Reviewerly/exHarmony huggingface dataset:
<i>CookieArmor</i>: Safeguarding against cross‐site request forgery and session hijacking

Internet browsers use cookies and session‐IDs for maintaining HTTP state information which has led to several security vulnerabilities such as cross‐site request forgery (CSRF) and session hijacking. Several works have been carried out in the past to address these threats and most of these works propose an additional layer between the client‐server communication architecture. While some solutions propose this layer at the client side, others propose it at the server side. This work proposes an efficient client‐side proxy named CookieArmor for addressing the threat of CSRF and cross‐site scripting (XSS). CookieArmor is a two‐token selective CSRF prevention mechanism with restricted relaxation for cross‐origin request. CookieArmor determines whether a script is capable of performing state changing operations and safeguards only such scripts using a two‐token CSRF prevention scheme. CookieArmor uses active Session‐ID filtering to safeguards against session hijacking. CookieArmor also uses quotient‐filter between the proxy and its internal database for improving the performance. Efficiency of CookieArmor demonstrates significant improvement than other solutions.
No-reference remote sensing image quality assessment based on gradient-weighted natural scene statistics in spatial domain

Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a no-reference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics is proposed. A 36-dimensional image feature vector is constructed by extracting the local normalized luminance features and the gradient-weighted local binary pattern features of local normalized luminance map in three scales. First, a support vector machine classifier is obtained by learning the relationship between image features and distortion types. Then based on the support vector machine classifier, the support vector regression scorer is obtained by learning the relationship between image features and image quality scores. A series of comparative experiments were carried out in the optics remote sensing image database, the LIVE database, the LIVEMD database, and the TID2013 database, respectively. Experimental results show the high accuracy of distinguishing distortion types, the high consistency with subjective scores, and the high robustness of the method for remote sensing images. In addition, experiments also show the independence for the database and the relatively high operation efficiency of this method.
A hybrid approach to generate visually seamless aerial mosaicks from unmanned aerial vehicles

The need for understanding the terrain or conditions of large areas aerially has gained prominence as the aerial images provide a near clear coverage of the area under study. Individual image provides just a portion of the area, thus to understand the whole area, mosaicking or stitching of these i mages is needed. Image mosaicking aids in providing with a ”Big Picture” as an outcome by joining the images taken during the flight. In this paper we propose a method which aims at generating a seamless aerial mosaick using only the images captured by the UAV as input. This involves identifying candidate images from the images captured by the UAV periodically during its flight and stitching the images together. This method evaluates various feature descriptors and feature matching techniques that can be integrated into the mosaicking system. The proposed work is a hybrid approach that uses the Scale Invariant Feature Transform (SIFT) for feature extraction and the key features are matched using the Fast Library for Approximate Nearest Neighbors (FLANN). RANdom Sample Consensus (RANSAC), is used for the removal of features that are redundant or act as outliner, providing candidates for Homography estimation. This is followed by image stitching that involves the use of Multi-Band Blending to produce a visually seamless mosaick. The results obtained were evaluated for quality using Universal Quality index Measure (QIM) and is found to be perfect.
Laser-Based Interferometric Techniques for the Study of Musical Instruments

Laser-based interferometry techniques for the study of musical instruments are discussed in this chapter. The presented work demonstrates the advantages that the laser-based optical techniques provide and is mainly focused on the capabilities of the Electronic Speckle Pattern Interferometry (ESPI). The mathematical description of the time-average ESPI, the reading of the contour lines and the overcoming of the limitations of amplitude and phase vibration are analyzed. Furthermore, four representative studies using the ESPI for a Cretan Lyra, a Bendir, a classical Guitar and the ancient Greek lyra Chelys, are presented and demonstrate the capabilities of the method.
TAPON-MT: A versatile framework for semantic labelling

Semantic labelling refers to the problem of assigning known labels to the elements of structured information from a source such as an HTML table or an RDF dump with unknown semantics. In the recent years it has become progressively more relevant due to the growth of available structured information in the Web of data that need to be labelled in order to integrate it in data systems. The existing approaches for semantic labelling have several drawbacks that make them unappealing if not impossible to use in certain scenarios: not accepting nested structures as input, being unable to label structural elements, not being customisable, requiring groups of instances when labelling, requiring matching instances to named entities in a knowledge base, not detecting numeric data, or not supporting complex features. In this article, we propose TAPON-MT, a framework for machine learning semantic labelling. Our framework does not have the former limitations, which makes it domain-independent and customisable. We have implemented it with a graphical interface that eases the creation and analysis of models, and we offer a web service API for their application. We have also validated it with a subset of the National Science Foundation awards dataset, and our conclusion is that TAPON-MT creates models to label information that are effective and efficient in practice.
Peer Assessment Improvement Using Fuzzy Logic

Peer assessment, consists of a prearrangement between learners to consider and specify the level, value, or quality of a product or performance or other equal-status learners. The practice imposes itself when trying to evaluate a large number of students, teachers are practically obliged to use peer assessment, especially in Massive Open Online Courses (MOOCs). However, the novice students, unlike their teachers, are not formed to assess others contributions. Therefore, their evaluations are unreliable and may be biased. Here we try to improve the peer assessment outcome, using fuzzy logic to model opinions, those opinions are weighed according to their validity, then aggregated in order to achieve consensus, hence reliable evaluation.
Universally Composable Accumulators

Accumulators, first introduced by Benaloh and de Mare (Eurocrypt 1993), are compact representations of arbitrarily large sets and can be used to prove claims of membership or non-membership about the underlying set. They are almost exclusively used as building blocks in real-world complex systems, including anonymous credentials, group signatures and, more recently, anonymous cryptocurrencies. Having rigorous security analysis for such systems is crucial for their adoption and safe use in the real world, but it can turn out to be extremely challenging given their complexity. In this work, we provide the first universally composable (UC) treatment of cryptographic accumulators. There are many different types of accumulators: some support additions, some support deletions and some support both; and, orthogonally, some support proofs of membership, some support proofs of non-membership, and some support both. Additionally, some accumulators support public verifiability of set operations, and some do not. Our UC definition covers all of these types of accumulators concisely in a single functionality, and captures the two basic security properties of accumulators: correctness and soundness. We then prove the equivalence of our UC definition to standard accumulator definitions. This implies that existing popular accumulator schemes, such as the RSA accumulator, already meet our UC definition, and that the security proofs of existing systems that leverage such accumulators can be significantly simplified. Finally, we use our UC definition to get simple proofs of security. We build an accumulator in a modular way out of two weaker accumulators (in the style of Baldimtsi et al. (Euro S&P 2017), and we give a simple proof of its UC security. We also show how to simplify the proofs of security of complex systems such as anonymous credentials. Specifically, we show how to extend an anonymous credential system to support revocation by utilizing our results on UC accumulators.
Clusterization by the K-means method when K is unknown

There are various methods of objects’ clusterization used in different areas of machine learning. Among the vast amount of clusterization methods, the K-means method is one of the most popular. Such a method has as pros as cons. Speaking about the advantages of this method, we can mention the rather high speed of objects clusterization. The main disadvantage is a necessity to know the number of clusters before the experiment. This paper describes the new way and the new method of clusterization, based on the K-means method. The method we suggest is also quite fast in terms of processing speed, however, it does not require the user to know in advance the exact number of clusters to be processed. The user only has to define the range within which the number of clusters is located. Besides, using suggested method there is a possibility to limit the radius of clusters, which would allow finding objects that express the criteria of one cluster in the most distinctive and accurate way, and it would also allow limiting the number of objects in each cluster within the certain range.
Self-tuning fast adaptive algorithm for impulsive noise suppression in color images

In this paper, a self-tuning version of the newly introduced Fast Adaptive Switching Trimmed Arithmetic Mean Filter, which is a very efficient technique for impulsive noise suppression, is elaborated. Most of the methods presented in the rich literature have numerous parameters, whose proper settings are crucial for efficient noise suppression. Although researchers often provide recommended values for their algorithms’ parameters, the actual choice remains in the hands of the user. Our goal is to free the operator from parameter selection dilemma and to propose an algorithm which includes required expert knowledge within itself. The only obligatory inputs of the proposed algorithm (from the user perspective) are the image itself and the size of the operating window.
Local Rademacher Complexity Machine

Support Vector Machines (SVMs) are a state-of-the-art and powerful learning algorithm that can effectively solve many real world problems. SVMs are the transposition of the Vapnik–Chervonenkis (VC) theory into a learning algorithm. In this paper, we present the Local Rademacher Complexity Machine (LRCM), a transposition of the Local Rademacher Complexity (LRC) theory, the state-of-the-art evolution of the VC theory, into a learning algorithm. Analogously to what has been done for the SVMs, we will present first the theoretical ideas behind the LRC theory, we will show how these ideas can be translated into a learning algorithm, the LRCM, and then how the LRCM can be made efficient and kernelizable. By exploiting a series of real world datasets, we will show the effectiveness of the LRCM against the SVMs.
Estimating the Resource Demand in Power-Aware Clusters by Regressing a Linearly Dependent Relation

Large-scale clusters are often built with over-provisioned service resources, so as to satisfy the huge demand raised by enormous users in cloud environments. By estimating the resource demand of workloads, an on-demand resource provisioning method can be realized in these clusters, thus improving the energy efficiency. However, to guarantee Quality of Service (QoS), the resource demand of workload should be accurately estimated so as to provide suitable resources. Many statistical approaches estimate actual resource demand based on some workload features. But the relations between actual resource demand and workload features are generally obscure, and it's a big challenge to gain an accurate estimation under an obscure relation. In this paper, by considering a cluster as a queueing system, we construct a linearly dependent relation between resource demand and multiple feature combinations. The linearly dependent relation is inconstant due to its variable coefficients. Then, to ascertain specific relations which match actual situations, we design a Basic Linear regression (BL) algorithm. BL can obtain the optimal values for these coefficients, thus determining the inconstant relation to specific ones. Finally, we propose a Constructed Linear regression (CL) approach to estimate actual resource demands. CL forms a two-layer neural network by using several processes of BL as the neurons. To evaluate CL, we realize an On-Demand Resource Provisioning (ODRP) method in a typical power-aware cluster. Several evaluation metrics are proposed for conducting extensive experiments. The experimental results show that CL is effective to make accurate estimations.
How to Assess the Usability Metrics of E-Voting Schemes

Voters play an important role in end-to-end verifiable e-voting schemes because the schemes encourage them to carry out several security-critical tasks by themselves. If the voters cannot complete the tasks by themselves or experience bad usability while executing them, vote manipulations by either a faulty software or deliberate attacks cannot be detected which renders verification useless. Therefore, the scheme’s usability is of crucial importance and demands an early investigation of human factors when implementing e-voting systems. In this paper, we give an overview of user study design challenges when investigating end-to-end verifiable e-voting schemes. We provide guidelines that address these challenges and support researchers in the design of user studies. The guidelines are based on the literature and the authors’ experiences.
A meta-argumentation approach for the efficient computation of stable and preferred extensions in dynamic bipolar argumentation frameworks

Bipolar argumentation frameworks (BAFs) extend Dung’s argumentation frameworks to explicitly represent the notion of support between arguments, in addition to that of attack. BAFs can be profitably used to model disputes between two or more agents, with the aim of deciding the sets of arguments tha t should be accepted to support a point of view in a discussion. However, since new arguments, attacks, and supports are often introduced to take into account new available knowledge, BAFs as well as the set of accepted arguments (under a given semantics) change over the time. In this paper we first tackle the problem of efficiently recomputing sets of accepted arguments of dynamic BAFs (under the preferred and stable semantics). Focusing on a deductive interpretation of the support relation, we introduce an incremental approach that, given an initial BAF, an initial extension for it, and an update, computes an extension of the updated BAF. This is achieved by introducing a meta-argumentation transformation according to which an initial BAF, as well as its extension and an update, are transformed into a plain argumentation framework (AF) with a suitable initial extension and update. Thanks to the use of the meta-argumentation intermediate level, our approach is able to incorporate existing AF-solvers and an incremental technique for plain AFs in order to compute an extension of the updated BAF. Moreover, our approach can be seamlessly applied to a more general form of BAFs, namely Extended Bipolar Argumentation Frameworks (EAFs), where defeasible supports and defeats are modelled by means of second-order attacks (i.e., attacks toward elements of the support or attack relation). We experimentally validated our approach on both BAFs and EAFs. The experiments showed that, on average, our technique is almost 100 times faster than computing extensions of updated BAFs or EAFs from scratch.
Machine Learning Approaches for Prediction of Facial Rejuvenation Using Real and Synthetic Data

This paper proposes a novel machine learning approaches to predict the outcome of facial rejuvenation prior to a cosmetic procedure. This is achieved by estimating the required amount of dermal filler volume that needs to be applied on the face by learning the underlying structural mapping from the pretreatment and posttreatment 3D face images. We develop and train our proposed deep neural network, called Rejuv3DNet, designed specifically to predict the dermal filler volume. We also propose the kernel regression (KR)-based model to validate and improve our volume estimation results using regression. Our other contributions include the development of the first 3D face cosmetic dataset, which consists of real-world pretreatment and posttreatment 3D face images and a novel technique for the generation of synthetic cosmetic treatment 3D face images. Our experimental results show that the proposed Rejuv3DNet and the KR model achieve 62.5% and 66.67%, respectively, on real-world data, while these techniques achieve a prediction accuracy of 75.2% and 89.5%, and 77.2% and 90.1% on our two different synthetic datasets. Our proposed techniques have been found to be computationally efficient, achieving near real-time prediction performance. The reported accuracies are our preliminary results for proof of concept, which can be improved with more data. The proposed approach has the potential for further investigation in the cosmetic surgery domain.
Temporal Motionless Analysis of Video using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.
A SCRIM Drive System Using Backstepping Control and Revamped Recurrent Romanovski PNN with Mended ACO

A six-phase copper rotor induction motor (SCRIM) drive system with nonlinear uncertainties is controlled by a backstepping control system using a revamped recurrent Romanovski polynomials neural network (PNN). To achieve better control performance, an adaptive law of the revamped recurrent Romanovski PNN is based on the Lyapunov function to estimate the lumped uncertainty. Additionally, an error estimated law is proposed to compensate the estimated error. Meanwhile, the mended ant colony optimization (ACO) is used for regulating two variable learning rates in the revamped recurrent Romanovski PNN. Finally, some experimental results and a comparative analysis are presented to verify the effective of the proposed control system.
Peculiarities of Psychological Culture Development for Gifted Senior Pupils in Rural Settings

The main purpose of the article is to consider psychological culture of a gifted personality as a resource for achievements and social self-realization. The work introduces the outcomes of empirical research concerning differences in developmental components of psychological culture of gifted senior pupils and their peers with average and low performance in rural settings. In addition, the authors analyze the peculiarities of a cognitive and value-notional component of personal psychological culture identified in gifted senior pupils. The article emphasizes the importance of the formation of psychological culture for a gifted person as its systemic characteristics and an integral part of the basic culture. The review of modern problems of research of giftedness and psychological culture of personality is made. The basic components of psychological culture are determined, they are empirically studied in gifted students. The empirical data characterizing the level of psychological literacy, readiness for self-development and life orientations of gifted high school students are generalized. Statistical confirmation of sufficient level of development of cognitive and value-semantic components of psychological culture of the gifted senior pupils of rural school is resulted that allow to consider them as real internal resources of achievements and social self-realization of the gifted personality.
OCEAN: Object-centric arranging network for self-supervised visual representations learning

Learning visual representations plays an important role in computer vision and machine learning applications. It facilitates a model to understand and perform high-level tasks intelligently. A common approach for learning visual representations is supervised one which requires a huge amount of human annotations to train the model. This paper presents a self-supervised approach which learns visual representations from input images without human annotations. We learn the correct arrangement of object proposals to represent an image using a convolutional neural network (CNN) without any manual annotations. We hypothesize that the network trained for solving this problem requires the embedding of semantic visual representations. Unlike existing approaches that use uniformly sampled patches, we relate object proposals that contain prominent objects and object parts. More specifically, we discover the representation that considers overlap, inclusion, and exclusion relationship of proposals as well as their relative position. This allows focusing on potential objects and parts rather than on clutter. We demonstrate that our model outperforms existing self-supervised learning methods and can be used as a generic feature extractor by applying it to object detection, classification, action recognition, image retrieval, and semantic matching tasks.
The optimization of sum-product network structure learning

Sum-Product Network (SPN) are recently introduced deep tractable Probabilistic Graphical Models providing exact and tractable inference. SPN have been successfully employed as density estimators in some artificial intelligence fields, however, most of the proposed structure learning algorithms focus on improving the performance of a certain aspect of model, at the cost of reducing other performance. This is due to the fact that there is no effective balance between network width and depth during learning process. In this paper, we propose two clustering analysis algorithms to replace the clustering part of LearnSPN. We improve the structure quality of the generated model by deepening the network while adjusting the network width adaptively, trying to find a balance between the expressive power, representation ability, inference accuracy and simplicity. Experimental results prove that LearnSPN equipped by our clustering method has different degrees of improvement in various performances.