Continuous Subspace Clustering (RMVCSC) algorithm, which can untangle heavily mixed clusters by optimizing a single continuous objective. The proposed objective uses robust estimators to automatically clip specious inter-cluster connections while maintaining convincing intra-cluster correspondences in the common representation subspace learned from multiple views. The common representation subspace can reveal the underlying cluster structure in data. RMVCSC is optimized in an alternating minimization scheme, in which the clustering result and the common representation subspace are simultaneously optimized. Since different views can describe distinct perspectives of input data, the proposed algorithm has more accurate clustering performance than conventional algorithms by exploring information among multi-view data. In other words, the proposed algorithm optimizes a novel continuous objective in the simultaneously learned common representation subspace across multiple views. By using robust redescending estimators, the proposed algorithm is not prone to stick into bad local minima even with outliers in data. This kind of robust continuous clustering methods has never been used for multi-view clustering before. Moreover, the convergence of the proposed algorithm is theoretically proved, and the experimental results show that the proposed RMVCSC can outperform several very recent proposed algorithms in terms of clustering accuracy.
Clustering Based on Supervised Learning of Exemplar Discriminative Information

In machine learning and data mining applications, clustering is a critical task for knowledge discovery that attract attentions from large quantities of researchers. Generally, with the help of label information, supervised learning methods have more flexible structure and better result than unsupervised learning. However, supervised learning is infeasible for clustering task. In this paper, to fill the gap between clustering and supervised learning, the proposed clustering methods introduce the exemplars discriminative information into a supervised learning. To build the effective objective function, a strategy for reducing intracluster distance and increasing intercluster distance is introduced to form a unified optimization objective function. With initially setting the clustering centers, the data that near the centers are selected as exemplars to indicate the ground truth of different classes. Discriminative learning is then introduced to learn the partition hyperplane and classify all the data into different classes. New clustering centers are calculated for selecting new exemplars alternately. Using the proposed algorithms, the unsupervised K -means clustering problem is effectively solved from the perceptive of optimization. Feature mapping is also introduced to improve the performance by reducing the intercluster distance. A novel framework for exploring discriminative information from unsupervised data is provided. The proposed algorithms outperform the state-of-the-art approaches on a wide range of benchmark datasets in terms of accuracy.
Heat production estimation by using natural gamma-ray well-logging technique in phosphatic khneifis deposit in Syria

Natural gamma-ray (GR) well-logging technique is used to estimate the radioactive heat production (HP) parameter as a function of depth, in the phosphatic Khneifis deposit in Palmyrides region in Syria. Seven available drilled wells with their natural GR records are used to characterize and estimate the HP magnitude in the study region. The analysis of 2660 measured points in those wells shows that HP varies between a minimum of 0 and a maximum of 7.97 μw/m3, with an average of 1.33 μw/m3 and a standard deviation of 1.68 μw/m3. Multi-fractal approach with the use of concentration-number (C-N) model and log-log plots is originally proposed and used as a new approach for characterizing the HP variations and isolating different HP populations in the study region. The high HP ranges are mainly related to the phosphatic layers, relatively enriched by uranium concentrations, comparing with their surroundings.
Lightweight formalisation of adaptive middleware

The development of adaptive middleware systems is still a challenge due to the complexity of dealing with traditional adaptation issues, such as Why to adapt? When to adapt? Where is the need for change? What kind of change is needed? How is the adaptation performed? The emerging of new technologies (e.g., process mining) and maturation of existing ones (e.g., model checkers) create new opportunities to face those challenges. This paper presents an end-to-end solution for developing adaptive middleware that integrates software architecture concepts, lightweight formalisation and process mining to handle the mentioned challenges. The proposed solution allows developers to build adaptive middleware by working at a high level of abstraction, i.e., at the architectural level. In this way, it potentially reduces the effort to implement the middleware and reinforces structural, safety and quantitative properties at development time and when the middleware is already in execution.
On the computational complexities of three problems related to a privacy measure for large networks under active attack

With the arrival of modern internet era, large public networks of various types have come to existence to benefit the society as a whole and several research areas such as sociology, economics and geography in particular. However, the societal and research benefits of these networks have also given rise to potentially significant privacy issues in the sense that malicious entities may violate the privacy of the users of such a network by analyzing the network and deliberately using such privacy violations for deleterious purposes. Such considerations have given rise to a new active research area that deals with the quantification of privacy of users in large networks and the corresponding investigation of computational complexity issues of computing such quantified privacy measures. In this paper, we formalize three natural problems related to such a privacy measure for large networks and provide non-trivial theoretical computational complexity results for solving these problems. Our results show the first two problems can be solved efficiently, whereas the third problem is provably hard to solve within a logarithmic approximation factor. Furthermore, we also provide computational complexity results for the case when the privacy requirement of the network is severely restricted, including an efficient logarithmic approximation.
Online cost-rejection rate scheduling for resource requests in hybrid clouds

The hybrid cloud computing model has attracted considerable attention in recent years. Due to security and controllability of private cloud, some resource requests are required to be scheduled in private parts of hybrid cloud. However, these requests could be rejected because of the resource limitation of private parts of hybrid cloud. In this paper, all resource requests are classified into two categories, i.e., the special requests required to process in private cloud, and the normal requests insensitive in private or public clouds. By considering the normal requests undivided to dispatch a cloud and divided to dispatch different clouds, we propose the online cost-rejection rate scheduling strategy (OCS) for the normal requests undivided, and the online scheduling strategy for some normal requests divided (OCS-divided), which could make suitable requests placement decisions in real-time and minimize the cost of renting public cloud resources with a low rate of rejected requests. Then, we transform both online models into two one-shot optimization problems by taking advantage of Lyapunov optimization techniques, and employ the optimal decay algorithm to solve the one-shot problems. The simulation results demonstrate that our scheduling strategies can achieve the trade-off between cost and rejection rate, and process the real-time resource requests in hybrid clouds. OCS-divided can achieve an average cost saving of about 25% compared with OCS and maximize the resource utilization.
Bangla Handwritten Digit Recognition Using Deep Convolutional Neural Network

Handwritten Bangla digit recognition is one of the most challenging computer vision problems due to its diverse shapes and writing style. Recently deep learning based convolutional neural network known as deep CNN finds wide-spread applications in recognizing different objects due to its high accuracy. This paper investigates the performance of some state-of-the-art deep CNN techniques for the recognition of handwritten digits. It considers four deep CNN architectures, such as AlexNet, MobileNet, GoogLeNet (Inception V3), and CapsuleNet models. These four deep CNNs have been experimented on a large, unbiased and highly augmented standard dataset, NumtaDB and confirmed that the AlexNet showed the best performance on the basis of accuracy and computation time.
An assessment of total petroleum hydrocarbon and trace metal concentration in the sediments of Ugbo water way, South western Nigeria

Sediment samples from Ugbo water way, South Western Nigeria were assessed for total petroleum hydrocarbon (TPH) and trace metal concentrations. Seven sampling stations were selected and one control station. Total petroleum hydrocarbon (TPH) in the sediment samples was determined using Gas Chromatography-Flame Ionisation Detector (GC-FID) while trace metal concentrations were determined with Atomic Absorption Spectrophotometer (AAS). In situ measurement of the physicochemical parameters of the water was done during sampling. The average concentration of TPH in the sediment samples is 0.131 mg/kg which is far well below the maximum permissible limit (10 mg/kg) and the occurrence of the measured trace metals is in the decreasing order for the different metals Fe>Pb>Zn>Cu>Ni>Mn>Cd. Fe had the highest concentration with an average value of 0.845 mg/kg while Cd had the lowest average concentration of 0.032 mg/kg. The highest concentration of the TPH occurs at the point of offload of petroleum motor spirit (K5) and increases outwards in the down current direction as we travel away from K5. The levels of Cd, Fe, and Mn all exceeded the U.S.E.P.A recommended limit, while the rest of the metals (Pb, Zn, Cu, and Ni) were found to be within recommended limits. Key words: Trace metal, pollution, physico-chemical parameters, sediment.
Weakly Supervised Instance Segmentation Using Hybrid Networks

Weakly-supervised instance segmentation, which could greatly save labor and time cost of pixel mask annotation, has attracted increasing attention in recent years. The commonly used pipeline firstly utilizes conventional image segmentation methods to automatically generate initial masks and then use them to train an off-the-shelf segmentation network in an iterative way. However, the initial generated masks usually contains a notable proportion of invalid masks which are mainly caused by small object instances. Directly using these initial masks to train segmentation models is harmful for the performance. To address this problem, we propose a kind of hybrid networks in this paper. In our architecture, there is a principle segmentation network which is used to handle the normal samples with valid generated masks. In addition, a complementary branch is added to handle the small and dim objects without valid masks. Experimental results indicate that our method can achieve significantly performance improvement both on the small object instances and large ones, and outperforms all state-of-the-art methods.
Contrast enhancement and details preservation of tone mapped high dynamic range images

This paper proposes a Tone Mapping (TM) approach converting a High Dynamic Range (HDR) image into a Low Dynamic Range (LDR) image while preserving as much information of the HDR image as possible to ensure a good LDR image visual quality. This approach is based on a separable near optimal lifting scheme using an adaptive powerful prediction step. The latter relies on a linear weighted combination depending on the neighboring coefficients extracting then the relevant finest details in the HDR image at each resolution level. Moreover the approximation and detail coefficients are modified according to the entropy of each subband. The pixel’s distribution of the coarse reconstructed LDR image is then adjusted according to a perceptual quantizer with respect to the human visual system using a piecewise linear function. Simulation results provide good results, both in terms of visual quality and TMQI metric, compared to existing competitive TM approaches.
Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures

We propose a generalized 3D shape descriptor for the efficient classification of 3D archaeological artifacts.Our descriptor is based on a multi-view approach of curvature features, consisting of the following steps: pose normalization of 3D models, local curvature descriptor calculation, construction of 3D shape descriptor using the multi-view approach and curvature maps, and dimensionality reduction by random projections.We generate two descriptors from two different paradigms: 1) handcrafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier and 2) machine learnt, in which the descriptor automatically learns the object features through a pretrained deep neural network model (VGG-16) for transfer learning and passed on to the classifier.These descriptors are applied to two different archaeological datasets: 1) non-public Mexican dataset, represented by a collection of 963 3D archaeological objects from the Templo Mayor Museum in México City that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and 2) 3D pottery content-based retrieval benchmark dataset, consisting of 411 objects.Once the multi-view descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: K -nearest neighbor, support vector machine, and structured support vector machine.Our object descriptors classification results are compared against five popular 3D descriptors in the literature, namely, rotation invariant spherical harmonic, histogram of spherical orientations, signature of histograms of orientations, symmetry descriptor, and reflective symmetry descriptor.Experimentally, we were able to verify that our machine learnt and handcrafted descriptors offer the best classification accuracy (20% better on average than comparative descriptors), independently of the classification methods.Our proposed descriptors are able to capture sufficient information to discern among different classes, concluding that it adequately characterizes the datasets.