Reviewerly/exHarmony huggingface dataset:
Concept, signs, legal regime of virtual museums

The article explores the concept, features, legal regime of virtual museums. The historical stages of development and formation of virtual museums are analyzed. For the concept of the logic of constructing the definition of "virtual museum", identifying its constructive features, some of the definitions existing in the doctrine are systematized in chronological order, and an analysis of the frequency of use of semantic units by the authors is carried out. The possibility of applying the legal regime of the database, multimedia works in relation to virtual museums is considered. The author singled out and studied the following features of a virtual museum: complexity, the presence of a computer program (original program code) in the structure, virtuality, interactivity, digital form of expression. The paper analyzes and systematizes the theoretical positions on the research topic, studied doctrinal approaches. Based on the results of the study, the author's definition of the concept of "virtual museum" is proposed.
Application to demonstrate Client-Side Vulnerabilities in Capture-The-Flag Competitions

Capture-The-Flag is a cybersecurity competition that has intentionally-vulnerable programs for players to exploit. For Client-Side Web-Exploitation challenges, the exploitation happens on the victim's browser and victim interaction has to be automated. Here, an Open Source application is made that can be used in CTFs to automate the process for multiple challenges. The app supports browsers like Chrome and Firefox. It requires to be deployed only once to be used for all challenges requiring user interaction. It also has anti-dos features.
Probing Mean Values and Correlations of High-Spin Systems on a Quantum Computer

We consider simulation of the high spins on a quantum computer. The protocols which allow one to measure the mean value of spin and correlations between spins are proposed. As a result, we determine the time-dependence of the mean values of spin-1 in the magnetic field prepared on the ibmq-santiago quantum computer. In addition, we study the evolution of two interacting spins on the ibmq-santiago quantum computer. The time-dependencies of mean value of spin-1 and correlations between these spins are detected. Finally, we generalize these protocols for the spins of an arbitrary values.
Blockchain technology in intelligent transportation systems

Consistently developing incorporation of connected vehicle innovations into intelligent traffic signal control frameworks achieves huge information security issues in the associated vehicular networks. This chapter presents a novel decentralized and secure by plan design for associated vehicle data security, which depends on the arising blockchain worldview. Blockchain, broadly known as one of the disruptive technologies that arose lately, is encountering a fast turn of events and has the maximum capacity of altering the inexorably centralized intelligent transportation system (ITS) in applications. Blockchain can be used to build up a secure, trusted, and decentralized self-ruling ITS environment, making better use of the heritage ITS framework and assets, particularly successful for publicly supporting innovation. This chapter leads a primer investigation of blockchain-based ITS (B 2 ITS). We outline an ITS-oriented, seven-layer calculated model for blockchain, and on this premise we address the key exploration issues in B 2 ITS. We consider that blockchain is one of the got and confided-in structures for building the recently evolved Parallel Transportation Management and Control System (PTMS), and this way we examine the connection between B 2 ITS and PTMS. Finally, we present contextual research for blockchain-based dynamic resource-sharing organizations. In our perspective, B 2 ITS addresses the future pattern of ITS exploration and practice, and this paper is pointed toward invigorating further exertion and giving accommodating direction and reference to future examination works.
Measuring Programming Ability for Novice Programmers

&lt;p&gt;Coding is a key activity in the software development process and a programmer&amp;rsquo;s programming ability determines the software quality. Different from professional programmers, novice programmers usually refers to programmers who have learned a programming language for about three years. At this stage, measuring their programming ability is of great significance to improve their programming abilities. In previous work, researchers have proposed a variety of ways to measure programming ability for professional programmers. We set out to find out the best way to measure novice programming ability. We first exacted a questionnaire from published comprehension experiments for measuring programming ability. Then, we performed control experiments to compare the answers to the questionnaire with their performance. We found that module number and the number of programming-related websites visited seem to be a reliable way to measure programming ability for novice programmers. Furthermore, we perform exploratory factor analysis to generate a model to verify the effectiveness of our findings.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Simplicity and the Sub-Family Problem for Model Selection

Abstract Forster and Sober (1994) introduced the “sub-family problem” for model selection criteria that recommend balancing goodness-of-fit against simplicity. This problem arises when a maximally simple model (family of hypotheses) is artificially constructed to have excellent fit with the data. We argue that the problem arises because of a violation of the general maxim that balancing goodness-of-fit against simplicity leads to desirable inferences only if one is comparing models for the consideration of which one has a positive reason independently of the current data.
Application of Watson Visual Perception Model in Nanchang VI Visual Image Recognition Design

To shape a complete city image, it is necessary to find the first characteristic of the city so as to further improve the easy identification of the city image, create a good city image, and make the city more competitive. This paper combines the Watson visual perception model to carry out the visual image recognition design of Nanchang VI to improve the communication effect of the urban VI visual image. Moreover, this paper proposes a video watermarking algorithm based on MPEG-4 encoding using the open-source Xvid codec. In addition, this paper proves that the proposed algorithm has good application value in imperceptibility and robustness through a large number of experiments and data analysis. Finally, this paper verifies the reliability of the method proposed in this paper through the study of multiple sets of data.
A method of detecting run-on essays based on the degree of tangency

This study proposes a method to detect whether an essay is off-topic or not based on the degree of tangency, because of the lack of accurate and efficient algorithms for off-topic detection in domestic essay-assisted grading systems. This study uses the LDA topic model to extract the topic in the set of topic requirements and reference essays and extracts the topic words of the essays to be evaluated, based on which the proposed method of calculating the topic degree is used to calculate the topic degree, to measure the relevance of the essays to the topic, and finally filter the runaway essays by setting a reasonable threshold. The experimental results show that this method is more effective than the traditional TF-IDF algorithm based on the vector space model and can detect more run-on essays, and the accuracy rate is higher, with the F value reaching over 90%, which achieves the intelligent processing of essay run-on detection and can be effectively applied in English composition teaching.
Pixel-level end-to-end dual-channel bill text detection based algorithm

Existing scene text detection algorithms do not fully utilize high-level semantic and spatial information, which limits the model's ability to classify complex background pixels and to detect and localize text instances at different scales, and the upsampling process is time-costly. To solve the above problems, this paper proposes a double-channel bill text detection (DBTD) model. The model consists of two parts: spatial information path (SP) and semantic information path (SE). SP can retain rich spatial details and SE can provide a large reception domain. Based on these two paths, a new feature fusion module (FFM) is introduced to effectively fuse features. A comparison with multiple models is performed on the ICDAR2015 dataset, and results show that the proposed model has the best detection performance, and F1 is improved to 79.8%.
Out of Sight, but Not Out of Mind: A Diagrammatic Conversation on Relational Drawing

Abstract The authors reflect on their ongoing dialogue about connections between drawing in the practice of art and in the practice of mathematics. They show how visualizing relational connections in mathematical diagrams can be used to contextualize aspects of Heath’s drawings. The account provides a sequence of loops, reflecting on drawings where relational marks coexist with those in which they do not. Diagrams are the vehicle for this dialogue across the disciplines of art and science, acting as boundary objects supported by a common visual vocabulary of loops, connections, change over time, negative space, and a shared interest in exploring ideas through the physicality of practice.
Photovoltaic power prediction based on improved moth-flame optimization algorithm and extreme learning machine

Accurate photovoltaic power forecasting is a direct method to deal with the uncertainty of photovoltaic power generation. Therefore, this paper proposed a photovoltaic power prediction model. Firstly, in order to improve the optimization performance of the moth-flame optimization (MFO) algorithm, the differential mutation operator was introduced into the MFO algorithm, and the improved MFO (IMFO) algorithm was proposed. Secondly, the IMFO algorithm was applied to determine the optimal parameters of extreme learning machine (ELM), and the photovoltaic power prediction model based on IMFO-ELM was established. Finally, the photovoltaic dataset provided by Desert Knowledge Australia Solar Center was applied to test the prediction performance of the IMFO-ELM model. The results showed that the proposed model accurately predicts the photovoltaic power prediction and control the mean absolute percentage error below 5%.
Area Efficient Multiplier-less 2D DWT Architecture using Kogge Stone Adder

For the utilization of transform in rapid/low force applications, there ought to be high effectiveness and low equipment cost is required. 2D DWT has made out of multi rate channels. It deteriorates the signal with time and frequency area in various sub-groups. Likewise Fast design presents rapid preferred position in it and quantities of levels of deteriorations are expanded. So there is high calculation capacity in it that is improving the usage of equipment. In Convolution based or customary strategy transform, there is filtering of information signal into low pass and high pass channel sub-groups with the utilization of low pass and high pass channels. At that point these low pass and high pass sub bands are sub inspected where the image is recuperated from it. So essentially it utilizes more power and an excessive number of calculations with which speed is decreased in these kinds of transform. In this paper, multiplier-less 2D DWT is implemented using KSA. Multiplier-less technique is consisting of I/P shift register unit, Look up table (LUT) and shift accumulator. KSA is a parallel prefix form of the carry look ahead adder. This design is implemented Xilinx 14.2i and simulated slice, delay, memory and LUT.
Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation

Despite its importance, unsupervised domain adaptation (UDA) on LiDAR semantic segmentation is a task that has not received much attention from the research community. Only recently, a completion-based 3 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> method has been proposed to tackle the problem and formally set up the adaptive scenarios. However, the proposed pipeline is complex, voxel-based and requires multi-stage inference, which inhibits it for real-time inference. We propose a range image-based, effective and efficient method for solving UDA on LiDAR segmentation. The method exploits class prototypes from the source domain to pseudo label target domain pixels, which is a research direction showing good performance in UDA for natural image semantic segmentation. Applying such approaches to LiDAR scans has not been considered because of the severe domain shift and lack of pre-trained feature extractor that is unavailable in the LiDAR segmentation setup. However, we show that proper strategies, including reconstruction-based pre-training, enhanced prototypes, and selective pseudo labeling based on distance to prototypes, is sufficient enough to enable the use of prototypical approaches. We evaluate the performance of our method on the recently proposed LiDAR segmentation UDA scenarios. Our method achieves remarkable performance among contemporary methods.
Research progress and trend analysis of object tracking technology based on CiteSpace

Object tracking technology is an important research direction in the field of computer vision, and nowadays, it has played an increasingly indispensable role in the fields of intelligent surveillance, modern military, human-computer interaction, intelligent transportation, etc. While making breakthroughs, it has also brought great convenience to people&#x2019;s lives. In this paper, CiteSpace information visualization software is used to visualize the object tracking research literature based on nearly 10,000 papers in the field of object tracking from 2001 to 2021. From the bibliometric perspective, the visual knowledge graphs of information on three aspects of object tracking technology: scientific cooperation, research fields, and recent progress are analyzed, in which the analysis of scientific cooperation includes the distribution of countries and institutions, the analysis of research fields includes the distribution of categories and keywords, and the analysis of recent progress and frontiers includes the distribution of references and the analysis of keyword timeline graph. Then, we present the three main problems and challenges facing this technology: changes of illumination and color, changes of scene and posture, and the distinction between foreground and background. And three targeted suggestions are proposed for feature fusion, 3D reconstruction, and deeper development based on Siamese Network in deep learning algorithms. Finally, the future development of object tracking technology has been prospected.
Multi-sphere and Multi-kernel Fuzzy Support Vector Data Description for One-class Classification

Support vector data description (SVDD) is a machine learning method for one-class classification (OCC), which is widely used in anomaly detection. However, when faced with the problem of data nonlinearity, the kernel method has difficulty in kernel parameter selection, and the lack of non-target samples completely in strict OCC tasks makes the generalization ability of the model uncertain. In addition, the nonlinearity of the data is often manifested as multi-distribution in many applications, and the data set is also at risk of being mixed with noise. Aiming at the problems above, a three-stage learning multi-sphere and multi-kernel fuzzy support vector data description (MS-MK-FSVDD) is proposed. The main idea of the method is to replace the original single spherical boundary with a set of ellipsoid boundaries that can adapt to the distribution of the data. The method first clusters the data according to the maximum posterior probability in a Gaussian mixture model (GMM). Then, for the data in each cluster, centered kernel alignment (CKA) is used to calculate the combination coefficients of the multi-kernel functions. Finally, the joint probability density function in GMM is used for weights assigning. The experiments show that MS-MK-FSVDD not only overcomes the difficulty of kernel parameter selection and enhances the anti-noise ability of the model but also improves the classification performance, especially when the data has a specific distribution.
Enriched Robust Multi-View Kernel Subspace Clustering

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.
Research Convey on Text Classification Method based on Deep Learning

Deep learning technology has been rapidly developed in recent years and has been increasingly applied in the field of text classification, and many effective and novel classification methods have emerged. The development history of text classification is introduced, the text classification problem based on deep neural networks is analyzed, the characteristics and performance of various classical classification methods are compared and summarized, and it is shown that deep neural networks are more advantageous than traditional machine learning methods in the field of text classification on the whole. The shortcomings of current deep text classification models are pointed out and future research directions prospect.
Object Pose Detection to Enable 3D Interaction from 2D Equirectangular Images in Mixed Reality Educational Settings

In this paper, we address the challenge of estimating the 6DoF pose of objects in 2D equirectangular images. This solution allows the transition to the objects’ 3D model from their current pose. In particular, it finds application in the educational use of 360° videos, where it enhances the learning experience of students by making it more engaging and immersive due to the possible interaction with 3D virtual models. We developed a general approach usable for any object and shape. The only requirement is to have an accurate CAD model, even without textures of the item, whose pose must be estimated. The developed pipeline has two main steps: vehicle segmentation from the image background and estimation of the vehicle pose. To accomplish the first task, we used deep learning methods, while for the second, we developed a 360° camera simulator in Unity to generate synthetic equirectangular images used for comparison. We conducted our tests using a miniature truck model whose CAD was at our disposal. The developed algorithm was tested using a metrological analysis applied to real data. The results showed a mean difference of 1.5° with a standard deviation of 1° from the ground truth data for rotations, and 1.4 cm with a standard deviation of 1.5 cm for translations over a research range of ±20° and ±20 cm, respectively.
Turning Eight Family Homes Into Interactive, Pervasive Playgrounds During the COVID-19 Pandemic Lockdown

This paper presents an evaluation study of how eighth families adopted, played and experienced a movement-based game system of analog and digital technologies in their homes during a pandemic lockdown. The COVID-19 pandemic locked down many countries and grounded people in their homes with social and physical implications. A game system consisting of simple, tangible technologies with modular components was designed to meet these needs. The game system was developed for the players to set up in their homes easily and, therefore, should not depend on screens or extensive physical installations. The game system comprises simple, tangible technologies such as light and music cubes, a simple mobile robot, card game challenges, and a suite of mini-games combining the elements in a variety of playful experiences. Using the technology probes methodology, the game system was packed into a suitcase and evaluated by eight families that played the game in their homes, video-recorded their sessions, wrote a final report and were (informally) interviewed afterwards. The data set presents how the families turned their ordinary everyday spaces into interactive, pervasive playgrounds encouraging social and bodily exploration and play. Furthermore, the study shows how bodily movement and social play can be promoted through different technologies that stimulate various bodily senses and incorporate them through the different game and play structures into their everyday living environments. The findings resulted in four design implications to aid designers and researchers in future work on movement-based game systems and interactive, pervasive playground design. These design implications accommodate social and bodily activities in ordinary places otherwise not pre-allocated for play or game activities.
A Temporal RDF(S) Construction Method Based on Temporal Relational Database

RDF(S) is widely used in the fields of semantic extraction, unified organization and intelligent processing of big data due to its machine understandability. Considering that classical RDF(S) can only express static semantic information, many temporal RDF models have been proposed. However, the automatic construction of temporal RDF based on different data sources, especially the temporal relational database that has been extensively researched and commercialized, is still an urgent problem to be solved. Therefore, we put forward, in this paper, a general temporal RDF(S) model, which contains a temporal RDFS layer and a temporal RDF layer. On the basis, we propose a method including mapping rules and building algorithms of extracting data from temporal relational databases and building corresponding temporal RDF(S). The method not only consider the relational semantics and constraints of the database, but also consider the temporal semantics, which can fully extract semantic information contained in the database. Finally, a prototype system named tRDB2tRDF is implemented to validate method performance. The experimental results show that our method is feasible and effective.