Reviewerly/exHarmony huggingface dataset:
Hanmoji: What Chinese Characters and Emoji Reveal About Each Other

Chinese characters predate the introduction of digital emoji by approximately 3,000 years. Despite the temporal gap, there are striking parallels between the canonical set of Chinese radicals and the set of emoji that have currently been approved by the Unicode Consortium. Comparing the 214 Kangxi Chinese radicals with the 3,019 emojis in the Unicode 12.0 set can reveal semantic gaps and provide directions for new emoji. Our analysis found that 72.4% of radicals have reasonable emoji equivalents, while only 17.8% of radicals lack any emoji equivalent that we could determine.
Medical image segmentation by combing the local, global enhancement, and active contour model

The objects in the medical images are not visible due to low contrast and the noise. In general, X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) images are often affected by blurriness, lack of contrast, which are very important for the accuracy of medical diagnosis. It is difficult to segmentation in such case without losing the details of the objects. The goal of image enhancement is to improve certain details of an image and to improve its visual quality. So, image enhancement technology is one of the key procedures in image segmentation for medical imaging. This article presents a two-stage approach, combining novel and traditional algorithms, for the enhancement and segmentation of images of bones obtained from CT. The first stage is a new combined local and global transform domain-based image enhancement algorithm. The basic idea of using local alfa-rooting method is to apply it to different disjoint blocks of different sizes. We used image enhancement non-reference quality measure for optimization alfa-rooting parameters. The second stage applies the modified active contour method based on an anisotropic gradient. The simulation results of the proposed algorithm are compared with other state-of-the-art segmentation methods, and its superiority in the presence of noise and blurred edges on the database of CT images is illustrated.
Problems of Developing Self-Reliance in Learning in Primary School Children

The newly introduced state standard for primary education implies the formation of self-reliance in primary school children. However, this task is not an easy one to fulfill. Developing self-reliant learners can be challenging due to a number of fundamental contradictions such as the ones between the freedom offered to the child and the forced nature of some education methods; between the new educational tasks and the established school practices; between the invariant character of training standards and variability of individual learning features of schoolchildren. Overcoming these contradictions requires not only the formation of new teaching competencies in future teachers, but a fundamental change in the teacher’s professional position. It’s not enough to simply improve several means and methods of teaching, it’s important to develop and apply radically new education technologies: create an active and versatile educational environment, initiate mediated management of child learning and development, introduce individualization on the basis of personal activities of children, carry out pedagogical observations of every individual’s development. A lesson called “Self-taught learner”can be considered an example of such active and variable educational environment that promotes self-reliance in learning in children. In this lesson, every child masters some new learning content on their own and carries out basic learning actions step-by-step, moving along his/her individual learning route.
Adaptive Security Based on MAPE-K: A Survey

As systems evolve into interconnected heterogeneous components, their security threats increase in number and complexity, and static security measures are not capable of confronting all of them. A strategy to address this issue is the use of autonomic software, which adapts the security mechanisms at runtime according to the environmental changes that impact on the required security level. An approach to achieve autonomic computing is by using the MAPE-K reference model developed by IBM, which consists of a feedback loop composed of the functions: Monitor, Analyze, Plan, and Execute. In this manuscript, adaptive security models based on MAPE-K are surveyed, their characteristics are described, and a comparison of their domains, structures, and adaptive objectives is presented.
On Quantum Extensions of Hydrodynamic Lattice Gas Automata

We consider quantum extensions of classical hydrodynamic lattice gas models. We find that the existence of local conserved quantities strongly constrains such extensions. We find the only extensions that retain local conserved quantities correspond to changing the local encoding of a subset of the bits. These models maintain separability of the state throughout the evolution and are thus efficiently classically simulable. We then consider evolution of these models in the case where any of the bits can be encoded and measured in one of two local bases. In the case that either encoding is allowed, the models are efficiently classically simulable. In the case that both encoding and measurement is allowed in either basis, we argue that efficient classical simulation is unlikely. In particular, for classical models that are computationally universal such quantum extensions can encode Simon’s algorithm, thus presenting an obstacle to efficient classical simulation.
Research on Optimization of CAPTCHA Recognition Algorithm Based on SVM

Image verification code is the main mode of security verification in current network applications. The identification efficiency of verification code is a difficult problem that affects network data crawling, which has realistic research significance and value. This paper proposed an SVM (Support Vector Machine)-based recognition method. On the basis of fully analyzing the existing SVM recognition mechanism process, the image is first binarized and filtered for character image preprocessing, then feature extraction, and then classification model is established for recognition. It is proved that this method can achieve fast and accurate results by training and comparing characters in many categories such as adhesion and rotation.
A novel multi-view SVM based on consistent hidden density distributions between views for face recognition

Since the density distribution of each view, which can be often built only from the corresponding partial data observed along each view from the whole face data, ignores the coherent information between all the views, multi-view face recognition sometimes is seriously troubled by an unavoidable phe nomenon that the dissimilarity between the samples from the same class but different views is greater than that between the samples from the different classes of same view. In this study, by considering a common hidden space cross all the views, consistent hidden density distribution between views in the common hidden space is delved so as to address this issue. Accordingly, a novel multi-view support vector machine based on consistent hidden density distributions between views in common hidden space (2V-SVM-CHDD) is proposed for an efficient multi-view face recognition, and its theoretical convergence is also analyzed. Extensive experimental results on real face image datasets indicate the effectiveness of the proposed multi-view method.
Application of Elastic Principal Component Analysis to Person Recognition Based on Screen Gestures

Person identification based on touch screen gestures is a well-known method of authentication in mobile devices. Usually it is only checked if the user entered the correct pattern. Taking into account other biometric data based on the speed and shape of finger movements can provide higher security while the convenience of this authorisation method is not impacted. In this work the application of Sequential Joint Functional Principal Analysis (FPCA) as a dimensionality reduction method for gesture data is explored. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of gestures collected from 12 people. The effects of sampling rate on classification performance is also measured. It is shown that the Support Vector Machine classifier reaches the accuracy of 79% using features obtained using the Sequential Joint FPCA, compared to 70% in the case of Euclidean PCA.
Designing the imagery on image maps – how far can we take it?

Image maps are a popular cartographic product, as the synthesis of remote sensing imagery with cartographic symbols gives the map an authentic look and feel. Traditionally, the design of image maps considered cartographic symbols only. Recent research shows that the imagery can be designed with the same dedication. Local, object-related radiometric manipulations help highlighting or deemphasizing individual image objects. This helps the map maker to structure imagery objects, besides the symbols, into a visual hierarchy. However, the design capabilities of image objects are limited as the naturalistic look of the imagery has to be maintained. This work examines how many visual levels can be introduced to the imagery. Based on research addressing the variation length of visual variables, the concept transition to imagery is discussed before an empirical study evaluates the boundaries of image map design. The empirical results reveal that the design of image objects is more limited than with cartographic symbols as less than half of the subjects could correctly assign a six-layer visual hierarchy. The results indicate better results for distinguishing less imagery layers, while the study also shows very contrasting assigning performances of test persons.
Spherical Superpixels: Benchmark and Evaluation

Although a variety of superpixel algorithms have been developed and adopted as elementary tools in low-level computer vision and multimedia applications, most of them are designed for planar images. The quick growth of spherical panoramic images raises the urgent need of spherical superpixel algorithms and also a unifying benchmark of spherical image segmentation for the quantitative evaluation. In this paper, we present a general framework to establish spherical superpixel algorithms by extending planar counterparts, under which two spherical superpixel algorithms are developed. Furthermore, we propose the first segmentation benchmark of real-captured spherical images, which are manually annotated via a three-stage process. We use this benchmark to evaluate eight algorithms, including four spherical ones and the four corresponding planar ones, and discuss the results with respect to quantitative segmentation quality, runtime as well as visual quality.
High-quality object-space dynamic ambient occlusion for characters using Bi-level regression

The widely used ambient occlusion (AO) technique provides an approximation of some global illumination effects and is efficient enough for use in real-time applications. Because it relies on computing the visibility from each point on a surface, AO computation is expensive for dynamically deforming objects, such as characters in particular. In this paper, we describe an algorithm for producing high-quality dynamically changing AO for characters. Our fundamental idea is to factorize the AO computation into a coarse-scale component in which visibility is determined by approximating spheres, and a fine-scale component that leverages a skinning-like algorithm for efficiency, with both components trained in a regression against ground-truth AO values. The resulting algorithm accommodates interactions with external objects and generalizes without requiring carefully constructed training data. Extensive comparisons illustrate the capabilities and advantages of our algorithm.
On the contribution of the voice texture for speech spoofing detection

Automatic speech verification (ASV) consists in the implementation of automatic algorithm to measure and asses human biometric parameters. Serious vulnerabilities are emphasized concerning spoofing attacks. In this paper, we propose to investigate the possible contributions of voice textures to detect spoofing attack. Voice texture is a recent concept of voices characterization using an overall sound homogeneity. Well known spoofing attacks are based on text to speech (TTS), voice conversion (VC) and replay techniques. According to the nature itself of the spoofed voices, their textures are different from those of genuine speeches. The concept of the texture is well developed in the context of image processing. Local binary patterns (LBP) is one of the famous visual descriptor of the images. LBP was adapted to be used as speech texture descriptor. LBP coding is applied to all input genuine and spoofed signals. after that, histogram of the LBP descriptors is constructed and is used as features. Support Vector Machines (SVM) classifier is used to classify the obtained features as genuine or spoofed. From the experimental results, it is observed that the proposed method could increase the difference between genuine and spoofed speech.
A Method for Automatically Generating Schema Diagrams for OWL Ontologies

Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.
Relations Between Secret Sharing and Secret Image Sharing

Secret sharing (SS) for (k, n) threshold generates secret data into n shadows, where any k or more shadows can reconstruct the secret while any $$k-1$$ or less shadows reconstruct nothing of the secret. SS is useful for cloud computing security, block chain security and so on. Since nowadays image covers more information, secret image sharing (SIS) is studied widely. Although most SIS principles are derived from SS and SIS belongs to SS, SIS has its specific features comparing to SS due to image characteristics, such as, pixel value range, region relationship and so on. In this paper, first we discuss the relations between SIS and SS, where differences between them are mainly considered. Then, some typical sharing principles are employed to further indicate the differences and analyze the possible ways to deal with the issues when directly applying SS to SIS. Finally, we perform experiments to verify our analyses.
Framework for Assessing Professional Growth of Software Developers

The distribution of issues and tasks among software developers in IT companies is important project manager problem. In the paper a JSM model is proposed based on professional growth index of software developers. Special questionnaire, framework and software tools are presented for calculating the professional growth index of software developers. The discussion of usage and example of calculating of professional growth index are presented.
An approch for FPGA implementation of LST-SW algorithm

Satellite remote sensing (SRS) needs to make use of up-to-date technology.The trend in SRS missions has always been towards using hardware devices with smaller size, lower cost, more flexibility, and higher computational power.Therefore, Field Programmable Gate Array (FPGA) technology is highly used by the scientific community and hardware developers to implement different SRS algorithms.Thus, FPGAs offer well-suited architectural features; large number of programmable logic elements, distributed and block RAMs, DSP and register slices and look up tables.This paper describes an approach to the implementation of the Land Surface Temperature Split-Window (LST-SW) algorithm structure based on FPGA technology.
A scheme on converting quantum deniable authentication into universal quantum designated verifier signature

The deniable authentication and designated verifier signature protocol can achieve the property of deniability by a transcript simulation algorithm, so two protocols are similar in the concepts and application scenarios. In this paper, we will avoid the research ideas of classical cryptography and research how to convert quantum deniable authentication protocol into quantum designated verifier signature in this universal case that the owner of the signature and the signer isn’t the same user in quantum designated verifier signature scheme such as electronic ID cards and e-passports. A mode on converting quantum deniable authentication into quantum designated verifier signature is firstly proposed by introducing quantum signature with public verifiability, including the entities and the implementation process converting, and then a real quantum designated verifier signature scheme is proposed, which satisfies all security requirements of a designated verifier signature scheme and can withstand intercept-resend attack and impersonation attack and entangle-measure attack.
Unified and Scalable Digit-Serial Systolic Array for Multiplication and Division Over GF (2m)

This brief offers a new unified and scalable digit-serial systolic array structure to implement the unified Stein's multiplication and division algorithm. The proposed structure is flexible enough to help the designer select the required number of processing elements and manage the latency of the multiplication/division operations. Thus, the proposed design can realize the required time performance with minimum space complexity. The implementation results of the proposed design and the previously reported digit-serial competitor designs display that the proposed scalable architecture has better performance for 32-bit embedded cryptographic processors that need reasonable performance with a small footprint.
Control points searching algorithm for autonomous mobile robot navigation with obstacle avoidance

Path planning is an important task in autonomous navigation. It aims to determinate the suitable path that allows the robot to reach the goal configuration safely and in short time. This manuscript investigates the navigation strategy for the wheeled mobile robot in known environment containing static obstacles. The approach is based on the evaluation of the visibility condition which handles if there is an intersection between the planned path and obstacles. Then, control points are determined to facilitate the construction of the safe path. Furthermore, a sliding mode controller is used for tracking the planned trajectory. At last, simulation results are presented to show the efficiency of the developed strategy.
Comparative Analysis of Chaotic Variant of Firefly Algorithm, Flower Pollination Algorithm and Dragonfly Algorithm for High Dimension Non-linear Test Functions

Non-linear test functions are NP-Class problems. To solve them, Swarm Algorithms (SA) have been used in last two decades very effectively. In this work, three swarm based algorithms (i.e. Firefly Algorithm (FFA); Flower Pollination Algorithm (FPA) and Dragonfly Algorithm (DA)) have been used. Chaos is familiarized with swarm algorithm to improve their performance. As per our knowledge, most of the studies have applied chaos on one standard SA and compared it with other standard algorithm(s). No comparison has been shown among the chaotic variant of different algorithms. Comparison of Chaotic variants of FFA, FPA & DA with their standard algorithms has been performed using four high dimensions non-linear test functions on the basis of Mean fitness (i.e. P1) and convergence rate (i.e. P2). The results indicate that chaotic variant has performed better than standard and FFA evaluates best fitness for multi-modal function (i.e. f3 and f4).
Photovoltaic Output Prediction Model Based on Echo State Networks with Weather Type Index

In order to reduce the adverse impact of photovoltaic generation instability on power grid, a model for predicting photovoltaic output value based on Echo State Networks with weather type index was proposed. The weather type index was established by the ratio relationship between the average power generation of different weather types, and then it was used as one of the input variables of the prediction model. Compared with traditional neural network, Echo State Networks has great improvement in stability, global optimality, local minimum problem, convergence speed and complexity of training process. Furthermore, leaky integrator neuron was used to optimize the classical neuron model. It enhanced the short-term memory ability of the reserve pool and improved the prediction ability and accuracy for time series. In the simulation experiment, using MATLAB it was verified that compared with BP neural network and classical ESN, the proposed method had higher prediction accuracy and showed better prediction effect in predicting photovoltaic power output.
IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK

Abstract. We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.
Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems

Cognitive applications that involve complex decision making such as smart lighting have non-deterministic input–output relationships, i.e., more than one output may be acceptable for a given input. We refer them as non-deterministic multiple output classification (nDMOC) problems, which are particularly difficult for machine learning (ML) algorithms to predict outcomes accurately. Evaluating ML algorithms based on commonly used metrics such as Classification Accuracy (CA) is not appropriate. In a batch setting, Relevance Score (RS) was proposed as a better alternative, which determines how relevant a predicted output is to a given context. We introduce two variants of RS to evaluate ML algorithms in an online setting. Furthermore, we evaluate the algorithms using different metrics for two datasets that have non-deterministic input–output relationships. We show that instance-based learning provides superior RS performance and the RS performance keeps improving with an increase in the number of observed samples, even after the CA performance has converged to its maximum. This is a crucial result as it illustrates that RS is able to capture the performance of ML algorithms in the context of nDMOC problems while CA cannot.
Polygamy Inequalities for Qubit Systems

Entanglement polygamy, like entanglement monogamy, is a fundamental property of multipartite quantum states. We investigate the polygamy relations related to the concurrence $C$ and the entanglement of formation $E$ for general $n$-qubit states. We extend the results in [Phys. Rev. A 90, 024304 (2014)] from the parameter region $\alpha\leq0$ to $\alpha\leq\alpha_0$, where $0<\alpha_0\leq2$ for $C$, and $0<\alpha_0\leq\sqrt{2}$ for $E$.