Reviewerly/exHarmony huggingface dataset:
Image Recognition Algorithms Based on the Representation of Classes by Convex Hulls

Various approaches to the construction of pattern recognition algorithms based on the representation of classes as convex hulls in a multidimensional feature space are considered. This trend is well suited for biometrics problems with a large number of classes and small volumes of learning samples by class, for example, for problems of recognizing people by faces or fingerprints. In addition to simple algorithms for a point hitting a convex hull, algorithms of the nearest convex hull with different approaches to assessing the proximity of a test point to the convex hull of classes are investigated. Comparative experimental results are given and the advantages and disadvantages of the proposed approach are formulated.
Specification of Basis Spacing for Process Convolution Gaussian Process Models

Gaussian process (GP) models have been widely used for statistical modeling of point-referenced data in many scientific applications, including regression, classification, and clustering problems. Standard specification of GP models is computationally inefficient for applications with a large sample size. One solution is to construct the GP by convolving a smoothing kernel with a discretized White noise process, which requires choosing the number of bases. The distance between adjacent bases plays a key role in model accuracy. In this paper, we perform a series of simulations to find a general rule for the basis spacing required for an accurate representation of a discrete process convolution GP model. Under certain common conditions, we find that using a basis spacing of one-quarter the practical range of the process works well in practice.
Reasoning About Trustworthiness in Cyber-Physical Systems Using Ontology-Based Representation and ASP

This paper presents a framework for reasoning about trustworthiness in cyber-physical systems (CPS) that combines ontology-based reasoning and answer set programming (ASP). It introduces a formal definition of CPS and several problems related to trustworthiness of a CPS such as the problem of identification of the most vulnerable components of the system and of computing a strategy for mitigating an issue. It then shows how a combination of ontology based reasoning and ASP can be used to address the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.
DEVELOPMENT OF THE ISSUE OF USING CLOUD-ORIENTED SYSTEMS OF OPEN SCIENCE IN THE UKRAINIAN EDUCATIONAL SPACE

The article presents the Ukrainian experience in developing the issue of using cloud-oriented systems of open science. In particular, the concept of “digital transformation” and the processes it covers (digitization and digitalization) are considered. The main directions of digitalization in Ukraine are outlined. The main changes in the direction of Ukraine's integration into the European research area are reflected. The main provisions of the project “Cloud-based systems of open science for teachers’ training and professional development” (since October 2020) – the winner of the competition “Support for research of leading and young scientist”, implemented through grant support of the National Research Foundation of Ukraine. The purpose and objectives of the project, the expected social effect and results are announced. One of the key results should be the methods of using open science services.
AI SHOPPING SOLUTION - The Smartest All-in-One Shopping Solution

Abstract In today’s world, purchasing and shopping at supermarkets and malls are becoming part and parcel of urban life. We notice the huge rush in the markets and malls on holiday as well as in the weekends. The problem occurs at the customer end to find their likely products and to pay the bills standing at long queue; both of these are time consuming and tiring. In this paper, we discuss about an all new complete AI (Artificial Intelligence) SHOPPING SOLUTION which will make our marketing experience completely smart and hassle free. Especially during and after the COVID-19 situation, social distancing and human health will become the foremost priority of the modern civilization. This is the ideal situation to make use of this all new technology in which the customer can purchase his/her needful products at one’s fingertips by visiting the nearby market or getting the things delivered comfortably to his/her place by self-sanitizing drones. This entire shopping system will be controlled by our own indigenous developed mobile app or website.
Machine Learning-based PHY-authentication for Mobile OFDM Transceivers

In this paper, a machine learning (ML) framework is devised for physical layer (PHY) authentication in mobile orthogonal frequency-division multiplexing (OFDM) transceivers. The ML framework utilizes various classification models that exploit features extracted to capture the unique hardware behavior of different transmitters, namely: coarse carrier frequency offset (CFO), fine CFO, and residual CFO. These features are leveraged to train various classification models to distinguish between legitimate and non-legitimate transmitters. Since these features are mainly dependent of the hardware behavior more than the channel behavior, they are more resilient in mobility scenarios where channels are more dynamic. For validation, we adopt a software-defined radio (SDR) testbed to record the pertinent measurements in indoor and outdoor mobility environments. It is shown that the proposed approach can distinguish between legitimate and malicious transmitters effectively. Among various classifiers adopted, support vector machine (SVM) gives a true positive rate (TPR) classification of 0.97 with a false positive rate (FPR) of 0.02 in indoor environment; and a TPR of 0.93 with an FPR of 0.05 in outdoor environment.
Forensic face recognition based on KDE and evidence theory

Forensic face recognition (FFR) has been studied in recent years in forensic science. Given an automatic face recognition system, output scores of the system are used to describe the similarity of face image pairs, but not suitable for forensics. In this study, a score-mapping model based on kernel density estimation (KDE) and evidence theory is proposed. First, KDE was used to generate probability density function (PDF) for each dimensional feature vector of face image pairs. Then, the PDFs could be utilized to determine separately the basic probability assignment (BPA) of supporting the prosecution hypothesis and the defence hypothesis. Finally, the BPAs of each feature were combined by Dempster’s rule to get the final BPA, which reflects the strength of evidence support. The experimental results demonstrate that compared with the classic KDE-based likelihood ratio method, the proposed method has a better performance in terms of accuracy, sensitivity and specificity.
Schizophrenia Classification Using Fuzzy Kernel C-Means

Abstract Schizophrenia is a severe mental disorder that induces the mind, feeling, and behavior. Earlier treatment for this disease is essential; therefore, the ability to predict schizophrenia also becomes essential. Fuzzy kernel c-means was proposed in this research, utilizing the data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. There are several kernel functions; however, RBF and polynomial kernel function were used. As the evaluation, k-fold cross-validation with k = 3, 5, 7, and 10 was used, and each of the performances was analyzed. From the experiments, it was concluded that fuzzy kernel c-means using RBF kernel with σ = 0.01 and σ = 1 provides better performance than polynomial kernel with a similar running time with fuzzy c-means.
Quantum Information Propagation Using Huygens' Principle

A new computational framework for quantum electromagnetics has recently been proposed (D.-Y. Na, J. Zhu, F. L. Teixeira, and W. C. Chew, arXiv 1911.00947, 2019). In this work, we demonstrate the use of Huygens' Principle for quantum information propagation. Instead of numerical eigenmodes, Fourier modes are used to quantize the field. Wave propagation and second-order correlation function calculated based on this scheme are shown.
ICPR2020 Competition on Text Detection and Recognition in Arabic News Video Frames

After the success of the two first editions of the “Arabic Text in Videos Competition—AcTiVComp”, we are proposing to organize a new edition in conjunction with the 25th International Conference on Pattern Recognition (ICPR’20). The main objective is to contribute in the research field of text detection and recognition in multimedia documents, with a focus on Arabic text in video frames. The former editions were held in the framework of ICPR’16 and ICDAR’17 conferences. The obtained results on the AcTiV dataset have shown that there is still room for improvement in both text detection and recognition tasks. Four groups with five systems are participating to this edition of AcTiVComp (three for the detection task and two for the recognition task). All the submitted systems have followed a CRNN-based architecture, which is now the de facto choice for text detection and OCR problems. The achieved results are very interesting, showing a significant improvement from the state-of-the-art performances on this field of research.
Application of high-precision iterative techniques to improve the efficiency of encoding and decoding

The article discusses the methods and algorithms of high-precision iterative coding and decoding, which can improve the efficiency of error correction during decoding, finalized the basic principles of high-precision iterative code as a separate area in the field of noise-resistant coding, and obtained new results by modeling the work and the mathematical apparatus of the high-precision iterative coding system and decoding.
Digital reality and ontological status of digital technologies

In the article, based on the analysis of the definition of the category of reality, as well as the main trends in the development of non­classical philosophy, the legitimacy of introducing the concept of digital reality into the scientific and philosophical language is substantiated. The basis for asserting the existence of digital reality in the structure of the universe is a specific set of properties of digital technologies (digital objects), which distinguishes them from other objects of the universe and determines their special existence. In the article, based on the highlighted properties of digital technologies, they are defined as functional objects that perform operations with information presented in discrete form (measured in bits) by executing programs (algorithm) on a physical medium.
New Dimensions of Information Warfare: The Economic Pillar—Fintech and Cryptocurrencies

The fast-paced technological advancements of the last decades have led to digitizing an ever-increasing amount of information, processes, and activities. A wide range of new digital devices have made our lives easier, faster, and funnier, quickly becoming indispensable for both work and daily life. As a result, the digital realm has dramatically expanded its boundaries, replacing the physical world in several areas. Information warfare has found fertile ground to expand into this modernized electronic world, creating new scenarios and novel attacks on nations and citizens’ virtual perimeter. The economic sector plays an essential role in this context, widely affected and profoundly changed by recent technological advancements. For instance, the rapid rise of fintech systems, on the one hand, has led to the globalization of markets, with evident benefits on industries and tertiary services. On the other hand, the financial sector’s dependence on digital systems and information has increased dramatically, also introducing new digital risks. This paper explores the new threats opened up by the latest technological advancements to the national economy of a typical developed Country. After identifying two of the major targets of information warfare – cryptocurrencies and stock markets – we investigate possible attacks and evaluate their potential repercussions on the national economy, also highlighting promising avenues for future research and experimentation.
A Recommender Algorithm: Gradient Recurrent Neural Network Applied to Yang-Baxter-Like Equation

In this article, a traditional recommender algorithm termed gradient recurrent neural network (GRNN) model is introduced. Allowing for numerous practical problems such as the problems related to recommender systems or multi-agent systems that can be turned into matrix equation problems to resolve, the GRNN model becomes a more critical and promising role. The GRNN model, designed with the assistance of a square-norm-based energy function, is quite applicable to a recommender system and substantiated to be high-efficient in solving convex optimization linear or nonlinear problems. Simultaneously, implementing elaborately a theoretical analysis and numerical experiment computational simulation, the inherent exponential and stable convergence of the GRNN model is validated. With the aid of it, a theoretical nontrivial solution of the Yang-Baxter-like matrix equation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$XAX=AXA$</tex> can be obtained successfully.
The Role of Seismological Networks in Civil Protection in Low and High Seismic Hazards

Earthquakes are one of the most devastating natural disasters on Earth, causing sometimes huge economic losses and many human casualties. Since earthquake prediction is not yet possible, the purpose of civil protection is to reduce damage and protect human lives, in which the seismological networks of different countries play a very important role. Special applications of seismic networks are the early warning systems that can be used to protect vulnerable infrastructures using automated shutdown procedures, to stop high velocity trains and to save lives if the general public is notified about imminent strong ground shaking. In this paper, we describe the aims and operation of seismological networks, covering in more detail the early warning systems. Then we delineate the seismotectonic settings and seismicity in Hungary and Kazakhstan, furthermore, describe the operating seismological networks and the related scientific research areas with emphasis on civil protection. Hungary and Kazakhstan differ not only in the size of their territory, but also in their seismicity, therefore, in addition to the similarities, there are also significant differences between the aims and problems of their seismological networks.
Decentralized Fuzzy Sliding Mode Control Using Jaya Algorithm

This paper presents a decentralized fuzzy sliding mode controller (DFSMC) which its parameters are optimized using Jaya algorithm. This Controller used to control the levels in a quadruple tank system (QTS). Sliding mode controller (SMC) is robust in respect to the plant uncertainties and perturbation, but has chattering phenomenon that is a drew-back. We use a fuzzy logic system in order to reduce the chattering phenomenon. Jaya algorithm is employed to obtain the optimal parameters of the fuzzy logic system. The simulation results show that the proposed method is more effective.
A Novel Ensemble Framework for Face Search

This paper proposes an ensemble of different state-of-art algorithms for realizing a face search system aimed at achieving higher accuracies compared to any single algorithm. This is achieved by leveraging most promising deep networks (Facenet, OpenFace, DeepFace, and VGGFace – originally trained for face recognition) and different Approximate Nearest Neighbor Search (ANNS) algorithms (Annoy and LSHash). Face images in the database are subjected to feature extraction (embeddings computed by deep networks) and indexing (in set structure for faster search) by ANNS algorithms. An input face query image is processed in the following four stages. First, the face region is detected from the query image and appropriately aligned for further processing. Second, the facial features are extracted using multiple deep networks. Third, the ANNS algorithms perform fast search by efficiently shrinking the gallery size from millions to a few hundred faces. Fourth, a fine matching is performed using two different methods separately to produce the final search results. These are (a) cosine similarity and (b) score-based matching and re-ranking of results. The experimental results demonstrate the diversity in results obtained by use of multiple feature extractors and ANNS techniques and the accuracy achieved by using the proposed ensemble framework.
A Target Recognition Algorithm in Remote Sensing Images

In the remote sensing image, the background is complex, which results in the low detection rate and slow speed of some target recognition algorithms. Therefore, an improved RFB Net model is used in this paper. The algorithm builds a feature pyramid network based on RFB Net model and adds more branches to the RFB module to improve the network recognition ability. The experimental results show that the improved algorithms have higher detection rate and better recognition effect.
Abstraction Hierarchy Based Explainable Artificial Intelligence

This work explores the application of Cognitive Work Analysis (CWA) in the context of Explainable Artificial Intelligence (XAI). We built an AI system using a loan evaluation data set and applied an XAI technique to obtain data-driven explanations for predictions. Using an Abstraction Hierarchy (AH), we generated domain knowledge-based explanations to accompany data-driven explanations. An online experiment was conducted to test the usefulness of AH-based explanations. Participants read financial profiles of loan applicants, the AI system’s loan approval/rejection decisions, and explanations that justify the decisions. Presence or absence of AH-based explanations was manipulated, and participants’ perceptions of the explanation quality was measured. The results showed that providing AH-based explanations helped participants learn about the loan evaluation process and improved the perceived quality of explanations. We conclude that a CWA approach can increase understandability when explaining the decisions made by AI systems.
Generation of third-order parametric down-conversion photons via weak nonlinearities

We consider six-photon entangled state emitted from a single third-order parametric down-conversion process. In the regime of weak nonlinearities, we present a symmetry detector which is capable of analyzing the twin-beam six-photon symmetric states. By cascading the symmetry detectors, as an application, it is shown that one can purify the desired six-photon entangled state from an arbitrary twin-beam six-photon symmetric state. With linear optics we propose a fruitful scheme for exploring a class of multimode-photon entangled states from third-order parametric down-conversion process. Furthermore, we provide a method to generate the six-photon polarization entangled Greenberger-Horne-Zeilinger state based on linear optics and weak nonlinearities.
FFT-Based Zero-Bit Watermarking for Facial Recognition and Its Security

The security of a system through biometric features is one of the acceptable trends to which every researcher recommends. Such biometric features contain features of iris, fingerprint, face, etc. Today, these security traits are widely exploited by an imposter to trace unlicensed access to the system. So it demands to secure these features since foremost alarming challenges are raises when these trustworthy features are compromised. This paper endorses the design of zero-bit watermarking in which a user’s unique id is integrated with his facial features in the multi-transform domain. The user’s unique ID is used to establish true authentication of the host facial features and also to recognize the face without any segmentation techniques. To make this effort tangible, Singular values are calculated of some particular frequency coefficients of the host image that are generated using FFT transformation in which a number of appropriate regions of the host image are selected based on their frequency of information. These Singular values of host facial image are calculated using SVD in which watermark (image) user’s unique ID is integrated correctly while maintaining the equilibrium among imperceptibility, robustness, and payload. The resultant watermarked image is tested against various image processing attacks and satisfying results are conducted to assure robustness of the model. Thus, the model is indeed satisfying security of the host biometric of the user.
BBRefinement: A Universal Scheme to Improve the Precision of Box Object Detectors

We present a conceptually simple yet powerful and general scheme for refining the predictions of bounding boxes produced by an arbitrary object detector. Our approach was trained separately on single objects extracted from ground truth labels. For inference, it can be coupled with an arbitrary object detector to improve its precision. The method, called BBRefinement, uses a mixture of data consisting of the image crop of an object and the object’s class and center. Because BBRefinement works in a restricted domain, it does not have to be concerned with multiscale detection, recognition of the object’s class, computing confidence, or multiple detections. Thus, the training is much more effective. It results in the ability to improve the performance of SOTA architectures by up to two mAP points on the COCO dataset in the benchmark. The refinement process is fast; it adds 50–80 ms overhead to a standard detector using RTX2080; therefore, it can run in real time on standard hardware. Finally, we show that BBRefinement can also be applied to COCO’s ground truth labels to create new, more precise labels. The link to the source code is provided in the contribution.
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters

Having data with a high number of features raises the need to detect clusters which exhibit within subspaces of features a high similarity. These subspaces can be arbitrarily oriented which gave rise to arbitrarily-oriented subspace clustering (AOSC) algorithms. In the diversity of such algorithms some are specialized at detecting clusters which are global, across the entire dataset regardless of any distances, while others are tailored at detecting local clusters. Both of these views (local and global) are obtained separately by each of the algorithms. While from an algebraic point of view, none of both representations can claim to be the true one, it is vital that domain scientists are presented both views, enabling them to inspect and decide which of the representations is closest to the domain specific reality. We propose in this work a framework which is capable to detect locally dense arbitrarily oriented subspace clusters which are embedded within a global one. We also first introduce definitions of locally and globally arbitrarily oriented subspace clusters. Our experiments illustrate that this approach has no significant impact on the cluster quality nor on the runtime performance, and enables scientists to be no longer limited exclusively to either of the local or global views.