Reviewerly/exHarmony huggingface dataset:
NATURAL LANGUAGE INTERFACE FOR QUERYING LINKED DATA USING CASE-BASED REASONING

With the emergence of Linked Data in RDF format, query systems should execute structured queries in SPARQL.Still, only expert programmers can specify their information needs and cope with Linked Data sources' diversity and heterogeneity.Therefore, understanding Natural Language (NL) queries to construct correct SPARQL queries is a great challenge for these query systems to access multiple heterogeneous semantic sources and Linked Data sets.This paper presents a new approach for querying Linked Data with NL queries.Our method identifies the topic and search criteria of the NL query based on the Case-Based Reasoning (CBR) technique.Next, we match the identified topic and search criteria to their corresponding entities in the dataset.Then, we represent the query as triples based on the semantic relations provided by the dataset.Finally, our system executes the SPARQL queries generated from the query triples, based on the proposed generation rules, to query the dataset and retrieve the relevant answers.Experiments showed that the proposed approach is efficient compared to the existing systems in terms of precision and recall.
Computer Multimedia Art Pattern Creation Based on P-Filling Algorithm

Art education is an important part of quality education in China. It undertakes the important responsibility of cultivating students' aesthetic ability, art appreciation ability, art perception ability, and expression ability. In junior middle school art teaching, the skill of pattern creation is an ability that needs to be focused on cultivating students. The computer multimedia art patterns are currently created mainly relying on manual drawing. A computer multimedia art pattern production model is proposed based on the P-filling algorithm in this paper. After using the P-filling algorithm to quickly generate a large number of fake images and retrieve the code of the most recent damaged image, the code estimates the missing content by generating a model. On this basis, the semantic P-filling algorithm and the perceptual P-filling algorithm are combined, and the unsaturated region is enlarged by improving the activation function sigmoid function, which solves the problem that the gradient is easy to disappear. Experimental results show that, through this production model, users can produce a large number of computer multimedia art patterns with hand-drawn style features through very little interaction and parameter control.
Analysis of the Use of Visualization in Teaching Subjects of Different Ages

Global technological changes and the widespread use of information and communication technologies lead to the need to study the formation of information literacy skills of various demographic groups.The formation of the information economy creates increased requirements for the level of proficiency in digital and information technologies in order to successfully exist in a modern high-tech environment, which leads to the need for changes in the education system.These changes are aimed at developing the ability to understand the dynamics of the information world, to navigate the increasing flow of information.To this end, the latest technologies are actively used in educational programs, which, on the one hand, improve the quality of the educational process, helping to master educational content more effectively, and on the other hand, contribute to the mastery and use of digital and information technologies by students.This study examines the technologies of visual learning, which significantly improve the quality of the educational process.As part of the study of modern trends in the educational environment, the emphasis was placed on the use of visualization as a form of presentation of educational material.Different forms of visualization were investigated: active, passive, value-semantic, universal for different age groups of respondents (schoolchildren, students, adults), a total of 412 people participated in the study.For six months, they worked with different forms of visualization as part of the training.It was revealed that it is necessary to take into account age factors, motivational features that will allow you to build individual training routes based on operating with different forms of visual information, which will allow you to critically and systematically study materials, qualitatively develop skills and abilities, be effective, achieve goals and results.
Research on the Path of Network Opinion Expression in AI Environment for College Students

Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.
A Formal Model for Analogies in Civil Law Reasoning

In his famous German paper “Juristische Schlüsse a fortiori und a pari” from 1965, Alchourron developed a model for analogies taking into consideration different types of legal rules. Taking this as a starting point we develop a formal model going deeper into jurisprudence by referring to balancing interests. This model deals with analogies based on a-fortiori-arguments, a-pari-arguments and a-simile-arguments.
Flowart, a physical activity at the level of hypermodernity, even hypomodernity

Our current hypermodern era, as described by many authors, imposes frenzied rhythms of life on individuals exacerbated by new communication technologies. In this incessant tumult, it has become more and more difficult for humans to take time for themselves or even a moment to reflect on their daily lives. It therefore seems necessary for some to indulge in more contemplative and meditative leisure and sporting activities that reconnect with the principles of otium and thus fall into a form of hypomodernity. It is in this reflective context that this study was conducted to interview seven flowart practitioners using a qualitative approach. Halfway between artistic practice and physical activity, flowart seems to allow these individuals to reconnect with a rhythm of life that is more biological than digital. These participants see a way to slow down the frenzy of their daily life in this practice while becoming more aware of their place in a cosmic and metaphysical whole.
Elite Firefly and Long Short Term Memory Based Model for Texture Classification

The texture is an important feature in the visual content of the images and plays a significant role in the computer vision system.Many existing methods were involved in applying the feature selection and classifiers for texture classification.Existing methods have the limitation of poor convergence in the feature selection and overfitting problems in the classification.In this research, the Elite FireFly -Long Short Term Memory (EFF-LSTM) model is proposed to improve the efficiency of the texture classification.The EFF method has the advantage of replacing the low fitness firefly with a high fitness firefly in the search process and the LSTM model has the advantage of a store the important features in long term for the classification.This process provides good convergence in the feature selection and important features are applied to overcome the overfitting problem.Two datasets such as UIUC and KTHTIPS-2b were used to test the performance of the proposed EEF-LSTM model.The Min-Max normalization method is applied to enhance the quality of the images.The Local Directional Ternary Pattern (LDTP), Dual-Tree Complex Wavelet Transform (DTCWT), and Grey-Level Co-occurrence Matrix (GLCM) were the extracted features from the pre-processed images.The EFF method is applied to select the relevant features with good convergence.The LSTM method is applied to classify the images based on the EFF selected features.The proposed EFF-LSTM model has an accuracy of 96.5 % and the existing Convolutional Neural Network (CNN) has 94.3 % accuracy.
Conceptual Frameworks for Big Data Visualization: Discussion of Models, Methods, and Artificial Intelligence for Graphical Representations of Data

Data visualization is a rich interdisciplinary domain with applications across disciplines. In spite of significant developments in the practice of data visualization, the evolution of big data continues to create challenges and opportunities for new forms of data visualization. The gap in data visualization is primarily driven by an interaction between the sophistications of the human mind and the dynamic aspects of information representation, perceptions and consumption. In particular, the visualization of textual data holds much promise and relevant frameworks need to be developed to address this opportunity. In our current stream of research, we provide a brief overview of the developments in and scope of data visualization from a textual analytics perspective. We present a novel textual data visualization framework “Artificial Intelligence Based Textual Data Visualization Framework Analysis,” which helps advance the orientation of textual data visualizations. We conclude with a note on the potential for the use of artificial intelligence in data visualization.
Low Area FPGA Implementation of SP-box PRESENT Cryptography with MSCLCG Key Generation

Nowadays, the Lightweight cryptography algorithm is mostly used for security level purposes, because it's designed with the help of basic arithmetic logic blocks.The basic blocks are perfectly placed in respective modules to process the encryption with effective manner.The fundamental shifting based PRESENT algorithms are frequently used to help the hackers to retrieve the data.To overcome this issue, Separation With NOT (SWN) with Srikanth Parikibandla box (SP-box) architecture is used in this research work.Due to the usage of modified Register Transfer Level (RTL) structure, the process and the confidentiality of the PRESENT encryption architecture is improved in a significant manner.The Modified Single Coupled Linear Congruential Generator (MS-CLCG) architecture is used to generate the random number, which act as a key for encryption as well as decryption.For every clock cycle, MS-CLCG architecture generates the random value to perform the encryption.The SWN-SP architecture occupied 56 counts of LUTs, 33 count of Flip-flops and 30 counts of slices, which are less compared to the existing SRS-PRESENT architecture.
Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing

Deep neural networks (DNNs) are proven vulnerable to attack against adversarial examples. Black-box transfer attacks pose a massive threat to AI applications without accessing target models. At present, the most effective black-box attack methods mainly adopt data enhancement methods, such as input transformation. Previous data enhancement frameworks only work on input transformations that satisfy accuracy or loss invariance. However, it does not work for other transformations that do not meet the above conditions, such as the transformation which will lose information. To solve this problem, we propose a new noise data enhancement framework (NDEF), which only transforms adversarial perturbation to avoid the above issues effectively. In addition, we introduce random erasing under this framework to prevent the over-fitting of adversarial examples. Experimental results show that the black-box attack success rate of our method Random Erasing Iterative Fast Gradient Sign Method (REI-FGSM) is 4.2% higher than DI-FGSM in six models on average and 6.6% higher than DI-FGSM in three defense models. REI-FGSM can combine with other methods to achieve excellent performance. The attack performance of SI-FGSM can be improved by 22.9% on average when combined with REI-FGSM. Besides, our combined version with DI-TI-MI-FGSM, i.e., DI-TI-MI-REI-FGSM can achieve an average attack success rate of 97.0% against three ensemble adversarial training models, which is greater than the current gradient iterative attack method. We also introduce Gaussian blur to prove the compatibility of our framework.
An Evaluation of Communicative Language Teaching in Pakistan: A Study of Undergraduate English Learners of Pakistan

The research was focused on the use of a communicative method of teaching. Through the use of such an advanced approach, students are unable to be considered proficient speakers of the English language. Various factors were highlighted in this research that causes a hindrance in the success of communicative method teaching. It was also identified how this approach can have positive effects on the students that would help them become proficient speakers of the English language (Nazir, Abbas, &amp; Naz, 2020). A 5-point Likert scale was used in the questionnaires that were filled by 100 students of only one private sector in Lahore. The results deducted the positive effects of this approach along with the reasons for its inability to produce proficient speakers and how it can be sorted out.
Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.
Features of Spiritual and Moral Education of Cadets of Cossack Classes: an Empirical Study

The article is devoted to the study of the specifics of the spiritual and moral education of mid-level Cossack cadets. Special attention in the study is paid to the justification of the need for classes and events on Orthodox themes within the framework of additional education for the Cossack class. The experience of diagnosing the features of spiritual and moral education and discussing the results obtained is presented. The importance of the implementation of the psychological and pedagogical program for the development and improvement of the moral and ethical component in Cossack cadet classes is emphasized.
Knowledge Graph-Based MOBA-Game Champion Recommendation

With the rapid development of the e-sports industry, various competitions are being held. The most high-profile events are those related to League of Legends, where not only are design companies struggling to provide players with a wider selection of champions, but club tournament analysts are also focusing on line-up. Since there are strong relationship constraints between champions, it becomes a valuable research topic to design a champion lineup selection recommender for players and e-sports athletes. Using the data of the 2020 summer tournament and of the players in the same period, we proposed a recommendation scheme of the champion lineup based on the knowledge graph technique and the results were verified and analyzed.
Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.
Development Mobile Application Android Based for Sensor and Transducer

Abstract The aims of this research are: (1) producing a mobile applications android based for sensor and transducer field of study on vocational education; (2) know the quality of the product mobile applications android based for sensor and transducer field study on vocational education. The method used is the Software Development Life Cycle with the Waterfall model. The stages in Waterfall model are: (1) requirements definition; (2) system and software design; (3) implementation and unit testing; (4) integration and system testing; (5) operation and maintenance. The black box test results show the performance of Sensor application software can all function properly. While the results of the compatibility test show the application can run on various android systems (at least Android 4.2).
cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU

The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure client and model-holder data privately on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with a larger-scale dataset, due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of a convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is 2-fold: (1) To avoid the trivia and complicated homomorphic matrix computations brought by BGV-based solutions, it adopts GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. (2) To improve the computation efficiency on GPU, a hybrid optimization approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the other frameworks.
Comparison and intelligent analysis of NTRU and ETRU signature algorithms for public key digital signature

Abstract Three American mathematicians made the NTRU public-key cryptosystem in 1996, it has a fast speed, small footprint, and also it is easy to produce key advantages. The NTRU signature algorithm is based on an integer base, the performance of the signature algorithm will change when the integer base becomes other bases. Based on the definition of “high-dimensional density” of lattice signatures, this paper chooses the ETRU signature algorithm formed by replacing the integer base with the Eisenstein integer base as a representative, and analyzes and compares the performance, security of NTRU and ETRU signature algorithms, SVP and CVP and other difficult issues, the speed of signature and verification, and the consumption of resources occupied by the algorithm.
BA-BNN: Detect LDoS Attacks in SDN Based on Bat Algorithm and BP Neural Network

Low-rate Denial of Service (LDoS) attack is a novel form of the denial-of-service attack. Consequently, there is something difficult to detect it by conventional attack detection techniques. BP neural network detects LDoS attacks is feasible because of its features such as high non-linear. However, it also has the defects such as poor global search ability. Bat algorithm that has the strong global search ability can make up those defects. We proposed a LDoS attack detection method that is on account of Bat Algorithm and BP Neural Network (BA-BNN). For the sake of making a verification on how valid our method is, a series of LDoS attack experiments were carried out in a virtual Software Defined Network (SDN) composed of Mininet emulator and Ryu controller, obtaining the TCP and UDP traffic data. We use the algorithm called random forest to select characteristics of training data for BP neural network ameliorated by an intelligent algorithm, Bat algorithm, and construct the LDoS attack detection model. We compare our method with some other methods. The results show that the method we adopt is a highly accurate LDoS attack detection method. It has higher correct rate and detection rate, and lower false negative rate and false positive rate.
An Optimal Method for Extracting the Center of Light Stripes

Abstract A method for extracting the center of the light stripe to effectively reduce the environmental noise is proposed in this paper. The block matching algorithm is adapted to use the global information in the structured light image to group image blocks with similar light stripe structures. The center coordinates of the light stripe in each group of image blocks are extracted by the gray gravity method, and its average value is used as the final center of light stripes in the similar image block, which reduces the influence of random noise on the accuracy of the extraction algorithm.