Reviewerly/exHarmony huggingface dataset:
Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

To be effective, robots will need to reliably operate in scenes with refractive objects in a variety of applications; however, refractive objects can cause many robotic vision algorithms, such as structure from motion, to become unreliable or even fail. We propose a novel method to distinguish between refracted and Lambertian image features using a light field camera. While previous refracted feature detection methods are limited to light field cameras with large baselines relative to the refractive object, our method achieves comparable performance, and we extend these capabilities to light field cameras with much smaller baselines than previously considered, where we achieve up to 50% higher refracted feature detection rates. Specifically, we propose to use textural cross correlation to characterize apparent feature motion in a single light field, and compare this motion to its Lambertian equivalent based on 4-D light field geometry. For structure from motion, we demonstrate that rejecting refracted features using our distinguisher yields lower reprojection error, lower failure rates, and more accurate pose estimates when the robot is approaching refractive objects. Our method is a critical step toward allowing robots to operate in the presence of refractive objects.
Live Migration in Bare-Metal Clouds

Live migration allows a running operating system (OS) to be moved to another physical machine with negligible downtime. Unfortunately, live migration is not supported in bare-metal clouds, which lease physical machines rather than virtual machines to offer maximum hardware performance. Since bare-metal clouds have no virtualization software, implementing live migration is difficult. Previous studies have proposed OS-level live migration; however, to prevent user intervention and broaden OS choices, live migration should be OS-independent. In addition, the overhead of live migration mechanisms should be as low as possible. This paper introduces BLMVisor, a live migration scheme for bare-metal clouds. To achieve OS-independent and lightweight live migration, BLMVisor utilizes a very thin hypervisor that exposes physical hardware devices to the guest OS directly rather than virtualizing the devices. The hypervisor captures, transfers, and reconstructs physical device states by monitoring access from the guest OS and controlling the physical devices with effective techniques. To minimize performance degradation, the hypervisor is mostly idle after completing the live migration. A performance evaluation confirmed that the OS performance with BLMVisor is comparable to that of a bare-metal machine.
Dynamic texture analysis with diffusion in networks

Dynamic texture is a field of research that has gained considerable interest from computer vision community due to the explosive growth of multimedia databases. In addition, dynamic texture is present in a wide range of videos, which makes it very important in expert systems based on videos such as medical systems, traffic monitoring systems, forest fire detection system, among others. In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed. The dynamic texture is modeled as a directed network. The method consists in the analysis of the dynamic of this network after a series of graph cut transformations based on the edge weights. For each network transformation, the activity for each vertex is estimated. The activity is the relative frequency that one vertex is visited by random walks in balance. Then, texture descriptor is constructed by concatenating the activity histograms. The main contributions of this paper are the use of directed network modeling and diffusion in network to dynamic texture characterization. These tend to provide better performance in dynamic textures classification. Experiments with rotation and interference of the motion pattern were conducted in order to demonstrate the robustness of the method. The proposed approach is compared to other dynamic texture methods on two very well known dynamic texture database and on traffic condition classification, and outperform in most of the cases.
Lattice-based revocable (hierarchical) IBE with decryption key exposure resistance

Revocable identity-based encryption (RIBE) is an extension of IBE that supports a key revocation mechanism, which is an indispensable feature for practical cryptographic schemes. Due to this extra feature, RIBE is often required to satisfy a strong security notion unique to the revocation setting called decryption key exposure resistance (DKER). Additionally, hierarchal IBE (HIBE) is another orthogonal extension of IBE that supports key delegation functionalities allowing for scalable deployments of cryptographic schemes. So far, R(H)IBE constructions with DKER are only known from bilinear maps, where all constructions rely heavily on the so-called key re-randomization property to achieve the DKER and/or hierarchal feature. Since lattice-based schemes seem to be inherently ill-fit with the key re-randomization property, no construction of lattice-based R(H)IBE schemes with DKER are known. In this paper, we propose the first lattice-based RHIBE scheme with DKER without relying on the key re-randomization property, departing from all the previously known methods. We start our work by providing a generic construction of RIBE schemes with DKER, which uses as building blocks any two-level standard HIBE scheme and (weak) RIBE scheme without DKER. Based on previous lattice-based RIBE constructions without DKER, our result implies the first lattice-based RIBE scheme with DKER. Then, building on top of our generic construction, we construct the first lattice-based RHIBE scheme with DKER, by further exploiting the algebraic structure of lattices. To this end, we prepare a new tool called the level conversion keys, which enables us to achieve the hierarchal feature without relying on the key re-randomization property. In this full version, we give the formal proofs of our proposed schemes.
Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction

A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications.In this work, we propose a post-processing framework to improve foreground segmentation performance of background subtraction algorithms.We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground.Firstly, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions.Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions.Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully-connected Conditional Random Field.Experiments are conducted over four large and heterogeneous datasets with varied challenges (CDNET2014, LASIESTA, SABS and BMC) demonstrating the capability of the proposed framework to improve background subtraction results.
Background Subtraction Based on Integration of Alternative Cues in Freely Moving Camera

Previous approaches to background subtraction in freely moving camera typically focus on improving the accuracy of motion estimation. In this paper, we propose that the accurate background subtraction is possible with the integration of alternative cues about foreground and background. We also put forward a novel background subtraction framework called the integration of foreground and background cues. Here, the foreground cues are extracted by the Gaussian mixture model compensated with image alignment, while the background cues are obtained from the spatiotemporal features filtered by the homography transformation. Subsequently, the integration is devised as a hierarchical competition procedure based on super-pixels under multiple levels with the underlying motivation to utilize the exclusiveness between these cues for the compensation of their corresponding defects. The result of competition between foreground and background cues in a particular super-pixel is used as the proximity, and the foreground is segmented by combining super-pixels with proximity under multiple levels. Comprehensive evaluations using standard benchmarks demonstrate the superiority of our work compared with the state-of-the-art.
Image De-Hazing Via Gradient Optimized Adaptive Forward-Reverse Flow-Based Partial Differential Equation

In this study, we propose a modified partial differential equation (PDE)-based algorithm for image de-hazing. The algorithm possesses relatively low computational complexity and the core function of the PDE is easily amenable to hardware implementation. New contributions include the optimization and automated processing for dark and hazy images, avoiding manual parameter tuning. Additionally, the regularization parameter is computed adaptively from the binary mask of the input image. This is combined with a gradient-based metric for optimization to automatically determine stopping time of the algorithm for both types of images. The proposed scheme is fast and utilizes spatial or frequency domain filters to achieve illumination and reflectance component estimation without resorting to logarithms. Moreover, there is absence of halos in de-hazed images compared to previous work. Extensive experiments indicate that the proposed approach yields results comparable to or better than several works from the literature.
Solving Square Jigsaw Puzzle by Hierarchical Loop Constraints

We present a novel computational puzzle solver for square-piece image jigsaw puzzles with no prior information such as piece orientation or anchor pieces. By "piece" we mean a square $d$d x $d$d block of pixels, where we investigate pieces as small as 7 × 7 pixels. To reconstruct such challenging puzzles, we propose to find maximum geometric consensus between pieces, specifically hierarchical piece loops. The proposed algorithm seeks out loops of four pieces and aggregates the smaller loops into higher order "loops of loops" in a bottom-up fashion. In contrast to previous puzzle solvers which aim to maximize compatibility measures between all pairs of pieces and thus depend heavily on the pairwise compatibility measures used, our approach reduces the dependency on the pairwise compatibility measures which become increasingly uninformative for small scales and instead exploits geometric agreement among pieces. Our contribution also includes an improved pairwise compatibility measure which exploits directional derivative information along adjoining boundaries of the pieces. We verify the proposed algorithm as well as its individual components with mathematical analysis and reconstruction experiments.
Depth image super-resolution based on joint sparse coding

This paper proposes a new approach to single depth image super-resolution (SR), based upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR process. Firstly, we introduce synthetic characteristic image patch to learn a joint dictionary from the low-resolution depth map as well as its corresponding low-resolution intensity image. Then, we derive the joint nonlocal center sparse representation model based on sparse coding and theoretical analysis. In reconstruction process, we use Bayesian interpretation approach to estimation the sparse code coefficients for each unknown HR image patch. Meanwhile, we use an iterative algorithm to solve the JSC model. In addition, we exploit image patch redundancy within and across different scales, produce visually pleasing results without extensive training on external database. Experimental results demonstrate that the proposed method outperforms favorably many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality and underpin the validity of our proposed model.
An improved technique for increasing availability in Big Data replication

Big Data represents a major challenge for the performance of the cloud computing storage systems. Some distributed file systems (DFS) are widely used to store big data, such as Hadoop Distributed File System (HDFS), Google File System (GFS) and others. These DFS replicate and store data as multiple copies to provide availability and reliability, but they increase storage and resources consumption. In a previous work (Kaseb, Khafagy, Ali, & Saad, 2018), we built a Redundant Independent Files (RIF) system over a cloud provider (CP), called CPRIF, which provides HDFS without replica, to improve the overall performance through reducing storage space, resources consumption, operational costs and improved the writing and reading performance. However, RIF suffers from limited availability, limited reliability and increased data recovery time. In this paper, we overcome the limitations of the RIF system by giving more chances to recover a lost block (availability) and the ability of the system to keep working the presence of a lost block (reliability) with less computation (time overhead). As well as keeping the benefits of storage and resources consumption attained by RIF compared to other systems. We call this technique "High Availability Redundant Independent Files" (HARIF), which is built over CP; called CPHARIF. According to the experimental results of the HARIF system using the TeraGen benchmark, it is found that the execution time of recovering data, availability and reliability using HARIF have been improved as compared with RIF. Also, the stored data size and resources consumption with HARIF system is reduced compared to the other systems. The Big Data storage is saved and the data writing and reading are improved.
On the weight distribution of random binary linear codes

We investigate the weight distribution of random binary linear codes. For 0 &lt; λ &lt; 1 and n → ∞ pick uniformly at random λn vectors in and let be the orthogonal complement of their span. Given 0 &lt; γ &lt; 1/2 with 0 &lt; λ &lt; h ( γ ) let X be the random variable that counts the number of words in C of Hamming weight γn . In this paper we determine the asymptotics of the moments of X of all orders .
A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection

In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or "normal" behavior.In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks.More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process.Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations.We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.
SML-Bench – A benchmarking framework for structured machine learning

The availability of structured data has increased significantly over the past decade and several approaches to learn from structured data have been proposed. These logic-based, inductive learning methods are often conceptually similar, which would allow a comparison among them even if they stem fro m different research communities. However, so far no efforts were made to define an environment for running learning tasks on a variety of tools, covering multiple knowledge representation languages. With SML-Bench, we propose a benchmarking framework to run inductive learning tools from the ILP and semantic web communities on a selection of learning problems. In this paper, we present the foundations of SML-Bench, discuss the systematic selection of benchmarking datasets and learning problems, and showcase an actual benchmark run on the currently supported tools.
Joint deep semantic embedding and metric learning for person re-identification

We focus on the person re-identification (re-id) task, whose goal is to automatically re-identify individual persons from multiple non-overlapping cameras or the same camera across time. While most existing works rely on exploring properties of the visual data, we consider taking advantage of both visual and textual representations. Given images and natural language descriptions of the persons in the probe, the re-id system is required to rank all the samples in the gallery set. We embed the visual representations and textual descriptions in a unified space, in which we map similar examples close to each other and map dissimilar examples farther apart. Our premise is that, in general, strong semantic correlations exist between different persons. The space casts a person in gallery set as a combination of the persons in probe set. The model is trained in an end-to-end fashion. We conduct extensive experiments on the challenging i-LIDS, PRID-2011, CUHK03 and Market-1501 datasets, and confirm that the proposed model achieves state-of-the-art performances.
Mellin polar coordinate moment and its affine invariance

The moment-based method is a fundamental approach to the extraction of affine invariants. However, only integer-order traditional moments can be used to construct affine invariants. No invariants can be constructed by moments with an order lower than 2. Consequently, the obtained invariants are sensitive to noise. In this paper, the moment order is generalized from integer to non-integer. However, the moment order cannot simply be generalized from integer to non-integer to achieve affine invariance. The difficulty of this generalization lies in the fact that the angular factor owing to shearing in the affine transform can hardly be eliminated for non-integer order moments. In order to address this problem, the Mellin polar coordinate moment (MPCM) is proposed, which is directly defined by a repeated integral. The angular factor can easily be eliminated by appropriately selecting a repeated integral. A method is provided for constructing affine invariants by means of MPCMs. The traditional affine moment invariants (AMIs) can be derived in terms of the proposed MPCM. Furthermore, affine invariants constructed with real-order (lower than 2) MPCMs can be derived using the proposed method. These invariants may be more robust to noise than AMIs. Several experiments were conducted to evaluate the proposed method performance.
iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimization

Employing effective optimization strategies in organizations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organizations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilized is a challenging problem as there are many factors that can impact the overall performance of the organization. We have developed a system that optimizes to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimization problems, which means there are many objectives to consider when optimizing, and there is much uncertainty in the environment. The latest version of the system uses a multiobjective genetic algorithm as its core optimization logic, with modifications such as fuzzy dominance rules (FDRs), to help overcome the issues associated with many-objective optimization. The system also utilizes genetically optimized type-2 fuzzy logic systems to better handle the uncertainty in the data and modeling. This paper shows the genetically optimized type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1 million a year, the reduction of over 2500 t of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.
Making Effective Use of Healthcare Data Using Data-to-Text Technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs, and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial, and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover, and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports, or via e-health platforms by their doctors. Unfortunately, such text is the outcome of a highly labor-intensive process if it is done by healthcare professionals. It is also prone to incompleteness and subjectivity and hard to scale up to different domains, wider audiences, and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.