Reviewerly/exHarmony huggingface dataset:
New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

Digital image collection as rapidly increased along with the development of computer network.Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user's requirements in similarity evaluations such as image content similarity, edge, and color similarity.Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR).The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images.The algorithms used to calculate the similarity between extracted features.There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval.For the general image retrieval, the goal of the query is to obtain images with the same object as the query.Such CBIR imitates web search engines for images rather than for text.For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray.In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed.CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features.This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters.The parameters are precision, recall and response time.
Information Design for Personas in Four Professional Domains of User Experience Design, Healthcare, Market Research, and Social Media Strategy

Practitioners in user-centric industries have increasingly recognized the applicability of personas.However, the methods used to create personas in different domains remain inconsistent and unsystematic.We analyzed 51 studies focused on designing personas for professional purposes and find the practice most prevalent in the user experience (UX) design, healthcare, market research, and social media strategy domains.Within these domains, UX design personas are characterized by their focus on user activity and goals, market research personas on customers' lifestyles, health personas on medical patients' physical symptoms, and social media strategy personas on interactions within online communities.We identify and compare the elements in the personas.Based on this, we provide guidelines for professionals interested in developing personas for recruiting users, understanding barriers to positive user experience, and building online communities, including how to represent persona details related to lifestyle, contexts of product usage, and scaling of online data.
Experimental demonstrations of unconditional security in a purely classical regime

So far, unconditional security in key distribution processes has been confined to quantum key distribution (QKD) protocols based on the no-cloning theorem of nonorthogonal bases. Recently, a completely different approach, the unconditionally secured classical key distribution (USCKD), has been proposed for unconditional security in the purely classical regime. Unlike QKD, both classical channels and orthogonal bases are key ingredients in USCKD, where unconditional security is provided by deterministic randomness via path superposition-based reversible unitary transformations in a coupled Mach-Zehnder interferometer. Here, the first experimental demonstration of the USCKD protocol is presented.
A Blockchain-Based Public Auditing Protocol with Self-Certified Public Keys for Cloud Data

Cloud storage can provide a way to effectively store and manage big data. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in a traditional way, which leads to the introduction of the auditing techniques. This paper proposes a public auditing protocol with a self-certified public key system using blockchain technology. The user's operational information and metadata information of the file are formed to a block after verified by the checked nodes and then to be put into the blockchain. The chain structure of the block ensures the security of auditing data source. The security analysis shows that attackers can neither derive user’s secret key nor derive users’ data from the collected auditing information in the presented scheme. Furthermore, it can effectively resist against not only the signature forging attacks but also the proof forging attacks. Compared with other public auditing schemes, our scheme based on the self-certified public key system has been improved in storage overhead, communication bandwidth, and verification efficiency.
A Protection against the Extraction of Neural Network Models

Given oracle access to a Neural Network (NN), it is possible to extract its underlying model. We here introduce a protection by adding parasitic layers which keep the underlying NN's predictions mostly unchanged while complexifying the task of reverse-engineering. Our countermeasure relies on approximating a noisy identity mapping with a Convolutional NN. We explain why the introduction of new parasitic layers complexifies the attacks. We report experiments regarding the performance and the accuracy of the protected NN.
No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization

We study the first-order convex optimization problem, where we have black-box access to a (not necessarily smooth) function f:ℝⁿ → ℝ and its (sub)gradient. Our goal is to find an e-approximate minimum of f starting from a point that is distance at most R from the true minimum. If f is G-Lipschitz, then the classic gradient descent algorithm solves this problem with O((GR/e)²) queries. Importantly, the number of queries is independent of the dimension n and gradient descent is optimal in this regard: No deterministic or randomized algorithm can achieve better complexity that is still independent of the dimension n.In this paper we reprove the randomized lower bound of Ω((GR/e)²) using a simpler argument than previous lower bounds. We then show that although the function family used in the lower bound is hard for randomized algorithms, it can be solved using O(GR/e) quantum queries. We then show an improved lower bound against quantum algorithms using a different set of instances and establish our main result that in general even quantum algorithms need Ω((GR/e)²) queries to solve the problem. Hence there is no quantum speedup over gradient descent for black-box first-order convex optimization without further assumptions on the function family.
Dependence of Photon Detection Efficiency on Normal-State Sheet Resistance in<i>Marginally</i>Superconducting Films of NbN

We present an extensive set of data on nanowire-type superconducting single-photon detectors based on niobium-nitride (NbN) to establish the empirical correlation between performance and the normal-state resistance per square. We focus, in particular, on the bias current, compared to the expected depairing current, needed to achieve a near-unity detection efficiency for photon detection. The data are discussed within the context of a model in which the photon energy triggers the movement of vortices <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> superconducting dissipation, followed by thermal runaway. Since the model is based on the non-equilibrium theory for conventional superconductors deviations may occur, because the efficient regime is found when NbN acts as a marginal superconductor in which long-range phase coherence is frustrated.
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification

Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in DNNs. The proposed LightLayers consists of LightDense and LightConv2D layers that are as efficient as regular Conv2D and Dense layers but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting in lightweight DNN models that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CIFAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, and CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.
Unsupervised learning methods for data clustering

Unsupervised learning is a kind of machine learning strategy utilized for drawing inferences from the datasets containing input data without any labeled responses. The purpose of the unsupervised machine learning method is to determine the similarities in data points and assemble similar data together. The commonly used unsupervised learning technique is cluster analysis, which is massively utilized for exploratory data analysis to determine the hidden patterns and to group the data. Here, a review of unsupervised learning techniques is done for performing data clustering on massive datasets. For clustering, different classical clustering strategies are adapted that group similar data instances in one group. From the analysis, hierarchical clustering is capable to provide clusters with cluster configurations having only one cluster, with each element in the dataset, and treating all elements as a solitary cluster. Density-based clustering spots dense clusters in the high-dimensional data as clusters, which are divided by gaps controlled by parameters. In addition, the nomenclature of different clustering mechanisms is devised in this chapter. However, the higher number and sturdy multiplicity of conventional clustering techniques are infeasible to acquire categorization which is both absolute and historic. By concentrating on discerning criteria, the methods are classified and illustrated in this chapter.
Optical distortion correction considering radial and tangential distortion rates defined by optical design

This paper proposes a high-accuracy distortion correction method for optical imaging distortion mapping and computational image predistortion. Due to the establishment of the predistortion is based on the distortion evaluation in the common optical design software CODE V, it can be used in the broad field of optical imaging system. Two distortion correction methods considering radial and tangential distortion rates which defined by optical design are introduced to perform distortion correction, and the forward and inverse image warping interpolation methods are adopted, separately. Simulations are conducted to verify the effectiveness in correcting complex distortion for freeform off-axis optical system. Furthermore, we implement a distortion correction experiment for an augmented reality head-mounted display that suffers from an asymmetric distortion with the maximum tangential distortion over 7%. For the first method, fast and high-accuracy image warping is achieved, and the RMS error of the approximation predistortion image is less than 1 pixel. Meanwhile, the RMS errors of the corrected imaging points are approximately 1.93 pixels vertically and 2.16 pixels horizontally when using a 5-megapixel camera. The second method considers the additional influence of the actual machining and assembly errors of the optical system, and the RMS errors of 0.89 pixels vertically and 0.85 pixels horizontally are achieved.
The Feasibility and Significance of Employing Blockchain-Based Identity Solutions in Health Care

The wide adoption of wireless communication and mobile devices has facilitated the development of numerous applications to provide citizens with convenient access to health-related tracking and management services. Most of those services require the storage of some personal data and therefore resort to common user authentication practice (e.g., using a username and password combination) to ensure data is delivered to the appropriate party. As a result, users often find themselves having to maintain or memorize many combinations of accounts and their associated login credentials during their interaction with different services throughout the lifespan. Given the advancement of blockchain and distributed ledger technologies, a wealth of services in various domains including health care has explored the feasibility of migrating existing centralized services to such decentralized infrastructures. Because of this exploration, traditionally centralized authentication approaches managed by one party can no longer support the need of onboarding users, managing, and monitoring user activities and transactions in a decentralized manner. A community of researchers has hence been formed to study blockchain-based identity solutions, such as decentralized identities and self-sovereign identities, that would allow users to have a more common way to identify themselves when accessing a plethora of services. The main goal of these identity methods is to eliminate the need of requiring users to maintain multiple identifiers or online credentials as each individual has only one identity that truly represents themselves. These identities would be established and secured by cryptographic principles such that they still preserve at least the same security and privacy levels as their centralized counterparts. In this chapter, we first present a systematic overview of the underlying motivations and principles of blockchain-based identities to provide the audience with a basic understanding of how such identities operate and the pressing need to incorporate them. We will also introduce two of the popular blockchain-based identity frameworks currently adopted in decentralized applications. We then discuss the potential applications of these identities and their feasibility using the health care domain as a case study to hopefully inspire our readers with ideas that can be further investigated as research solutions in the health care or other domains. Lastly, we will conclude the chapter with additional discussions on the practicality of blockchain-based identities and the potential caveats or limitations associated. This chapter will serve as a cornerstone for healthcare executives, informaticians, and security/privacy experts to further investigate and make infrastructural decisions.
Commodity Image Classification Based on Improved Bag-of-Visual-Words Model

With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively.
Improved Bounds on Sizes of Generalized Caps in $AG(n,q)$

An $m$-general set in $AG(n,q)$ is a set of points such that any subset of size $m$ is in general position. A 3-general set is often called a capset. In this paper, we study the maximum size of an $m$-general set in $AG(n,q)$, significantly improving previous results. When $m=4$ and $q=2$, we give a precise estimate, solving a problem raised by Bennett.
Tolerance in the Communicative Culture of Modern Educational Manager

The main results of the study are the analysis of the source base from the standpoint of scientific approaches to various fields of knowledge (economics, sociology, psychology, pedagogy, management, culturology, etc.), which made it possible to identify a modern interpretation of "education management". The determinants of educational management as a component of social in the context of intangible, spiritual resource of education have been revealed. The similar and different characteristics of the managerial activity of the education manager and the pedagogical worker have been outlined. The essence, origin, structure, types and directions of functioning of communicative culture of the manager of education have been determined.
Nonreciprocal transition between two nondegenerate energy levels

Stimulated emission and absorption are two fundamental processes of light–matter interaction, and the coefficients of the two processes should be equal. However, we will describe a generic method to realize the significant difference between the stimulated emission and absorption coefficients of two nondegenerate energy levels, which we refer to as a nonreciprocal transition. As a simple implementation, a cyclic three-level atom system, comprising two nondegenerate energy levels and one auxiliary energy level, is employed to show a nonreciprocal transition via a combination of synthetic magnetism and reservoir engineering. Moreover, a single-photon nonreciprocal transporter is proposed using two one-dimensional semi-infinite coupled-resonator waveguides connected by an atom with nonreciprocal transition effect. Our work opens up a route to design atom-mediated nonreciprocal devices in a wide range of physical systems.
Self-Supervised Classification Network

We present Self-Classifier – a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels. In our theoretical analysis, we prove that degenerate solutions are not in the set of optimal solutions of our approach. Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised classification and contrastive representation learning approaches, it does not require any form of pre-training, expectation-maximization, pseudo-labeling, external clustering, a second network, stop-gradient operation, or negative pairs. Despite its simplicity, our approach sets a new state of the art for unsupervised classification of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised representation learning. Code is available at https://github.com/elad-amrani/self-classifier .
On the PIV/PTV uncertainty related to calibration of camera systems with refractive surfaces

Abstract This paper investigates the calibration and measurement uncertainty related to the use of different camera models in optical systems that include refractive surfaces. A refractive surface is an interface between media with different optical properties which introduces distortions in the imaging process due to the refraction of the lines-of-sight. This is an issue common to all the investigations of fluids flowing around or inside transparent solid geometries and is of relevance for a strong curvature of the solid/fluid interface. Appropriate modelling of the refractive effects is possible by integrating the pinhole camera model with a ray-tracing method, as demonstrated in a previous work (Paolillo and Astarita 2020 IEEE Trans. Pattern Anal. Mach. Intell. ). On the other side, analytical camera models with a pure mathematical foundation, like those based on polynomials or rational functions, are classically used in the PIV/PTV community. Due to the non-linear nature of the involved distortions, the accuracy of these models in representing the imaging process in presence of refractive geometries depends strongly on the polynomial order and noise of the data used for the calibration. The current work provides a numerical estimate of the uncertainty inherent to the analytical camera models by using data generated via a reference refractive camera model. The present results show that high accuracy requires high orders, which implies a large number of calibration parameters and high demand for computational resources. In particular, the rational mapping functions exhibit superior performance compared to the polynomials, although their calibration is found to be sensitive to image noise and they might yield large extrapolation errors. An experimental verification is also reported, which shows that for the estimation of the velocity statistics a 7th order polynomial model offers results comparable to those of a refractive camera model.
Speculative Dereferencing: Reviving Foreshadow

In this paper, we provide a systematic analysis of the root cause of the prefetching effect observed in previous works and show that its attribution to a prefetching mechanism is incorrect in all previous works, leading to incorrect conclusions and incomplete defenses. We show that the root cause is speculative dereferencing of user-space registers in the kernel. This new insight enables the first end-to-end Foreshadow (L1TF) exploit targeting non-L1 data, despite Foreshadow mitigations enabled, a novel technique to directly leak register values, and several side-channel attacks. While the L1TF effect is mitigated on the most recent Intel CPUs, all other attacks we present still work on all Intel CPUs and on CPUs by other vendors previously believed to be unaffected.