Reviewerly/exHarmony huggingface dataset:
Comparison Between Features Extraction Techniques for Impairments Arabic Speech

Automatic Speech Recognition (ASR) is a tough task, with the existence of related noise and high unpredictability in a speech presenting the most severe problems.Especially with regard to the noise of speech impairments, whether due to disability or mispronunciation in children.Extraction of noise-resistant features to compensate for speech degradation due to noise impact has remained a difficult challenge in the last few years.This research investigated the impact of different wavelet generations for extracting speech features, then test the produced dataset from each technique with two types of deep learning techniques : deep long shortterm memory (LSTM) and hyper deep learning model convolutional neural network with long short-term memory (CNN-LSTM).The result shows that the deep long short-term memory of MFCC has reached 93% as an accuracy while in the hyper deep learning model of CNN-LSTM the accuracy of MFCC was 91%, as the highest recorded accuracy which proves that MFCC would be the best feature extraction technique for our developed dataset.
A New Entrapment Based Invader Capture Strategy for Multi-robot Surveillance Systems

This study presents a new entrapment-based invader capture strategy for multi-robot surveillance systems. Most approaches have been focused on enclosing an invader using a particular formation. However, in terms of physical robot capability, it is hard to capture the invader practically. In this study, the invader has its escape cut off while multiple robots stand against it at some critical points on the invader’s path. The strategy consists of four parts. A graph is constructed on a given map for the Voronoi roadmap using the Voronoi diagram. Since constructed graph has a lot of nodes and edges, the thinning process is performed while reducing the computational load. Subsequently, candidate nodes are extracted on the whole map. If the invader escapes along the shortest path to the exit, the critical cluster is selected from the candidate nodes by checking the reachability of arrival times to the points. The proposed strategy is applied to a SLAM map using multiple robots. The results show that critical points are properly found, and all robots capture the invader successfully.
A Self-learning Musical Tool to Support the Educational Activity

Computer-assisted musical composition (for the generation of a melody, accompaniment or rhythm) is a topic that has stimulated multiple research projects. However, few of these projects take into account the didactic aspect by paying the right attention to students with Specific Learning Disorders (such as dyslexic students). Several previous works have proposed methods for harmonizing a melody, that is, for creating a succession of chords as an accompaniment to a melody. This paper illustrates a model for the harmonization of music bassline based on a machine learning approach. This model has been designed to support (dyslexic and non-dyslexic) students in studying Theory, Analysis and Composition. It allows students to harmonize a music bassline, giving them advices in case of mistakes in the concatenation of the chords. Future improvements of the method are discussed briefly at the end of the paper.
Cartoonify an Image using Open CV in Python

The main objective is to put ahead a result for making over pictures or vids of real- global into animated prints (Cartoon Images) or Videotape. The concept of the paper is grounded on unique shots and vids which are converted to an art shape similar as oil. Amongst all the methods usable, the operation of a Generative Adversarial Network (GAN) known as Cartoon GAN may be used for the styling actual- world pictures that use two loss functions videlicet, glad loss and inimical loss for getting a sharp and clear image. This project would follow an OpenCV library within the python whilst making the layout. Python is the collections of libraries and it has multiplex libraries for real- world operations. One corresponding library is OpenCV and it is a move-platform library used for Computer Vision. It consists of operations like vid and photo taking pictures and processing. It's majorly used in photograph conversion, object finding, face popularity, and a couple of different operations. In this paper, companion diploma provident style for objects beginning from caricature film land is delivered; it's supported popular hypotheticals related to color and locales of objects in cartoon film land, the gadgets square degree usually drawn close to the center of the image, the history colorations that the greater hourly drawn near the perimeters of cartoon image, and additionally the item colorations a decrease quantum bit for the edges.
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering

Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate contexts and reading comprehension to extract the final answer. While there has been some attention to improving the reading comprehension part of QA systems against errors that automatic speech recognition (ASR) models introduce, the passage retrieval part remains unexplored. However, such errors can affect the performance of passage retrieval, leading to inferior end-to-end performance. To address this gap, we augment two existing large-scale passage ranking and open domain QA datasets with synthetic ASR noise and study the robustness of lexical and dense retrievers against questions with ASR noise. Furthermore, we study the generalizability of data augmentation techniques across different domains; with each domain being a different language dialect or accent. Finally, we create a new dataset with questions voiced by human users and use their transcriptions to show that the retrieval performance can further degrade when dealing with natural ASR noise instead of synthetic ASR noise.
Analysis and evaluation of synchronous and asynchronous FLchain

Motivated by the heterogeneous nature of devices participating in large-scale federated learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, namely FLchain, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing solution for FL when dealing with large datasets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.
An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data

The compressors used in today’s natural gas production industry have an essential role in maintaining the production line operational. Each of the compressors’ components has routine maintenance tasks to avoid sudden failures. Hence, the significant advantages and benefits of performing preventative maintenance tasks in time are decreasing equipment downtime, saving additional costs, and improving the safety and reliability of the whole system. In this paper, anomaly classification and detection methods based on a neural network hybrid model named Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies in sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system for predictive maintenance. In research methodology, this paper has conducted experiments that employed different RNN architectures such as GRU, LSTM, Stacked LSTM, and Stacked GRU with various functions to create a baseline for model evaluation. Each architecture used audio signals dataset received from the compressor system for experiments to consider each neural network model’s performance. According to performance results, an optimal model for anomaly detection with the best performance scores has been proposed in this research. Experiments combined one-dimensional raw audio signal features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Hence, such hybrid methods can effectively detect normal and anomaly audio signals collected from a compressor system, increasing the compressor system’s reliability and the sustainability of the gas production line. The combination of multiple-resource features in the proposed hybrid model showed a 100% score in all four-evaluation metrics such as accuracy, precision, recall, and F1 in LSTM-based autoencoder in both test and train results.
A Novel Target Tracking Scheme Based on Attention Mechanism in Complex Scenes

In recent years, target tracking algorithms based on deep learning have realized significant progress, especially the Siamese neural network structure, which has a simple structure and excellent scalability. Although these methods provide excellent generalization capabilities, they fail to perform the task of learning target information discrimination smoothly due to being affected by distractors such as background clutter, occlusion, and target size. To solve this problem, in this paper we propose a newly improved Siamese network target tracking algorithm based on an attention mechanism. We introduce a channel attention module and a spatial attention module into the original network to improve the problem of insufficient semantic extraction ability of the convolutional layer of the tracking algorithm in complex environments. A channel attention mechanism enhances the feature extraction ability by using the network to learn the importance of each channel and establish the relationship between channels, while a spatial attention mechanism strengthens the feature extraction ability by establishing the importance of spatial position in locating the target or carrying out a certain degree of deformation. In this paper, the above two models are combined to improve the robustness of trackers without sacrificing tracking speed. We conduct a comprehensive experiment on the Object Tracking Benchmark dataset. The experimental results show that our algorithm outperforms other real-time trackers in both accuracy and robustness in most complex environments.
COVID-19 and entrepreneurship courses in Latin American universities: an analysis of satisfaction comparing students' and lecturers' perspectives

Purpose This study aims to investigate how the disruptive change from a face-to-face to an online teaching/learning modality due to COVID-19 has been perceived by students and lecturers of entrepreneurship courses in universities in six Latin American countries by evaluating the conditions that affect their satisfaction. Design/methodology/approach Two online surveys were distributed to lecturers and students of entrepreneurship courses. A total of 559 responses were received from students and 270 lecturers from six Latin American countries. The information was analysed through principal component analysis (PCA), correlations and the Mann–Whitney–Wilcoxon test. Findings This research provides new information about the factors that are relevant to student and lecturer satisfaction when moving from mandatory face-to-face to online classes. The results show that overall satisfaction is lower among students than among teachers. Besides, that student communication with the lecturer and technology-supported access to lectures are two important aspects that affect student satisfaction. Regarding lecturers' satisfaction, the participation and attitude of their students in online classes are important conditions that affect their intention to teach online courses in the future. Practical implications This study has allowed us to reflect on online education and teaching methodologies in Latin-American universities after the COVID-19 pandemic, as well as to recognise opportunities for improvement to increase student and lecturer satisfaction in this new reality. Originality/value This study provides new evidence by comparing the satisfaction between lecturers and students in entrepreneurship courses and factors that affect their satisfaction in several Latin American countries, after change to an online modality due to COVID-19.
The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task

The work contains an original comparison of selected algorithms using artificial neural network models, such as RBF neural networks, and classic algorithms, approaches that are based on structured programming in the image identification task. The existing studies exploring methods for the problem of classifying musical notation used in this work are still scarce. The research of neural network based and the classical method of image recognition was carried out on the basis of the effectiveness of recognizing the notes presented on the treble staff. In order to carry out the research, the density of the data distribution was modeled by means of the probabilistic principal component analysis, and a simple regression was performed with the use of a radial neural network. The methods of image acquisition and analysis are presented. The obtained results were successively tested in terms of selected quality criteria. The development of this research may contribute to supporting the learning of musical notation by both beginners and blind people. The further development of the experiments can provide a convenient reading of the musical notation with the help of a classification system. The research is also an introduction of new algorithms to further tests and projects in the field of music notation classification.
Interactive Knowledge Visualization Based on IoT and Augmented Reality

In order to solve the integration value of information technology and education, it is mainly reflected in the container for storing and disseminating information, the problem is that learners lack the proper self-learning ability, and the author proposes an interactive knowledge visualization system based on the Internet of Things and augmented reality technology. According to the applicable characteristics of augmented reality technology applied to IoT data presentation and interaction, the method can analyze and describe its application possibility. In the design of interactive electronic technology computer-aided teaching system based on NET platform, the system hardware structure consists of user interface layer, business selection layer, and data management layer. Users such as teachers and students enter their identity information at the user interface layer to log in to the system and enter the business selection layer and click the corresponding program according to their application requirements, the service selection layer transmits the user’s selection instruction to the data management layer, and the data management layer selects the corresponding resources according to the user’s needs and feeds it back to the user. The interaction of the system is mainly reflected in interactive teaching and information interaction, and interactive teaching is reflected in the online teaching of teachers and students. Information interaction is embodied in the information transmission of the system information interaction model. Experimental results show that after applying the system, the number of students with high self-efficacy increased from 11 to 21 and the proportion increased from 21.4% to 34.8%. The system designed by the author has strong antipressure ability, can respond to the application instructions of a large number of users in real time, and has a good interactive teaching effect, which improves the self-efficacy of students.
IMPROVED NOISE-RESILIENT ISOLATED WORDS SPEECH RECOGNITION USING PIECEWISE DIFFERENTIATION

Speech is the primary method of communication among humans; it is the main form of communication to deliver emotions and thoughts. Intuitively, humans would find it convenient to communicate with machines through speech. The automatic speech recognition model’s primary goal is to transcribe or identify the word sequence represented by the acoustic signals or speech. In this advanced era of digital media and e-learning platforms, the effective use of voice recognition technology to deliver a more individualized and effective educational experience in e-learning initiatives. It helps the students to improve their oral pronunciation skills. Even though Arabic is one of the most spoken languages, the research works related to Arabic speech and text are lacking when compared to other languages. Here, we propose a hybrid model of [Formula: see text]-Nearest Neighbor (KNN) classifier and Dynamic Time Warping (DTW) for implementing a noise-resilient speech recognition system for isolated words in the Arabic language. The model is implemented with Mel-Frequency Cepstral Coefficients (MFCC) and its piecewise first and second derivatives as feature representation models. The proposed model is implemented in Python and simulated using Arabic Speech Corpus for Isolated Words [A. Alalshekmubarak and L. Smith, On improving the classification capability of reservoir computing for Arabic speech recognition, in International Conference on Artificial Neural Networks (Springer, Cham, 2014), pp. 225–232]. The proposed model is implemented with (1) MFCC alone for feature representation, (2) combination of MFCC with Delta coefficients, and (3) combination of MFCC with Delta and Delta–Delta coefficients. The implemented model is evaluated using different test sets of varying sizes of 100, 200, 500, 1000, 1500, and 2000. Evaluation is performed for both noised and noiseless speech using these three feature representation models and performed an evaluation of these three models. For evaluating this model’s performance in noised conditions, both white and babble noises of various signal-to-noise ratio values such as 10 dB, 20 dB, and 30 dB are added into the noiseless speech and estimate the classification accuracy in those situations. The proposed KNN–DTW model with MFCC outperformed all the other models in the literature. MFCC with Delta and Delta–Delta coefficients is effective than other two models using MFCC and MFCC with Delta coefficients. The proposed model could be used to recognize the isolated word recognition of grade 1 textbook vocabulary in classroom noisy environment for providing a better interactive classroom environment to Kuwaiti elementary students to enhance their math learning level. In an interactive computer-based education environment for Mathematics learning, the proposed model could be recognized as the spoken utterances of students.
Blockchain-Enabled Electronic Health Records for Healthcare 4.0

Healthcare delivery is on the verge of a fundamental shift into the new era of smart and connected health care, termed Health Care 4.0. Sharing healthcare data is an important step in improving the healthcare system's intelligence and service quality. Healthcare data, which is a personal asset of the patient, should be owned and managed by the patient rather than being dispersed among several healthcare systems, preventing data exchange and jeopardizing patient privacy. EHRs (electronic health records) assist individuals by allowing them to combine and manage their medical data. On the other hand, today's EHR systems fall short of providing patients with traceable, trustworthy, and secure ownership over their medical data, creating serious security risks. In this article, the authors propose PcBEHR (patient-controlled blockchain enabled electronic health records) as a way for patients to have safe control over their data that is decentralized, immutable, transparent, traceable, and trustworthy. Decentralized interplanetary file storage (IPFS) is used in the suggested technique.
An Improved Sequential Recommendation Algorithm based on Short-Sequence Enhancement and Temporal Self-Attention Mechanism

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art.
Informatization of Accounting System of Electric Power Enterprises Based on Sensor Monitoring and Cloud Computing

In order to promote, restrict, regulate, and balance the accounting work of electric power enterprises, the author proposes an electric power enterprise accounting system based on sensor monitoring and cloud computing. Through the method of integrating information technology on the basis of accounting computerization, through a sensor and modular design analysis, an accounting information management system model is formed. The accounting information management system based on cloud computing combines accounting and management functions. The system test results show that the accounting information management system is in a state of 480 concurrent users, the average response time of a data query is 4.3 seconds, and the average response time of a data access is 5.1 seconds. The average value of the execution data scale per second is 103.52. Conclusion. In order to further improve the accounting software of electric power enterprises, develop management accounting software, and effectively perform the functions of management accounting.