Reviewerly/exHarmony huggingface dataset:
Efficient and Flexible Sublabel-Accurate Energy Minimization

We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recognition. While discrete optimization methods are able to give theoretical optimality guarantees, they can only handle a finite number of labels and therefore suffer from label discretization bias. Existing continuous optimization methods can find sublabel-accurate solutions, but they are not efficient for large label spaces. In this work, we propose an efficient sublabel-accurate method that utilizes the best properties of both continuous and discrete models. We separate the problem into two sequential steps: (i) global discrete optimization for selecting the label range, and (ii) efficient continuous sublabel-accurate local refinement of a convex approximation of the energy function in the chosen range. Doing so allows us to achieve a boost in time and memory efficiency while practically keeping the accuracy at the same level as continuous convex relaxation methods, and in addition, providing theoretical optimality guarantees at the level of discrete methods. Finally, we show the flexibility of the proposed approach to general pairwise smoothness terms, so that it is applicable to a wide range of regularizations. Experiments on the illustrating example of the image denoising problem demonstrate the properties of the proposed method. The code reproducing experiments is available on https://github.com/nurlanov-zh/sublabel-accurate-alpha-expansion.
TMCR: A Twin Matching Networks for Chinese Scene Text Retrieval

In this paper, we focus on a critical task of retrieving common style in Chinese scene text: given an image of style text, the system returns all the images matching the queried text image. To that, a novel twin Transformer based matching network is proposed, which is featured by the integration of anchor-free detection, text recognition, and similarity matching networks. On the fly, our model retrieves the similarity of text features in the text area and evaluates it through recognition. Our experiments demonstrate that the proposed model outperforms the state-of-the-art in terms of both processing speed and accuracy. Additional experiments show that our model generalizes well on various benchmarks, including a self-constructed Chinese query data set with complex Chinese scenes in the real world.
Acceptance of Virtual Reality Games

As virtual reality (VR) games are getting more widespread, the need to understand the interaction between players and the VR games is gaining prominence. The present study examines player endorsement of virtual reality games from an amalgamation of technology acceptance, self-determination, and flow theory perspectives. A survey was carried out with participants (N = 396) who had played a VR game at least once and at most five times. Structural equation modeling analyses showed that perceived ease of use was the primary predictor for satisfaction of self-determination constructs (autonomy and competence) and flow constructs (immersion and concentration), which in turn predicted player enjoyment. Accordingly, the results suggest the importance of including self-determination constructs in addition to the flow perspective within the context of technology acceptance model, for explaining the acceptance of VR gaming. Findings also showed that enjoyment resulted in positive attitudes towards VR gaming, and these attitudes predicted intention to play VR games.
Modeling of Verb Phrases of the Kazakh Language

Formalization and modeling are an integral part to successful natural language processing. This article studies the formalization and modeling of verb phrases of the Kazakh language on the example of Verb government involving ontology. For the linguistic annotation of the concepts of the Kazakh language grammar, the UniTurk metalanguage was used. Ontological models of verb phrases of the Kazakh language are built in the Protégé environment.
STYLISTIC FEATURES OF MODERN SPANISH ADVERTISING DISCOURSE IN THE INTERNET SPACE

The article is devoted to the description of stylistic features of modern Spanish advertising discourse in the Internet space. For linguistic science, the interest in advertising discourse is mainly due to the general direction of the study of language as a tool of influence. Stylistic techniques and rhetorical figures contribute to achieving a communicative effect in advertising discourse, evoke emotions in recipients and create new convictions. Figures of speech play an important role in creating the ad copy or ad copy. It creates an advertising image for the product. The copywriter must choose the words so that they correctly affect the consumer's imagination, revealing some new aspects of the advertised object. The role of stylistics in advertising discourse is studied. The concept of the slogan is specified and its function in advertising is analyzed. The language of advertising messages is distinguished by its extreme concretization and semantic primitivism. The connotations are made present through the use of the most varied means. Rhetorical questions are quite frequent as a specialized means of influencing speech with a special expressive potential. The persuasive effect is determined by sentences containing syntactic constructions with an imperative mood.
Comparing Presence, Workload, and Performance in Desktop and Virtual Reality Flight Simulations

Virtual reality (VR) offers a training environment that promotes increased learning and performance. However, to what extent VR flight simulations offer increased performance compared to less-immersive simulators is not clear, and neither are their underlying cognitive aspects. In a within-subject experiment, we compared fight performance and subjective measures of workload, presence, and engagement in two flight training environments (Desktop and VR) on two flight tasks of equal difficulty (changing speed and performing turns). No differences were observed in the flight performance between these conditions. However, participants reported higher presence and engagement in the VR training environment. Additionally, we found a correlation between subjective workload and flight performance in the Desktop condition, but not in VR. We conclude that VR is promising for basic flight training tasks and encourage future work to explore this further by investigating neurophysiological indices of workload and engagement and establishing relationships between presence, workload, and flight performance.
Exploring Chinese College Students' Awareness of Information Security in the COVID-19 Era

Abstract The focus of this research was to look into the information security awareness of Chinese university students during the COVID-19 pandemic and make recommendations based on the survey's findings and research literature. The quantitative method is applied in this study. 111 Chinese college students were randomly sampled and requested to answer a Likert information security awareness questionnaire. The descriptive analysis of the data in this study is also done with SPSS. The findings revealed that the vast majority of college students know the significance of information security awareness and have basic information security awareness, based on the data collected. However, some students have not participated in relevant training courses, and many college students do not pay enough attention to personal information security, resulting in poor performance in areas such as files and passwords. As a result, effective solutions such as information security training projects are required to address the current deficiencies. The findings of this study have implications for university administrators and policymakers in terms of how to raise students' awareness of the security of their online learning information. The study should use mixed methods and large sample sizes in the future to provide more detailed and comprehensive survey data, and more credible evidence of college students' information security awareness.
Sentiment Analysis Using Novel Machine Learning

Before purchasing a product or service, People typically analyse the information about the product such as cost, warranty, quality etc. Only after getting satisfaction about such things, People try to buy that product based on the quality of service received. Since this process takes time and a chance of being duped by the dealer are higher, Sentiment analysis (SA) is necessary to purchase a product without any hesitation. Sentiment analysis examines reviews and comments of the products, which are in the form of text that requires several processes for providing the desirable information to the People. Moreover, Furthermore, SA is an important Natural Language Processing (NLP) research direction. In this paper, a novel sentiment analysis model is developed based on the Machine Learning (ML) Algorithm, which provides an accurate sentiment information for the texts having different perspectives. The method of Stop words are used for data pre-processing. By using count vectorizer, the text data is converted into the form of vectors for extracting the desired features. Finally, the type of sentiment whether it is positive, negative or neutral is determined based on the ML classifier namely, Random Forest (RF) classifier. This model is developed using novel ML approach that provides an accurate sentiment classification to the people or the industries who need the sentiment analysis.
Capturing Global and Local Information in Remote Sensing Visual Question Answering

Presently, many researches on remote sensing imagery aim to gather the image content but ignore the interaction with the images. The remote sensing visual question answering system automatically gives an answer based on the content of the input image and related questions involved in the remote sensing applications. While global scenes and salient local objects are two imperative parts in most applications of remote sensing, this motivated us to design a model that can understand the scale changes from global scenes to local targets in remote sensing imagery on vision question answering. In this paper, a new model Global-Local Visual Question Answer (GLVQA) and a dataset, which includes global and local question-answer pairs of remote sensing images, are created. Furthermore, GLVQA integrates features from different scales to improve the performance on answering global or local questions. From the evaluating results on our GLVQA dataset, GLVQA achieved a validation accuracy of 83.6%, which means our method has great potential in remote sensing VQA applications.
Using incomplete polynomial functions of the odd degree n and their inverses for data encryption and decryption

The need and the demand to hide the content of written messages from prying eyes has arisen as long as writing itself has existed. Particularly, recent times the protection of information is considered to be quite crucial due to the increased use and development of streaming applications. Therefore, the urging of having more sophisticated, stronger and hard to break data encryption and decryption systems is increasing. In order to attain these requirements, cryptography plays an important role, where many researchers have come up with different proposals and developed algorithms that have helped out a little in ensuring the confidentiality, integrity and authentication of the given information. However, even the internet security through modern cryptography is quite complex and depends on the difficulty of certain computational problems in mathematics. Modern systems of encryption are based on complex mathematical algorithms and carry out a combination of symmetric and asymmetric key encryption schemes to secure communication. For those a significant background in algebra, number theory and geometry is required. In this paper a new cryptosystem is presented, which is based on compound commutative functions such as exponential and logarithmic functions. After the necessary theoretical deliberation in this paper, we will provide the encryption and decryption approach accompanied with relevant examples.
Tensor Based Multivariate Polynomial Modulo Multiplier for Cryptographic Applications

Modulo polynomial multiplication is an essential mathematical operation in the area of finite field arithmetic. Polynomial functions can be represented as tensors, which can be utilized as basic building blocks for various lattice-based post-quantum cryptography schemes. This paper presents a tensor-based novel modulo multiplication method for multivariate polynomials over <inline-formula><tex-math notation="LaTeX">$GF(2^{m})$</tex-math></inline-formula> and is realized on the hardware platform (FPGA). The proposed method consumes <inline-formula><tex-math notation="LaTeX">$6.5\times$</tex-math></inline-formula> less power and achieves more than <inline-formula><tex-math notation="LaTeX">$6\times$</tex-math></inline-formula> speedup compared to other contemporary single variable polynomial multiplication implementations. Our method is embarrassingly parallel and easily scalable for multivariate polynomials. Polynomial functions of nine variables, where each variable is of degree 128, are tested with the proposed multiplier, and its corresponding area, power, and power-delay-area product (PDAP) are presented. The computational complexity of single variable and multivariate polynomial multiplications are <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$O(np)$</tex-math></inline-formula> , respectively, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the maximum degree of a polynomial having <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> variables. Due to its high speed, low latency, and scalability, the proposed modulo multiplier can be used in a wide range of applications.
Cloud Architecture-based Multi-agent Platform for Matching in Resource Sharing

This paper presents a cloud platform dedicated to industrial resource sharing between companies. The interactions between these companies are supervised by a multi-agent system and the management of resources is made with the help of an Internet of Things network and a consortium blockchain platform. In this paper, we only describe the matching process by introducing the agents and their collaboration. The matching problem consists of the allocation of resources offered by a company to several companies on the basis of costs minimization and the adequacy between demand and supply for each actor and the level of service maximization. A numerical example is proposed to highlight our proposal.
Evaluating Modifying Teacher Avatar Clip Sequencing Based on Eye-Tracked Visual Attention in Educational VR

We evaluate a novel virtual reality (VR) avatar animation architecture that is responsive to a viewer's eye gaze and we demonstrate that it can be used to improve teacher agents that are based on the playback of a recorded teacher. Our architecture modifies the playing, seeking, rewinding, stopping, and pausing of a teacher recording to create appropriate teacher avatar behavior based on a viewer's eye-tracked visual attention. We present preliminary results from our experiment that evaluated how subjects perceived three different teacher avatar behavioral responses.
Classifying Facial Regions for Face Hallucination

Recently, convolutional neural networks (CNNs) have dominated the face hallucination task due to their powerful feature representation capability. However, most of them simply use the same weights to treat different facial regions without considering the reconstruction difficulty of different facial regions, resulting in the component regions (e.g., eyes, nose, mouth) of the reconstructed faces tending to be blurred. In this paper, we propose a novel facial region classification network (FRCN) to address this problem. The proposed method first divides the input low-resolution (LR) facial image into several patch blocks, then classifies them into three categories according to their reconstruction difficulty, and finally inputs the three types of patch blocks into three networks with different weights for reconstruction and combining, thereby recovering high-quality high-resolution (HR) facial image. Experimental results show that FRCN can remarkably improve face reconstruction's performance.
Element Information Enhancement for Diagram Question Answering with Synthetic Data

Unlike natural pictures, diagrams are a highly abstract vehicle for knowledge representation, and Diagram Question Answering involves complex reasoning processes such as diagram element detection. However, due to low resource constraints, achieving efficient extraction of diagram elements is challenging. In addition, vision tasks rely on image feature extraction, and most feature extraction today is based on real scenario images on ImageNet. To solve the above problems, we programmatically synthesized a diagram dataset to implement diagram element prediction and put its feature extraction module to use on downstream task. In the actual task, we explicitly input the predicted image elements from the diagram into the model. The experimental comparison shows a significant improvement in our model compared to the baseline.
TD3-based Algorithm for Node Selection on Multi-tier Federated Learning

Federated learning enables distributed devices to conduct cooperative training models while protecting data privacy, so it is widely promoted in big data scenario and the scope of the Internet of Things. Federated learning in multi-tier computing can integrate the resources of the device-edge-fog-cloud layer to interact and cooperate. For example, in addition to offloading training locally, the tasks of the device layer can also be uploaded to the edge layer or the fog layer for training, while the global aggregation node can be selected at the edge or fog or cloud. However, due to the uncertainty of network bandwidth, computing resources and terminal training tasks at each layer, it brings challenges to resource allocation and task offloading under federated learning in multi-tier computing. Therefore, we propose a TD3-based algorithm which aims to solve how to select training nodes and aggregation nodes during joint training on multi-tier federated learning to minimize the average task delay. Numerical experiments show that our method has better performance in terms of energy consumption and delay compared with edge federated learning and traditional federated learning.
A Method of Cell Counting Based on Computer Vision

For a long time, making machines have the ability of pattern recognition is the direction of scientists' continuous research. The study of pattern recognition is the fundamental way to understand the essence of artificial intelligence. At present, computer vision has been used to identify and count cells. In view of the problems existing in the field of cell counting, such as difficulties in identifying cells, dense cells, viscosity, difficult cell counting, inaccurate judgment and so on, this paper realizes the identification and counting through the technology of computer vision, determines the types of cells, and explores the characteristics of dense and sticky cells, At the same time, based on the characteristics of dense and sticky cells, a color based threshold segmentation algorithm is introduced to optimize the performance of cell classification. At the same time, the recognition and classification of different regions of adhesive cells are carried out, and the adhesive cells in different regions are recognized and judged by the technology of computer vision. Through gray-scale transformation, threshold segmentation, morphological processing and LOG algorithm filtering of medical cell image, the cell region is extracted and counted. The cell counting is realized by calculating the connected domain. The experimental results preliminarily verify the effectiveness of this method, but it still needs to be improved.
Tools for Machine Learning

In this section we shall describe one of the cornerstones of modern machine learning, namely, the regression model. It was first introduced over 100 years ago as a statistical method for discovering the existence and extent of the relationships between a response variable and several explanatory variables also called predictors.
Inviolable e-Question paper via QR code Watermarking and Visual Cryptography

Recurrent allegations of electronic question paper (e-QP) leakage and examination malpractices in educational institutions can be handled by the course faculty (F) securely transferring the e-QP data to a trustworthy collector (C). In this case, the data is a mix of run length encoding (RLE) for the binarized diagrams and tamper-resistant e-QP question text encrypted with a data modification technique. QR codes are being utilised to encode this combination for the two halves of the secret e-QP data. Digital watermarking is being used to embed the QR code for 1 e-QP chunk in the same for the second. Its n secret image shares are generated by F through deterministic XOR based (n, n)-Visual Cryptography (VC). These are submitted to n non-trusted parties which deliver their respective portions to C before the examination. C performs all the necessary operations to get back the full e-QP data in its raw form. The resulting image RLE in plaintext format is utilised to decompress the binarized pictures in e-QP. This paper, therefore, suggests and explores the usage of QR code watermarking and VC in the construction of a secure examination cryptosystem that is robust towards ordinary watermarking and other attacks.
Segmented–Original Image Pairs to Facilitate Feature Extraction in Deep Learning Models

Researchers in the field of computer vision have been working on improving the extraction of foreground features. We use Deeplab-V3plus to obtain 300,000 background-removed images (segmented images). However, a model trained on segmented images has a far lower recognition rate for original images, and vice versa. The visualization results of t-SNE further show that the features of the segmented and original images are very different. Because deep learning is based on the assumption that data are identically distributed, the recognition rate is low in these cases. In automatic speech recognition, pure data is readily available, and researchers add noise to the data to improve the robustness of the model. Inspired by this, we use segmented–original image pairs in which the images guide each other. According to our mathematical proof, the features learned by the model are controllable and located in the feature space between the segmented and original image. Experiments further verify our intuition that segmented–original image pairs facilitate feature extraction in deep learning models.
The adaptive crossover operator for the traveling salesman problem

Crossover operators have a significant influence on the success of genetic algorithms (GAs). However, a single crossover operator may not be suitable for all instances and different search stages. The basic various crossover operators in the context of the traveling salesman problem (TSP) are discussed. Moreover, a learning-based mechanism for crossover selection is mentioned. The results of experimental comparison show that the application of learning based crossover selection mechanism enables to achieve better solutions than adopting only a single crossover.