Reviewerly/exHarmony huggingface dataset:
Codebook-Free Compact Descriptor for Scalable Visual Search

The MPEG compact descriptors for visual search (CDVS) is a standard toward image matching and retrieval. To achieve high retrieval accuracy over a large scale image/video dataset, recent research efforts have demonstrated that employing extremely high-dimensional descriptors such as the Fisher vector (FV) and the vector of locally aggregated descriptors (VLAD) can yield good performance. Since the FV (or VLAD) possesses high discriminability but small visual vocabulary, it has been adopted by CDVS to construct a global compact descriptor. In this paper, we study the development of global compact descriptors in the completed CDVS standard and the emerging compact descriptors for video analysis (CDVA) standard, in which we formulate the FV (or VLAD) compression as a resource-constrained optimization problem. Accordingly, we propose a codebook-free aggregation method via dual selection to generate a global compact visual descriptor, which supports fast and accurate feature matching free of large visual codebooks, fulfilling the low memory requirement of mobile visual search at significantly reduced latency. Specifically, we investigate both sample-specific Gaussian component redundancy and bit dependency within a binary aggregated descriptor to produce compact binary codes. Our technique contributes to the scalable compressed Fisher vector (SCFV) adopted by the CDVS standard. Moreover, the SCFV descriptor is currently serving as the frame-level hand-crafted video feature, which inspires the inheritance of CDVS descriptors for the emerging CDVA standard. Furthermore, we investigate the positive complementary effect of our standard compliant compact descriptor and deep learning based features extracted from convolutional neural networks with significant mean average precision gains. Extensive evaluation over benchmark databases shows the significant merits of the codebook-free binary codes for scalable visual search.
Clip: A Disk I/O Focused Parallel Out-of-Core Graph Processing System

Existing parallel out-of-core graph processing systems focus on improving disk I/O locality, which leads to restrictions on their programming models. Although improving the locality, these constraints also restrict the expressiveness and hence only sub-optimal algorithms are supported. These sub-optimal algorithms typically incur sequential, but much larger, amount of disk I/O. In this paper, we explore a fundamentally different tradeoff: less total amount of I/O rather than better locality. We show that out-of-core graph processing systems uniquely provide the opportunities to lift the restrictions of the programming model in a feasible manner. To demonstrate the ideas, we build Clip, which enables more efficient algorithms that require much less amount of total disk I/O. Our experiments show that the algorithms that can be only implemented in Clip are much faster than the original disk-locality-optimized algorithms. We also further extend our technique's scope of application by providing a semi-external mode. Our analysis and evaluation demonstrate that semi-external is not only feasible for many cases, but also be able to deliver a significant speedup for important graph applications. Moreover, we further improve the performance of originally supported applications by designing more optimizations and evaluate our system on NVMe SSD.
CI-SNF: Exploiting contextual information to improve SNF based information retrieval

Similarity networks contain important topological features and patterns critical to understanding interactions among samples in a large dataset. To create a comprehensive view of the interactions within a dataset, the Similarity Network Fusion (SNF) technique has been proposed to fuse the similarity networks based on different data types into one similarity network that represents the full spectrum of underlying data. In this paper, a modified version of SNF, which is named as Contextual Information based SNF (CI-SNF), is proposed. In CI-SNF, first, modified Jaccard distance is performed on the SNF fused similarity to utilize the contextual information contained in the fused similarity network. Second, the local consistency of samples from the same category is enhanced by speculating that the samples which are located high in the Jaccard distance based ranking list of a specific query are from the same category as the query. Third, the inverted index technique is introduced to utilize the sparsity property of the locally consistent similarity network to enhance the computational efficiency. To verify the effectiveness and efficiency of CI-SNF model, it is applied in four different tasks, Cover Song Identification (CSI), image classification, cancer subtype identification, and drug taxonomy, respectively. Extensive experiments on thirteen challenging datasets demonstrate that CI-SNF scheme outperforms state-of-the-art similarity fusion algorithms including SNF in all four tasks. It is also verified that utilizing the contextual information contained in the SNF-based similarity network helps to enhance the performance of the SNF-based scheme, further.
Paired 3D Model Generation with Conditional Generative Adversarial Networks

Generative Adversarial Networks (GANs) are shown to be successful at generating new and realistic samples including 3D object models. Conditional GAN, a variant of GANs, allows generating samples in given conditions. However, objects generated for each condition are different and it does not allow generation of the same object in different conditions. In this paper, we first adapt conditional GAN, which is originally designed for 2D image generation, to the problem of generating 3D models in different rotations. We then propose a new approach to guide the network to generate the same 3D sample in different and controllable rotation angles (sample pairs). Unlike previous studies, the proposed method does not require modification of the standard conditional GAN architecture and it can be integrated into the training step of any conditional GAN. Experimental results and visual comparison of 3D models show that the proposed method is successful at generating model pairs in different conditions.
An Adaptive Spectral Decorrelation Method for Lossless MODIS Image Compression

Spectral decorrelation has been considered an important approach in multispectral image compression to remove the redundancy among bands. In this paper, we propose a novel adaptive spectral decorrelation method based on clustering analysis for Moderate Resolution Imaging Spectroradiometer (MODIS) image compression. The remote sensing image bands are divided into different clusters by the method based on density peak clustering. Then, reversible Karhunen-Loeve transform and polynomial least square estimation are employed to reduce the band redundancy, achieving an effective spectral decorrelation. As shown by the experimental results, our method achieves remarkable bit-saving when compared to the state-of-the-art algorithms on MODIS image data set.
First steps toward CNN based source classification of document images shared over messaging app

Knowledge of source smartphone corresponding to a document image can be helpful in a variety of applications including copyright infringement, ownership attribution, leak identification, and usage restriction. In this work, we investigate a convolutional neural network-based approach to solve source smartphone identification problem for printed text documents which have been captured by smartphone cameras and shared over messaging platform. The proposed method comprises of a fusion technique which allows a single network to learn a model directly from two-channel images fused out of native letter images and their denoised versions. In the absence of any publicly available dataset addressing this problem, we introduce a new image dataset consisting of 770 images of documents printed in three different fonts, captured using 22 smartphones and shared over WhatsApp. A series of experiments are conducted on the newly captured dataset including an experiment in the presence of an active adversary who might re-scale the native images before sharing over WhatsApp. In all the experiments, for classification of WhatsApp-processed document images, the proposed method outperforms the baseline methods.
A Semi-supervised Data Augmentation Approach Using 3D Graphical Engines

Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. small data), such as human pose and behavior estimation/tracking which could be highly personalized. In this paper, we present a semi-supervised data augmentation approach that can synthesize large scale labeled training datasets using 3D graphical engines based on a physically-valid low dimensional pose descriptor. To evaluate the performance of our synthesized datasets in training deep learning-based models, we generated a large synthetic human pose dataset, called ScanAva using 3D scans of only 7 individuals based on our proposed augmentation approach. A state-of-the-art human pose estimation deep learning model then was trained from scratch using our ScanAva dataset and could achieve the pose estimation accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain adaptation on the synthetic images, in which its pose estimation accuracy was comparable to the same model trained on large scale pose data from real humans such as MPII dataset and much higher than the model trained on other synthetic human dataset such as SURREAL.
Community-Centric Brokerage-Aware Access Control for Online Social Networks

Widespread use and global outreach of Online Social Networks (OSNs) has made it indispensable for everyone to be a part of this global community. Users of OSNs want to share and disseminate some information to the general public, on the other hand, they also want to keep certain information hidden or restricted to a few people. An OSN allows users to join different groups based on their interests, however, some irrelevant people may also join these groups. This brings forth the challenge of privacy to OSNs. Therefore, an effective access control system is highly desirable that can manage the access control of incoming and outgoing users of a group in OSNs. This study deals with privacy issues of online communities and aims to support access control in community-based OSNs. We propose a Community-Centric Brokerage-Aware Access Control (CBAC) model that utilizes important concepts from Social Network Analysis (SNA) which are brokerage, one-to-many relationships, temporary relationships, as well as from emerging access control models such as attribute-based and trust-based access control along with decentralized policies. User attributes can play a vital role in detecting communities in a graph. For community detection, we propose Attribute-Based Community Detection (ABCD) algorithm and compare its results with Louvain, Newman’s Eigenvector, and Clauset algorithms. The results of the ABCD algorithm outperform other methods in terms of modularity and the number of communities for large and dense networks. The contribution of this study is to propose a CBAC model, its mathematical modeling and the ABCD algorithm for community detection. The performance of the CBAC model was evaluated in terms of grants, denials, and response time. The performance of the proposed the CBAC model is compared with the Role-based Access Control (RBAC) model and the Team-based Access Control (TMAC) model. The CBAC model offers the best data sharing capabilities and provides comparatively reasonable response time.
Broadband reflectance, emittance spectroscopy and self-potential geophysical survey for targeting gold sulphide lode deposit in Bhukia, Rajasthan, India

In this study, we have analyzed the comparative potential of reflectance and emittance bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor for detecting surface exposures of Proterozoic rocks and gossan capping of gold mineralization. In this regard, few new image analysis products are derived. These are matched filtering factor-modified ratio (MFR) image and the inverse of a difference image of the ratio and matched filtering map (IDRM). These image products are used with the conventional ratio images, relative band depth images and index-based principal component (PC) images of reflectance and emittance bands of ASTER for delineating metasediments of the study area. We have found that reflectance band products are superior for mapping metasediments and gossan capping than the emissivity products, which are only suitable to enhance quartzite. Self-potential (SP) geophysical survey is carried out around the identified gossan site as SP is sensitive to sulphide mineralization. Results of the SP survey indicated presence of sulphide enrichment underneath the gossan.
Generating robust entanglement via quantum feedback

Generating entangled states is one of the most important tasks in quantum information technology. However, in reality any entanglement generator must contain some characteristic uncertainty, and as a result the produced entangled state becomes an undesirable mixed state. This paper develops a coherent feedback control scheme that suppresses the characteristic uncertainty of a typical entanglement generator (non-degenerate optical parametric oscillator) for producing robust Gaussian entangled states. In particular, we examine a two-mode squeezed state and Gaussian four-mode cluster states to demonstrate the effectiveness of the proposed control method.
Meet‐in‐the‐middle attacks on round‐reduced tweakable block cipher Deoxys‐BC

Deoxys-BC is a tweakable block cipher designed by Jean et al. at ASIACRYPT 2014 within the TWEAKEY framework. Then Deoxys-BC is used in the CAESAR finalist Deoxys. In this study, the authors consider the security of Deoxys-BC against meet-in-the-middle attack in the single-key setting. Using the idea that a chosen tweak difference allows to cancel a difference in the state, they can construct 5-round meet-in-the-middle distinguisher on Deoxys-BC-128-128 which can be extended to attack on 8-round Deoxys-BC-128-128. Moreover, they construct 6-round meet-in-the-middle distinguisher on Deoxys-BC-256-128 which can be extended to attack on 10-round Deoxys-BC-256-128. As far as the authors know, these are the best attacks against Deoxys-BC in the single-key setting.
Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.
Simple, analytic solutions of the semiclassical Rabi model

We considered the semiclassical Rabi model, that is, a qubit (transition frequency ω0) interacting with a single-mode, classical field (frequency ω1). Using the method of multiple-scales we obtained simple, approximate, and analytic solutions in the resonant ω0=ω1 and blue detuned ω0<ω1 cases that hold for long times in the weak and strong coupling regimes. In the blue detuned case, the solutions hold for arbitrary qubit-field couplings. Moreover, the solutions are used to determine the motion of the Bloch vector and to give a geometric interpretation. Finally, we discuss applications of the results to physical systems and we propose a very simple experiment in atomic physics where the breakdown of the rotating-wave-approximation can be observed and where the results can be applied.
Identifying motion pathways in highly crowded scenes: A non-parametric tracklet clustering approach

Many approaches that address the analysis of crowded scenes rely on using short trajectory fragments, also known as tracklets, of moving objects to identify motion pathways. Typically, such approaches aim at defining meaningful relationships among tracklets. However, defining these relationships and incorporating them in a crowded scene analysis framework is a challenge. In this article, we introduce a robust approach to identifying motion pathways based on tracklet clustering. We formulate a novel measure, inspired by line geometry, to capture the pairwise similarities between tracklets. For tracklet clustering, the recent distance dependent Chinese restaurant process (DD-CRP) model is adapted to use the estimated pairwise tracklet similarities. The motion pathways are identified based on two hierarchical levels of DD-CRP clustering such that the output clusters correspond to the pathways of moving objects in the crowded scene. Moreover, we extend our DD-CRP clustering adaptation to incorporate the source and sink gate probabilities for each tracklet as a high-level semantic prior for improving clustering performance. For qualitative evaluation, we propose a robust pathway matching metric, based on the chi-square distance, that accounts for both spatial coverage and motion orientation in the matched pathways. Our experimental evaluation on multiple crowded scene datasets, principally, the challenging Grand Central Station dataset, demonstrates the state-of-the-art performance of our approach. Finally, we demonstrate the task of motion abnormality detection, both at the tracklet and frame levels, against the normal motion patterns encountered in the motion pathways identified by our method, with competent quantitative performance on multiple datasets.
A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression; but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.
Average-face-based virtual inpainting for severely damaged statues of Dazu Rock Carvings

Numerous image inpainting algorithms are guided by a basic assumption that the known region in the original image itself can provide sufficient prior information for the guess recovery of the unknown part, which is not often the case in actual art image inpainting. In order to solve the challenging inpainting case that there is little image prior in the remainder of the original image, we propose an average-face-based inpainting method based on a sample database with 3 steps: reference images selection, average image generation and exemplar-based image inpainting. In which, average image generation is crucial. In the inpainting framework, the average image can be directly viewed as an inpainting proposal for the severely damaged or absolutely lost image. Moreover, the average image can be applied to exemplar-based inpainting algorithm as a sample image to extend the searching region for match patch, so as to perform the restoration for images with large-scale or irregularly damaged holes. The inpainting experiments over some facial images of Dazu Rock Carvings demonstrate the validity and effectiveness of our method. It is first utilized for two extremely challenging inpainting tasks: reconstruction for the stolen head of Willow Avalokitesvara in Shimenshan No. 6 and the absolutely broken heads of two Avalokitesvaras in Beishan No. 180. Compared to the failure of the exemplar-based inpainting algorithm within the original image and the directly duplication of a similar image, the generated average image can be as a more reliable inpainting proposal. The comparative experiments also show the efficiency and advantage of the average face applied to exemplar-based inpainting framework. Compared with some related inpainting algorithms, our method is more competitive when there is little prior information in the original image. The efficient virtual inpainting results are valuable references for both cave art historians and conservators.
Research on the fixation transition behavior of drivers on expressway in foggy environment

To study the fixation behavior of drivers and the characteristics of the fixation transition in the path-finding process in a foggy environment, a natural driving behavior test method and noncontact eye tracker were used to conduct a real vehicle test and obtain the fixation distribution data of drivers. Dynamic and density clustering methods were used to analyze the distribution characteristics of a driver’s fixation area and to draw the zone of interest in a foggy environment. By using a Markov chain to analyze the driver’s fixation point transition, the driver’s fixation transfer probability matrix was constructed, and the driver’s fixation transition state was obtained. When the driver was driving in an environment with low visibility, the focus of his attention was concentrated in the nearby area, and a lower visibility resulted in a more focused visual search range. When the visibility was below 50 m, the driver’s visual search focus increased by 23.6%.
Particle learning for Bayesian semi-parametric stochastic volatility model

This article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.