Reviewerly/exHarmony huggingface dataset:
Canonical Correlation Analysis Feature Fusion With Patch of Interest: A Dynamic Local Feature Matching for Face Sketch Image Retrieval

An automatic photo retrieval system based on a face sketch has very useful application as to narrow down potential suspects in criminal investigations. This is true when there is no other evident except the face sketch that is rendered based on the recollection of a victim or eyewitness. Among the noticeable difficulties in matching the sketch and photo due to its modality difference are the generated sketch has some tendency of shape exaggeration, the sketch has very less accurate details and the real-world photo may expose to lighting variation unlike the sketch. In this paper, we attempt to address these complications by matching the sketch and photos using dynamic local feature of Difference of Gaussian Oriented Gradient Histogram (DoGOGH) on some selected patches. To avoid discriminative power degradation due to a large number of gallery images, two stage matching blocks are introduced in a cascaded fashion. The front block matches the feature such that it short lists k most similar photos for the second block. In this front block, Histogram of Oriented Gradient (HOG) and Gabor Wavelet (GW) features are fused by maximizing the correlation between the two using Canonical Correlation Analysis (CCA). Based on the short listed photos, the following block re-matched the sketch and photos using dynamically extracted local feature on its Patch of Interest (PoI). Eventually, the matching scores from the blocks are fused before getting rank-1 accuracy. The experimental results on two baseline datasets indicate that the proposed method outperforms the state-of-the-art methods. The extended evaluation on semi-forensic and forensic sketch datasets demonstrate its usage feasibility.
VM Performance Maximization and PM Load Balancing Virtual Machine Placement in Cloud

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users' and cloud providers' perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.
An Efficient Scheme of Digital Image Watermarking Based on Hessenberg Factorization and DWT

Nowadays, the widespread availability of digital images manipulating software leads to the easy potential for editing images with no need for professional knowledge. Generally, the digital images tampering is very subtle in order that it is not distinguishable by the system of human vision. Most of the attackers work on exploiting and misusing this property. Therefore, for preventing illegal manipulation and unlawful access for any digital image, digital images watermarking schemes are developed to hide an imperceptible watermark into a cover image. Based on the utilized application, the significant requirements for these watermarking schemes are security, capacity, robustness, and imperceptibility. In order to satisfy most of these requirements, in this paper, an efficient digital image watermarking scheme has been proposed that relies on Hessenberg Matrix Decomposition (HMD) applied to each block produced from transforming the cover image by discrete wavelet transform (DWT). In the experiments, the obtained results show that the proposed scheme has a high performance in providing the significant requirements of watermarking; security, capacity, robustness, and imperceptibility.
On planes through points off the twisted cubic in PG(3,q) and multiple covering codes

Let PG(3,q) be the projective space of dimension three over the finite field with q elements. Consider a twisted cubic in PG(3,q). The structure of the point-plane incidence matrix in PG(3,q) with respect to the orbits of points and planes under the action of the stabilizer group of the twisted cubic is described. This information is used to view generalized doubly-extended Reed-Solomon codes of codimension four as asymptotically optimal multiple covering codes.
The extensions of convergence rates of Kaczmarz-type methods

Kaczmarz-type methods, such as the randomized Kaczmarz method, the block Kaczmarz method and the Cimmino method, can be derived from the Kaczmarz method. In this paper, we introduce a new error term ‖xk−PN(A)x0−x†‖2 for Kaczmarz-type methods, where x† is the generalized solution of Ax=b and PN(A)x0 is the orthogonal projection of a given initial value x0 onto the null space N(A). It includes the well-known error term ‖xk−x∗‖2 as a special case when x0=0 and x†=x∗, where x∗ is a true solution of Ax=b. We investigate the behavior of the new error term and establish the corresponding convergence rates for Kaczmarz-type methods. Especially, from the estimate of new error term for the Kaczmarz method, we can get a more simple proof for the convergence of the Kaczmarz method.
Low-Density Lidar Based Estimation System for Bicycle Protection

This paper focuses on the development of a system to detect if a nearby car poses a collision danger to a bicycle, and to sound a loud horn to alert the car driver if a collision danger is detected. A sensing and estimation system suitable for use on a bicycle is therefore developed in order to track the trajectories of vehicles in a traffic intersection. An inexpensive solid-state low-density Lidar mounted at the front of an instrumented bicycle is used. The low angular resolution of the sensor creates many challenges. These challenges are addressed in this research by clustering based approaches for assigning measurement points to individual vehicles, by introducing a correction term with its own dynamic model for position measurement refinement, and by incorporating multi-target tracking using global nearest neighbor data association and interacting multiple model extended Kalman filtering. The tracking performance of the developed system is evaluated by both simulation and experimental results. Scenarios that involve straight driving (in all four directions) and left turning (opposing) vehicles at a traffic intersection are considered. Experimental results show that the developed system can successfully track cars accurately in these scenarios in spite of the low measurement resolution of the sensor.
Roundness-Preserving Warping for Aesthetic Enhancement-Based Stereoscopic Image Editing

Image editing is an effective solution to adapt contents for different applications. In this paper, we present a roundness-preserving warping model for stereoscopic image editing, in which energy constraints from image quality energy, aesthetics energy and depth adaptation energy are involved in the framework to solve the optimization. Specifically, to preserve object roundness during warping, the relationship between object's shape and disparity is established and is applied for depth adaptation. Different from the existing stereoscopic image editing methods, the main innovations of our method are to achieve a tradeoff in balancing information loss and reducing semantic distortion while providing a novel death adaptation model for recomposition and retargeting applications. Experimental results demonstrate the effectiveness of our method in enhancing the aesthetics of stereoscopic images.
A systematic evaluation of filter Unsupervised Feature Selection methods

Unsupervised Feature Selection (UFS) has aroused great interest in the last years because of its practical significance and application on a large variety of problems in expert and intelligent systems where unlabeled data appear. Specifically, Unsupervised Feature Selection methods based on the filter approach have received more attention due to their efficiency, scalability, and simplicity. However, in the literature, there are no comprehensive studies for assessing such UFS methods when they are applied, under the same conditions, to a wide variety of real-world data. To fill this gap, in this paper, we present a comprehensive empirical and systematic evaluation of the most popular and recent filter UFS methods, evaluating their performance in terms of clustering, classification, and runtime. The filter methods used in our study were applied on 50 datasets from the UCI Machine Learning Repository and 25 high dimensional datasets from the ASU Feature Selection Repository. To evaluate if the outcomes obtained by the assessed methods are statistically significant, the Friedman test and Holm post hoc procedure were applied in the clustering and classification results. From our experiments, we provide some practical guidelines and insights for the use of the filter UFS methods analyzed in our study.
Single-Filter Finite Fault Detection and Exclusion Methodology for Real-Time Validation of Plug-and-Play Sensors

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with well-understood measurement models, none of which are guaranteed for all-source systems. As part of an overall all-source assured or resilient navigation objective, this research contributes a key component-validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The proposed algorithm combines a residual-based test statistic with the partial update formulation of the Kalman-Schmidt filter to provide a reliable method for sensor model validation that protects the integrity of the navigation solution during the validation process, all using only a single existing filter. The performance of the proposed method is validated against traditional fault detection and exclusion methods (such as normalized solution separation and conventional residual sequence monitoring) using Monte-Carlo simulations in a 2D non-Global Positioning System navigation problem with a plug-and-play position sensor.
Voronoi Boundary Visibility for Efficient Path Planning

The subject of this paper is the computation of paths for mobile robots that navigate from a start position to a goal position in environments with static obstacles. Specifically, we focus on paths that are represented by straight lines. Such paths can for example directly be followed by omni-directional robots or can be used as an initial solution for path smoothing. In this context, the most common performance metrics are the path length, the obstacle clearance and the computation time. In this paper, we develop a new path planning algorithm that addresses all the stated performance metrics. Our method first determines all possible connections between the start position and goal position along the edges of the generalized Voronoi diagram (GVD) of a given obstacle map. The shortest connections are then refined using a balanced method for creating shortcuts along existing waypoints and introducing new waypoints in order to cut corners. As an important feature, our method reduces the number of required waypoints by iteratively adding new waypoints and then removing unnecessary waypoints along solution paths. Moreover, our method takes into account multiple start-goal connections, since the shortest start-goal connection along the edges of the GVD might not lead to the shortest solution path. A comprehensive computational evaluation for a large number of maps with different properties shows that the proposed method outperforms sampling-based algorithms such as Probabilistic Roadmaps (PRM) and exact methods such as Visibility Graphs (VG) by computing close-to-optimal solution paths with a specified minimum obstacle clearance in less time.
A Resource-Efficient Inference Accelerator for Binary Convolutional Neural Networks

This brief presents a novel architecture to implement a resource-efficient inference accelerator for binary convolutional neural networks (BCNN). The proposed architecture consistently processes each constituent block of a network in an output-oriented manner. It skips the redundant operations that are involved with the elements within a pooling window after the pooling result is determined as well as the operations with respect to the padded zeros. A BCNN inference accelerator has been implemented based on the proposed architecture using an FPGA. The resource efficiency is as high as 41.45M-OP/s/LUT in the CIFAR-10 classification task. The functionality of the proposed accelerator has been verified by implementing a fully-integrated BCNN inference system including an MCU.
Visual Exploration at Higher Fixation Frequency Increases Subsequent Memory Recall

Only a small proportion of what we see can later be recalled. Up to date it is unknown how far differences in visual exploration during encoding affect the strength of episodic memories. Here, we identified individual gaze characteristics by analyzing eye tracking data in a picture encoding task performed by 967 healthy subjects during fMRI. We found a positive correlation between fixation frequency during visual exploration and subsequent free recall performance. Brain imaging results showed a positive correlation of fixation frequency with activations in regions related to vision and memory, including the medial temporal lobe. To investigate if higher fixation frequency is causally linked to better memory, we experimentally manipulated visual exploration patterns in an independent population of 64 subjects. Doubling the number of fixations within a given exploration time increased subsequent free recall performance by 19%. Our findings provide evidence for a causal relationship between fixation frequency and episodic memory for visual information.
Automating QUIC Interoperability Testing

We present QuicInteropRunner (QIR) [1, 2], a test framework for automated and on-demand interoperability testing between implementations of the QUIC protocol [3]. QIR is a framework in which QUIC clients and servers interact with each other over a network that simulates various network conditions using ns-3 [4]. QIR automates QUIC interoperability testing by running a suite of test cases between containerized QUIC implementations. We describe the key constraints and insights that defined our work, recent innovations that made the framework possible, a high-level overview of our design, and a few exemplary tests. QIR is now supported and used by ten QUIC implementations as part of their development process, confirming our thesis that there is a need for automating interoperability testing and making it available on demand.
Start to End: Recommended Travel Routes Based on Tourist Preference

Abstract When going on a tour, one of the things to consider is making an itinerary before traveling in accordance to the time limit required by tourists. This application will help tourists plan a tour that suits them. In making this system, Greedy Algorithm is used to help optimizing process of searching the shortest travel time and to find the number of tourism site in the route chosen for the tourists. Tourism site points for start to end can also be determined and specified by tourists. The data used was gathered directly through observations in Yogyakarta, specifically Bantul and Sleman. The result is an application for guiding or routing tourism objects from one object to another from start to end and in between. These travel routes are shown through google maps with other information such as distance and travel time from one tourist attraction to other tourist attractions.
Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images

With an advancement in biomedical applications, many images are communicated over the public networks. Therefore, these medical images are prone to various security threats. Development of end to end secure communication protocol for these medical images is found to be a challenging task. Therefore, many researchers have proposed various image medica image encryption techniques to provide end to end security of medical images. However, the existing approaches of block-based recovery of the secret through progressive sharing paradigm have support for limited threshold value of the chosen blocks out of the total number of the blocks during the communication of the image. Most of the suggested scheme has fixed threshold value for the blocks during recovery of secret; works good for a limited threshold (k) value out of number of blocks (n) in which secret has been divided for security. A novel threshold based (any value of k and n) blockwide recovery of secret in progressive secret sharing has been introduced and analyzed for distributed environment. The proposed threshold block wise splitting using progressive visual secret sharing (T-BPVSS) achieves any general higher value of threshold for recovery of secret medical images. Proposed scheme is tested based on various parameters such as varying values of threshold for recovery of secret during enhanced security scenario, as well as changing dimensions of the images and introducing noise in the images. A detailed distributed computing recovery solution is also suggested for the original secret by using distribution technique of shares across the networks of computers. The scheme satisfies for perfect security condition in distributed environment using at least minimum decided threshold numbers of participants (k) before revealing any of the blocks of secret medical image.
One-Dimensional Pseudo-Chaotic Sequences Based on the Discrete Arnold’s Cat Map Over ℤ₃<i>ᵐ</i>

In this brief we employ the discrete Arnold’s cat map over the integer ring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{3^{m}}$ </tex-math></inline-formula> to construct one-dimensional pseudo-chaotic sequences. We analyze their period properties using the properties of the Fibonacci sequence over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{3^{m}}$ </tex-math></inline-formula> and show that they have twice the period of the sequences generated by the logistic map over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{3^{m}}$ </tex-math></inline-formula> recently proposed. Moreover, we investigate the pseudo-chaotic properties of the proposed sequences in the context of pseudo-chaos. Finally, these sequences are employed to design a pseudo-random number generator and a statistical analysis with the NIST statistical test suite is performed.
Shall we follow? Impact of reputation concern on information security managers’ investment decisions

Information security (infosec) is important for organizations. While budgeting for infosec is a crucial resource allocation decision, infosec managers may choose to follow other fellow experts' recommendations or baseline practices. The present paper uses reputational herding theory to explain the decision made by infosec managers to use a "let's follow others" strategy in this context. Based on a sample of 106 organizations in Finland, we find that infosec managers' ability to accurately predict the benefit of infosec investment, as well as their reputations, have significant effects on motivating them to discount their own information. Infosec managers' discounting of their own information, together with the strength of information that relates to infosec investment and mandatory requirements, motivates infosec investment. Our empirical results highlight the "let's follow others" strategy as an important alternative to cost–benefit analysis in terms of budgeting for infosec investment.
Some Practical Considerations for Compression Failure Characterization of Open-Cell Polyurethane Foams Using Digital Image Correlation

(1) Background: Open-cell polyurethane foam mechanical behavior is highly influenced by microstructure. The determination of the failure mechanisms and the characterization of the deformation modes involved at the micro scale is relevant for accurate failure modeling. (2) Methods: We use digital image correlation (DIC) to investigate strain fields of open-cell polyurethane foams of three different densities submitted to compression testing. We analyze the effect of some DIC parameters on the failure pattern definition and the equivalent strain magnification at the apparent ultimate point. Moreover, we explore speckle versus non-speckle approaches and discuss the importance of determining the pattern quality to perform the displacement correlation. (3) Results: DIC accurately characterizes the failure patterns. A variation in the subset size has a relevant effect on the strain magnification values. Step size effect magnitude depends on the subset size. The pattern matching criterion presented little influence (3.5%) on the strain magnification. (4) Conclusion: The current work provides a comprehensive analysis of the influence of some DIC parameters on compression failure characterization of foamed structures. It highlights that changes of subset and step sizes have a significant effect on the failure pattern definition and the strain magnification values, while the pattern matching criterion and the use of speckle have a minor influence on the results. Moreover, this work stands out that the determination of the pattern quality has a major importance for texture analysis. The in-depth, detailed study carried out with samples of three different apparent densities is a useful guide for DIC users as regards texture correlation and foamed structures.