Reviewerly/exHarmony huggingface dataset:
The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

Under the trend of rapid development and popularization of modern computer technology and information technology, the field of music education has also penetrated and integrated advanced computer technology and information technology. As an important branch of computer science, artificial intelligence involves many cross-cutting comprehensive subject content. As an indispensable part of our life, music is an art that reflects the emotions of human real life, and it is an ideology older than language. The rapid development of technology has injected many new elements into music, gradually changing the way people write, perform, produce, and enjoy music. The Internet has injected new elements and content into music education. It has transformed the original methods of writing, performing, producing, and appreciating music education and has had a great impact on music education concepts and teaching models. The future development trend of music education makes music education reach a new height under the application of artificial intelligence technology. Based on the current research results at home and abroad, this thesis closely integrates music theory and computational methods and advocates the comprehensive application of music theory, cognitive psychology, music cognition, neuroscience, artificial intelligence, signal processing, and other theories to solve music problems. Signal analysis identifies problems. In-depth discussion of the possibility and development trend of artificial intelligence technology applied to music education.
Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Most arm robot has an inefficient operating time because it requires operator to input destination coordinates. Besides, main problem of arm robot is object’s vulnerability when it is manipulated by the robot. This research goals is to develop an arm robot control system which has ability to automatically detect object using image processing in order to reduce operating time. It is also able to control gripping force for eliminating damage to objects caused by robot gripper. This research is implemented in LabVIEW 2011 software to control arm robot model which can represent industrial scale robot. The software is designed with informative visualization to help user learn and understand robotic control concept deeply. The system can automatically detect object position based on pattern recognition method which has four steps: pre-processing process to initialize picture taken by camera, segmentation process for separating object from the background, classification process to determine characteristics of object, and position estimation process to estimate object position in the picture. The object’s position data are then calculated by using kinematic equation to control the robot’s motion. The results show that the system is able to detect object and move the robot automatically with accuracy rate in x-axis is 95.578 % and in y-axis is 92.878 %. The system also implements modified PI control method with FSR as input to control gripping force with maximum overshoot value `10 %. Arm robot model control system developed is successfully meet the expectation. The system control can be implemented to industrial scale arm robot with several modification because of kinematic similarity between model and industrial scale robot.
A Critical Evaluation of Procedural Content Generation Approaches for Digital Twins

Procedural content generation (PCG) of terrains is one of the main building blocks for creating an automated and real-time virtual world. This study provides and in-depth review of the different tools, algorithms, and engines used for the PCG of terrains for the PCG of digital worlds; we focus especially on terrain generation but address also the main issues related to build structures and characters. It is important to know that the PCG of terrains can be implemented in a multidisciplinary scenario, for instance, modeling of games, simulating industrial maps, urban and rural planning of developments, government, and nongovernmental agency improvement plans. Many problems in current approaches were identified from the literature, where most of the researches studied were merely throwaway prototyping based, i.e., proof of concept-based systems, and were not tested in the real environment. Also, genetic algorithm-based PCG of terrains lack multiple features such as roads and buildings. The virtual cities generated through different engines were lacking a realistic look and feel. Terrain generation through multiobjective evolutionary algorithms (MOEAs) is investigated, and it is deemed the usage was restricted to the gaming domain and not extended to other fields. Findings suggest that the correctness of generated terrain is a big issue in the automatic generation of terrains. Thus, a focus on the automated correctness check is required. Other important content generation in video games such as structure generation and character generation has been extensively studied, and the techniques are analysed in the further sections of this research work.
Regression Analysis with Scikit-Learn (part 1 - Linear)

This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn). The lesson also discusses interpreting the results of a regression model and some common pitfalls to avoid.
A Comparative Selection of Best Activation Pair Layer in Convolution Neural Network for Sentence Classification using Deep Learning Model

Many natural language processing jobs need sentiment classification of text content. There is an urgent need, particularly with the rise of social media, to extract meaningful information from vast volumes of data upon that Internet utilizing sentiment analysis. We’re interested in adopting deep learning models to handle sentiment classification because of the advances that have been made. We present a framework named fastText along with Convolutional Neural Network in this study (CNN). To commence, we use fastText to create words vector representations that will be fed into the CNN. FastText’s purpose is to generate a generative model of a word as well as reflect word distance. This one will enable the parameters to also be established at an advantageous CNN point, which will help neural nets to perform much better in this circumstance. Second, we create a CNN architecture that is appropriate for sentiment analysis. We use two sets of convolutional layers along with pooling layers in this design. This is the first time, to our knowledge, that a 9-layer architecture and design model is developed based on fastText as well as CNN was used to assess the sentim ent of phrases. We use the Rectified Linear Unit (ReLU), Normalization, and Dropout techniques to improve the accuracy and generalizability of our model. We put our methodology to the test on a publicly available dataset of movie review extracts with five labels: negative, slightly negative, neutral, moderately positive, and positive. In this dataset, ourReLU pairwise network obtains a test accuracy of 96.4 percent, outperforming existing neural network models.
Reversible Image Steganography Techniques: A Performance Study

Reversible image steganography is one of the widely researched areas. The important reason behind this is, it reconstructs the actual version of the cover image exclusive of loss. Several techniques have been proposed for better performance in the past and even today. But still, there exists a gap in the literature that leads to further research and improvement of the previous techniques. In this article, a performance study is carried out on reversible image steganography techniques. The techniques over the past two decades were compared along with standard test images. PSNR and embedding capacity are taken into consideration as the evaluation parameters. The experimental results of each technique were tabulated and compared to provide insight. An in-depth analysis and descriptive statistics are done to give a clear understanding of the performance of each technique. The article offers a detailed assessment of the past techniques concerning the standard test.
A System to Detect Fake Products using Blockchain Technology

There are a large number of fake products in the market being manufactured, distributed, sold, and purchased. Here, the meaning of fake products is products that are not from registered or branded sellers and manufacturers. Fake products are actually gaining more importance in the market because of low cost and similarity of them with original products. In this study, genuinity of fake products is determined to help users so that they can distinguish between fake and original products with the help of blockchain technology and cryptocurrency. QR codes will help users to scan and acquire overall information of a product such as basic details, genuinity, ownership, and seller information.
Offline Arabic handwritten character recognition: from conventional machine learning system to deep learning approaches

Researchers have made great strides in the area of Arabic handwritten character recognition in the last decades especially with the fast development of deep learning algorithms. The characteristics of Arabic manuscript text pose several problems for a recognition system. This paper presents a conventional machine learning system based on the extraction of a set of preselected features and an SVM classifier. In the second part, a simplified convolutional neural network (CNN) model is proposed, which is compared to six other CNN models based on the pre-trained architectures. The suggested methods were tested using three databases: two versions of the OIHACDB dataset and the AIA9K dataset. The experimental results show that the proposed CNN model obtained promising results, as it is able to recognise 94.7%, 98.3%, and 95.6% of the test set of the three databases OIHACDB-28, OIHACDB-40, and AIA9K, respectively.
College English Multimodal Teaching Based on Digital Information Technology

In the era of in-depth application of knowledge technology in the field of education, the teaching mode combining school English teaching and network data technology is widely used in school English education. Teaching resources have become electronic resources under the influence of digital information technology. However, English teaching often ignores all aspects of English ability, so there are still shortcomings in English teaching. The role of information technology makes a single English teaching text model unable to meet the needs of scholars. In recent years, multimodal events have enriched the expression of language input and output content, which is in line with the learning needs of current students. Therefore, under the premise of digital information technology, this paper constructs a multimodal English teaching model from the aspects of multimodal English teaching resources, multimodal English teaching activities, and multimodal English teaching analysis. It aims to mobilize the enthusiasm of scholars to learn English from various aspects and build a multimodal teaching mode of English for teachers supported by digital data technology. Therefore, the experiment shows that the multimodal teaching mode of college English based on digital information technology proposed in this paper will mobilize the enthusiasm of students to learn in a comprehensive way. It greatly improves students’ English performance, promotes the development of students’ autonomous learning ability and comprehensive language ability, and improves the quality of English teaching by 20%.
Anomaly detection in surveillance videos based on H265 and deep learning

Shopping malls, banks, hospitals, markets, educational institutions, smart cities, and roadways are all places where video surveillance systems (VSS) are commonly used to improve public safety.The correctness and speed with which video anomalies are identified are usually the primary focus of security applications.Recently, many surveillance cameras have been installed at various locations around the world for public safety purposes.Massive volumes of video data are continuously generated by these cameras [1].
Health Information Prediction System of Infant Sports Based on Deep Learning Network

The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.
Intelligent Integration of Online Environmental Education Resources for English Language and Literature Majors Based on Collaborative Filtering Algorithm

There are currently many different types and dispersed online educational resources, which inconvenience users and result in a low utilisation rate of resources. As a result, a new approach is required to realise the integration and recommendation of educational resources. This paper examines the intelligent integration and recommendation of online learning resources for English language and literature majors based on CF. The development of online English language and literature education resources is currently in the process of being discussed, and some flaws in the process are being examined in this paper. The creation and incorporation of a network education resource database are proposed as some strategies and recommendations. The information entropy method is employed to address the cold start problem brought on by the data sparseness of new users and new projects in CF. While this is happening, the recommendation process’s similarity algorithm is being enhanced. This algorithm’s decision support accuracy has been found to be 96.01% after extensive testing. Its accuracy is roughly 8% better than that of conventional CF, which has a precision of 8%. The results demonstrated a degree of accuracy in the improved algorithm.
Enhancement of System Security by Using LSB and RSA Algorithms

A steganography hides information within other information, such as file, message, picture, or video. A cryptography is the science of converting the information from a readable form to an unreadable form for unauthorized person. The main problem in the stenographic system is embedding in cover-data without providing information that would facilitate its removal. In this research, a method for embedding data into images is suggested which employs least significant bit Steganography (LSB) and ciphering (RSA algorithm) to protect the data. System security will be enhanced by this collaboration between steganography and cryptography.
Double-image encryption scheme based on the phase-truncated multiple-parameter Fresnel transform

A double-image encryption algorithm is proposed with the phase-truncated multiple-parameter Fresnel transform. Firstly, the pixel positions of two plaintext images are scrambled and then the results are merged into one image with the scrambling operation. Subsequently, the resulting image is encrypted by phase truncation and phase reservation in the multiple-parameter Fresnel transform domain. The phase information is scrambled by the affine transform and then recombined with the amplitude information. The final encryption image is obtained with the pixel scrambling and diffusion methods to further enhance the security of the image encryption system, where the scrambling and diffusion operations are based on logistic map, logistic-sine system and 2D logistic-adjusted-sine map. The image encryption scheme is robust against the common attacks due to the nonlinear properties of diffusion and phase truncation. Numerical simulation results verify the performance and the security of the proposed double-image algorithm based on the phase-truncated multiple-parameter Fresnel transform.
Noninvasive acoustic time-of-flight measurements in heated, hermetically-sealed high explosives using a convolutional neural network

We present a data-driven technique for measuring the time-of-flight through material sealed within a container. Time-of-flight measurement provides a noninvasive means of quantifying the sound speed profile within a material by transmitting an acoustic burst and then measuring the time required for the burst to arrive at an opposing receiver. In a hermetically-sealed cylindrical container, a portion of the acoustic energy propagates through the material as a bulk wave, while the remainder of the acoustic energy propagates around the container walls as guided waves. As a result, interference from the guided waves obscures the bulk arrival, inhibiting measurement of the sound speed. The technique uses a Convolutional Neural Network (CNN) to identify critical features in the measured waveforms and identify bulk wave arrivals. We demonstrate this time-of-flight measurement technique on high explosive-filled containers as they are heated from room temperature to detonation. This is a particularly challenging application for acoustic time-of-flight measurements as the high explosives have significant sound speed gradients as they undergo heating, and they lead to significant attenuation of the bulk wave, as opposed to the guided waves, which do not suffer significant attenuation. We characterize the performance of the CNN as a function of the high explosive temperature and as a function of the CNN hyperparameters. We then provide physical insight into the error trends.
Implementation of Quantum Support Vector Machine Algorithm Using a Benchmarking Dataset

The evolution of quantum computers and quantum machine learning (QML) algorithms have started demonstrating exponential speed-ups. In machine learning problems, the efficient handling and manipulation of linear algebra subroutines defines the complexity of the task to be performed. Quantum computers handle big datasets in the form of vectors and matrix operations very efficiently. In this paper, quantum support vector machine (QSVM) algorithm is used to solve a classification problem using a benchmarking MNIST dataset of handwritten images of digits. Quantum SVM variational and kernel matrix algorithms are implemented to analyze quantum speedup on quantum simulator and physical quantum processor back-ends. The study compared classical and quantum SVM algorithms in terms of execution time and accuracy. The results explicitly prove quantum speed-up achieved by quantum classifiers on quantum back-ends for machine learning applications.
The effect of psychological ownership on organisational commitment

Many scholars have extended the theory of psychological ownership as it relates to employee behaviour in organisations, although only a handful of studies have examined the components of psychological ownership that impact an organisation.This study aims to verify the relationship between psychological ownership and organisational commitment, while scrutinising the main factors (constructs) in psychological ownership affecting organisational commitment.This study used survey responses collected from 349 staff members at a state-owned Thai telecommunications enterprise, the results from which indicated that only a 'sense of belonging' and 'work responsibility' significantly influenced organisational commitment.As such, organisations should provide a working environment in which employees feel they have a sense of autonomy and freedom of choice in their job.Furthermore, senior executives should be transparent and accountable for their actions, so that their behaviour can filter down throughout the organisation and be used as an example for other employees.Such practices can increase high levels of involvement and participation, ultimately leading to the creation of psychological ownership perspectives in an organisation.
Informational Task “Calculation of Technical and Economic Indicators” NPP I&amp;C ULCS and Risk Potential Assessment

Calculation of technical and economic indicators (TEI) is part of the information tasks of the upper-level control system of the NPP instrumentation and control system (I&C system). The TEI characterize the efficiency, reliability, and durability of the power unit equipment during its operation. Based on the automation of the TEI determination, objective information can be obtained about the efficiency of technological processes, the actual state of power equipment, as well as the risk potential of the ongoing processes.
Catastrophic Forgetting in Continual Concept Bottleneck Models

Almost all Deep Learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual Learning have been proposed, even though the extent of the forgetting is still unclear. In this paper, we analyze Concept Bottleneck (CB) models in the Continual Learning setting and we investigate the effect of high-level features supervision on Catastrophic Forgetting at the representation layer. Consequently, we introduce two different metrics to evaluate the loss of information on the learned concepts as new experiences are encountered. We also show that the obtained Saliency maps remain more stable with the attributes supervision. The code is available at https://github.com/Bontempogianpaolo1/continualExplain
Power Semantic Text Relation Extraction Method Based on Data Center

At present, traditional deep learning-based relation extraction methods are difficult to extract in complex contexts, and do not consider the impact of non-target relations in context on relation extraction. In response to this problem, this paper proposes a control input long short-term memory network. CI-LSTM, this network adds an input control unit composed of an attention mechanism and a control gate valve unit to the traditional LSTM. The control gate valve unit can perform key learning on key positions according to the control vector. The different features of the input are calculated. In this paper, the syntactic dependency relationship is finally selected to generate the control vector and the relationship extraction model is constructed through experiments. Better performance in complex contexts.