Reviewerly/exHarmony huggingface dataset:
Secured transmission design schemes based on chaotic synchronization and optimal high gain observers

The distinguishing feature of the proposed secured transmission design scheme is the use of chaotic nonlinear systems and optimal high gain observers for the encryption/decryption operation and their application in image cryptography. The primary concern in this work is to develop an innovative algorithm for optimizing the high gain observer performances. Therefore, the proposed strategy uses a nonlinear chaotic system in the plain-text encryption phase and the optimal high gain observer in the decryption phase with no interdependence between these two operations. Practically, the chaotic system is suitable to be used as a block cipher as it has a random nature depending on its initial conditions. In addition, it is possible to present the proposed block cipher method as an enhancement of the existing block cipher involving short keys. For the decryption, a challenging design combining a high gain observer and non-dominated sorting genetic algorithms (NSGA-II) for optimizing the observation gain is developed. Efficiency and substantial performances of the synchronization method, tested with nonlinear unified chaotic system and optimal high gain observer, are demonstrated by several numerical simulation scenarios on a prominent QR (Quick Response) code example.
Blockchain-Based Privacy-Preserving Approach Using SVML for Encrypted Smart City Data in the Era of IR 4.0

The collection of amounts of useful data from various IoT devices through machine learning (ML) techniques has been extensively applied in lots of areas of the smart city. To improve the efficiency of machine learning, people realize efficient data classification by supporting the ML model, namely, the support vector machine (SVM) model, which is used in the realization of linear classification. However, data security and data protection are not addressed in SVM classifier training from multiple entity-labeled IoT data. In the existing solution, they depend on the implicit hypothesis that learning data can be dependable collected from complex data providers, which is often not the reality. To solve the question between the above problems, in this article, we put forward a secure support vector machine—a secure SECURE SVM training scheme for protecting the privacy on encrypted IoT data based on blockchain. Firstly, a secure and dependable data encryption sharing platform is established among complex data providers by using blockchain technology, which is recorded in a distributed ledger. Secondly, the homomorphic cryptosystem is used to design secure components, and the secure polynomial multiplication is improved to design a secure SVM training algorithm, which only needs two interactions in one iteration and does not need a trusted third party. Finally, the experimental results display that the plan ensures the confidentiality of sensitive data of each data provider and the confidentiality and validity of SVM model parameters of data analysts.
Study on the Grammar System of Teaching Chinese as a Foreign Language in the Background of Information Technology

In order to improve the convenience and reliability of teaching Chinese as a foreign language, this study applies information technology to the intelligent system of teaching Chinese as a foreign language and the picture semantic recognition of teaching Chinese as a foreign language. Moreover, this paper proposes a sparse neural network SRSCNN, which shortens the training time without affecting the reconstruction effect and calculation speed, and improves the intelligent processing of teaching resources in the teaching process. When designing the overall framework of the system, a hierarchical and modular design method is adopted to make the logical structure of the whole system reasonable and the process clear. The study of the test results shows that teaching Chinese as a foreign language system based on information technology proposed in this paper has a good teaching effect and can effectively promote the reform of teaching Chinese as a foreign language.
Improved Blockchain Technology for Performance Optimization Model Design of Sports Clubs

Professional sports clubs are an important part of professional sports, and their development has a certain role in promoting the strength of China's competitive sports and the prosperity of the sports industry. However, the development of professional sports clubs in China is still facing many challenges. Among them, the key to their long-term development is to improve their financing ability and solve their financing difficulties in order to improve the performance of professional sports club. At present, research on the financing methods and financing difficulties of professional sports clubs has achieved certain results, but no systematic research on the application of blockchain technology to solve the pain points of professional sports club financing has been established. This study systematically compares the current situation and development problems of financing for professional sports clubs in China from the new perspective of combining blockchain and professional sports club financing. In addition, this article proposes a solution for the application of blockchain technology to optimize the performance of professional sports club, which has important theoretical and practical significance for promoting the development of professional sports clubs.
Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network

To generate a new ornamental image, add an image’s oil painting style information to any image while preserving the image’s semantic content. With the rapid advancement of deep learning (DL), image style transfer has become one of the most active areas of computer vision research (CV). This paper proposes an oil painting style transfer technique based on parallel convolutional neural networks to address the ineffective style transfer of locally similar regions in content images and the slow processing speed of existing methods. By incorporating Gaussian sampling and a parallelization algorithm, this method effectively transfers the style of an oil painting. The algorithm can combine the content of any image with a variety of well-known oil painting styles to create high-quality works of art. The experimental results indicate that, compared to existing methods, the proposed method can effectively reduce the style loss of the generated image, make the generated image’s overall style more uniform, and produce a more pleasing visual effect.
The Improved Cooperative Particle Swarm Optimization (ICPSO) with Dynamic Information Adjustment and Controllable Speed and Its Application in Neural Network Optimization

In view of the premature convergence of particle swarm optimization (PSO) that is often caused by the loss of diversity, an improved cooperative PSO (ICPSO) is proposed. The method can dynamically combine the optimum values of the particles themselves, the global particles and the optimum values in groups, use the current optimization stage to dynamically adjust the shared proportion of information and effectively fuse various reference information, which can obtain superior global and local optimization performance. Additionally, to improve the diversity of the algorithm, a dynamic adjustment method using the grouping coefficient [Formula: see text] for the convergence rate is put forward. This method makes the algorithm have a more appropriate convergence rate while improving the convergence precision and enhancing the performance of the algorithm. Finally, the algorithm is used to optimize a neural network. The convergence condition and convergence rate of the algorithm are assessed by theoretical analysis and simulation experiments. The results show that ICPSO has more advantages in its diversity and the adjustment of the convergence rate compared to other related algorithms. Regarding neural network optimization, the training speed and optimization precision of the ICPSO-BP neural network are the highest, which has reached the best and average level of classification accuracy 98.5%, 96.3% for 20 iterations in Iris, and 98.7%, 95.1% in Wine. Its average iteration times score the best in five problems out of six.
Three approaches to facilitate invariant neurons and generalization to out-of-distribution orientations and illuminations

The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. Neurons that are invariant to orientations and illuminations have been proposed as a neural mechanism that could facilitate OoD generalization, but it is unclear how to encourage the emergence of such invariant neurons. In this paper, we investigate three different approaches that lead to the emergence of invariant neurons and substantially improve DNNs in recognizing objects in OoD orientations and illuminations. Namely, these approaches are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions. Each of these approaches substantially improves the DNN's OoD accuracy (more than 20% in some cases). We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains - individual neurons in the intermediate layers become invariant to OoD orientations and illuminations. We anticipate this study to be a basis for further improvement of deep neural networks' OoD generalization performance, which is highly demanded to achieve safe and fair AI applications.
“Sense of Presence” in Immersive Virtual Reality Environment

There is no concept analysis in the literature about "sense of presence" in immersive virtual reality environment in the context of nursing clinical education. The purpose of this article is to explore the meaning and derive an operational definition of the concept of sense of presence within the context of immersive virtual reality environment in nursing clinical education. Using Rodgers' approach, this evolutionary concept analysis will clarify the concept's surrogate and related terms, attributes, antecedents, and consequences. A clearer conceptualization is paramount to help provide knowledge for nurses and other health care professionals, thus guiding their practice, theory, and research.
Ultrasonic breast tumor extraction based on adversarial mechanism and active contour

Breast cancer is a high incidence of gynecological diseases; breast ultrasound screening can effectively reduce the mortality rate of breast cancer. In breast ultrasound images, the localization and segmentation of tumor lesions are important steps for the extraction of lesions, which helps clinicians evaluate breast lesions quantitatively and makes better clinical diagnosis of the disease. However, the segmentation of breast lesions is difficult due to the blurred and uneven edges of some lesions. In this paper, we propose a segmentation framework combining active contour module and deep learning adversarial mechanism and apply it for the segmentation of breast tumor lesions.We use a conditional adversarial network as the main framework. The generator is a segmentation network consisting of a Deformed U-Net and an active contour module. Here, the Deformed U-Net performs pixel-level segmentation for breast ultrasound images. The active contour module refines the tumor lesion edges, and the refined result provides loss information for Deformed U-Net. Therefore, the Deformed U-Net can better classify the edge pixels. The discriminator is the Markov discriminator; this discriminator provides loss feedback for the segmentation network. We cross-train the discriminator and segmentation network to implement Adversarial Mechanism for getting a more optimized segmentation network.The segmentation performance of the segmentation network for breast ultrasound images is improved by adding a Markov discriminator to provide discriminant loss training. The proposed method for segmenting the tumor lesions in breast ultrasound image obtains dice coefficient: 89.7%, accuracy: 98.1%, precision: 86.3%, mean-intersection-over-union: 82.2%, recall: 94.7%, specificity: 98.5% and F1score: 89.7%.Comparing with traditional methods, the proposed method gives better performance. The experimental results show that the proposed method can effectively segment the lesions in breast ultrasound images, and then assist doctors to realize the diagnosis of breast lesions.
Blockchain Teaching Simulation Using Gamification

A public blockchain simulation, which bears some resemblance to Bitcoin. All players are interconnected with several possibilities to form coin and block transactions and the power to get a taste of the consensus algorithm controlling transactions and blocks. A software medium that is based on blockchain games began using whiteboards to become web-based simulation games with various teaching scenarios. Contributive tool-based instruction with experience in blockchain concepts that can be used in the classroom as well as online. Along with this, simulations can give students the opportunity to experiment with all relevant blockchain operations like signing, creating transactions, building blocks, etc. This post was written to offer our firsthand experience teaching evolving blockchain technology to business and business information technology students at the undergraduate and graduate levels using newly built gamification teaching applications. This research is based on a design-based research methodology. We describe the process from identifying the problem, to designing the tool and demonstrating its implementation.
An Interpretable Feature Selection Based on Particle Swarm Optimization

Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.
Short Text Classification with Tolerance-Based Soft Computing Method

Text classification aims to assign labels to textual units such as documents, sentences and paragraphs. Some applications of text classification include sentiment classification and news categorization. In this paper, we present a soft computing technique-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. The TSC algorithm is a supervised learning method based on tolerance near sets. Near sets theory is a more recent soft computing methodology inspired by rough sets where instead of set approximation operators used by rough sets to induce tolerance classes, the tolerance classes are directly induced from the feature vectors using a tolerance level parameter and a distance function. The proposed TSC algorithm takes advantage of the recent advances in efficient feature extraction and vector generation from pre-trained bidirectional transformer encoders for creating tolerance classes. Experiments were performed on ten well-researched datasets which include both short and long text. Both pre-trained SBERT and TF-IDF vectors were used in the experimental analysis. Results from transformer-based vectors demonstrate that TSC outperforms five well-known machine learning algorithms on four datasets, and it is comparable with all other datasets based on the weighted F1, Precision and Recall scores. The highest AUC-ROC (Area under the Receiver Operating Characteristics) score was obtained in two datasets and comparable in six other datasets. The highest ROC-PRC (Area under the Precision–Recall Curve) score was obtained in one dataset and comparable in four other datasets. Additionally, significant differences were observed in most comparisons when examining the statistical difference between the weighted F1-score of TSC and other classifiers using a Wilcoxon signed-ranks test.
Local Reductions for the Modal Cube

Abstract The modal logic $${\mathsf {K}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> is commonly used to represent and reason about necessity and possibility and its extensions with combinations of additional axioms are used to represent knowledge, belief, desires and intentions. Here we present local reductions of all propositional modal logics in the so-called modal cube, that is, extensions of $${\mathsf {K}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> with arbitrary combinations of the axioms $${\mathsf {B}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>B</mml:mi></mml:math> , $${\mathsf {D}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>D</mml:mi></mml:math> , $${\mathsf {T}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> , $${\mathsf {4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>4</mml:mn></mml:math> and $${\mathsf {5}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>5</mml:mn></mml:math> to a normal form comprising a formula and the set of modal levels it occurs at. Using these reductions we can carry out reasoning for all these logics with the theorem prover "Image missing"<!-- image only, no MathML or LaTex -->. We define benchmarks for these logics and experiment with the reduction approach as compared to an existing resolution calculus with specialised inference rules for the various logics.
Sequent Calculi for Choice Logics

Abstract Choice logics constitute a family of propositional logics and are used for the representation of preferences, with especially qualitative choice logic (QCL) being an established formalism with numerous applications in artificial intelligence. While computational properties and applications of choice logics have been studied in the literature, only few results are known about the proof-theoretic aspects of their use. We propose a sound and complete sequent calculus for preferred model entailment in QCL, where a formula F is entailed by a QCL-theory T if F is true in all preferred models of T . The calculus is based on labeled sequent and refutation calculi, and can be easily adapted for different purposes. For instance, using the calculus as a cornerstone, calculi for other choice logics such as conjunctive choice logic (CCL) can be obtained in a straightforward way.
Human action recognition using fusion‐based discriminative features and long short term memory classification

Summary Proposed is a discriminative feature modeling technique in three orthogonal planes (TOP) for human action recognition (HAR). Pyramidal histogram of orientation gradient‐TOP (PHOG‐TOP) and dense optical flow‐TOP (DOF‐TOP) techniques are utilized for salient motion estimation and description to represent the human action in a compact but distinct manner. The contribution of the work is to explicitly learn the gradual change of visual patterns using fusion of PHOG‐TOP and DOF‐TOP technique to discover the nature of the action. With this feature representation, dimensionality reduction is achieved by deep stacked autoencoders. The encoded feature representation is used in long short term memory (LSTM) classification for HAR. Experiments with better recognition rate demonstrate the discriminative power of the proposed descriptor. Moreover, the proposed modeling and LSTM classification outperforms the state of art methods for HAR.
LogInjector: Detecting Web Application Log Injection Vulnerabilities

Web applications widely use the logging functionality, but improper handling can bring serious security threats. An attacker can trigger the execution of malicious data by writing malicious data to the web application logs and then accessing the view–logs interface, resulting in a vulnerability of the web application log injection. However, detecting this type of vulnerability requires automatic discovery of log-injectable interfaces and view–logs interfaces, which is difficult. In addition, bypasssing the application-specific input-filtering checks to write an effective payload to the log is also challenging. This paper proposes LogInjector, an efficient web application log injection vulnerability detection method. First, it obtains the log storage form and location and then finds the log-injectable interfaces through the extended dynamic crawler. Second, it automatically identifies the web application view–logs interfaces. Finally, LogInjector utilizes a dynamic testing approach based on the feedback-guided mutation to detect web application log injection vulnerabilities. To verify the effectiveness of LogInjector, we test it in 14 popular web applications in real-world cases and compare it with Black Widow, the state-of-the-art web vulnerability scanner. LogInjector detects 16 web application log injection vulnerabilities, including 6 zero-day vulnerabilities, while Black Widow can only detect three log injection vulnerabilities, demonstrating the effectiveness of LogInjector in practice.
Predicting Student Learning Effectiveness in Higher Education Based on Big Data Analysis

With the global higher education entering the era of “quality is king,” the perception and acquisition of learning experience and the evaluation of learning quality and its effect have attracted more and more attention. However, at present, most of the academic evaluations of university courses are based on final examinations, supplemented by appropriate amount of regular tests, and little attention is paid to the development and quality of students’ learning ability in the learning process, which inevitably causes academic evaluations to be equivalent to assessments and deviates from the original purpose of academic evaluations to promote continuous improvement of teaching and learning quality. On this basis, this study uses the big data analysis method to predict the teaching effect of college students, in order to improve the existing teaching problems, grasp the teaching essence, and construct a relatively perfect curriculum evaluation system combined with the course of “teaching effect guidance + teaching action,” so as to further improve the academic evaluation system and improve the teaching quality. This will provide a reference for further improving the academic evaluation system and improving the teaching quality.