Reviewerly/exHarmony huggingface dataset:
The rise of big data on the development of music education and innovation to promote

Abstract We must seize the chance for growth in the big data era for innovation and optimization to efficiently optimize music education instruction in colleges and universities. This paper applies the SVM method to studying vocal recognition in popular music to build a music learning aid system. The system gives the exact location of the intro, bridge, and outro through the recognition of vocals in pop music, which helps learners to practice in a targeted way. MFCC features are used, and low-pass filtering is applied to the classification results later, resulting in a recognition rate of 85.74% on a frame-by-frame basis. From the test results, SVM has a better generalization ability than other classifiers, including ANN, GMM, and HMM, with a recognition rate of at least 8.84% higher. In the practicality test, the experimental class applied the music-assisted system for learning, and the comparison class used the traditional teaching method. 46 more people in the experimental class rated good music ability than the control class, 68 more rated good innovation status than the control class, and 71 more rated good independent learning ability than the control class. Therefore, big data technology must be used to innovate music education in colleges and universities to increase teaching efficacy successfully.
Construction of an innovative model of English online teaching based on a multimedia English learning system model

Abstract This paper explores a multimedia learning system suitable for students’ independent English learning through the current development of assisted English learning systems, university-level English teaching requirements, and learner characteristics. Based on the model of the proposed multimedia English learning system, in the endpoint detection stage of speech, short-time excess rate and short-time energy are combined to process the endpoint detection of speech. In the final speech comparison stage, the dynamic time planning algorithm DTW is analyzed and improved before the similarity recognition of speech. We also complete the requirement analysis of the English-speaking learning system by combining the relevant theories and applying the speech signal processing technology and template matching technology to realize a multimedia-assisted learning system suitable for college students to conduct independent English learning. Through the English-speaking test of two classes, the control class using traditional online teaching had many failures reaching 25%, while the experimental class using the English-assisted learning platform passed all of them. 80 points or more were achieved by only 5% of the control class, while 20% of the experimental class. The average score of the control class was 63.4, while the experimental class was 72.1, and the experimental class was 8.7 points higher than the control class. Therefore, teaching English-assisted learning platforms can improve students’ English-speaking abilities.
Research on the reform of English teaching based on big data technology

Abstract The rapidly developing big data technology not only directly affects people’s work and life but also plays a greater role in promoting the direction of education. This paper introduces online teaching methods to traditional teaching methods, constituting a hybrid online and offline teaching method, and optimizes the reform of business English teaching objectives, contents and processes. After pre-processing the teaching evaluation indexes, the rough set method based on big data technology is used to mine the association rules of teaching data. The principal component analysis evaluation method was used to evaluate the structure of the mined teaching data set. And the changes in teaching evaluation index values obtained by the principal component analysis method were used as the basis for determining the effectiveness of the business English teaching reform. The experiment proved that: aS10 students in group A with the worst business English level increased their teaching level value from 1.004 to 1.224 after the general reform, an increase of 20.77%. After the online and offline hybrid reform, the teaching level value increased from 1.004 to 1.306, an increase of 22.72%. This shows that the reformed business English teaching mode has deepened the reform and innovation of business English teaching through the full use of big data technology, improved the comprehensive English ability and literacy of the majority of students, and laid a solid foundation for the future development of students’ working career.
Student education management model and formation in universities based on hierarchical analysis

Abstract Information technology is of great significance in building a new college student education management model. This paper first presents the steps of constructing a hierarchical analysis method, which orders the combination of each index affecting the evaluation object to constitute a hierarchical and clear system, analyzes each element layer by layer, and calculates the weight value of each index. Then the process of the fuzzy comprehensive evaluation method is introduced, decomposing the total objective of the evaluation into a fuzzy set composed of several indicators, determining the affiliation function, and establishing the fuzzy matrix. Then the hierarchical analysis method and fuzzy comprehensive evaluation method are combined to evaluate the ability of college student education management. Finally, a new model of college student education management is proposed. Among the existing problems, 78.3% of students think that the construction of online platforms is lagging, and 89.8% think the current Internet platform lacks effective supervision. The weights of “students, education management team, family education, school management system, social environment, and education carrier” are 0.4323, 0.203, 0.08212, 0.13233, 0.10345, and 0.0687, respectively. It is not the sole responsibility of the school but requires the concerted efforts of the school, society, and family.
The impact of modern Internet technology on the teaching of American voice in colleges and universities

Abstract In this paper, firstly, the average prediction rating of interest points is performed by a recommendation model incorporating multiple factors through probabilistic matrix decomposition to improve the accuracy of the beauty teaching features obtained by matrix decomposition. Then, we combine the collaborative filtering recommendation algorithm and propose a recommendation model called TGSS-MF, and optimize the TGSS-MF recommendation model through the model of neural network for the sparse data problem faced by the interest point recommendation and the hidden feature vector representation problem of users and interest points, and finally use the TGSS-MF recommendation model to analyze the user needs of teachers, students and system administrators who are involved in teaching and learning. Finally, the TGSS-MF recommendation model is used to analyze the needs of users such as teachers, students, and system administrators involved in teaching and learning. A mobile teaching platform is designed to meet the characteristics of American voice teaching in colleges and universities. The performance analysis of the TGSS-MF recommendation model shows that when k=10, the accuracy and recall of the TGSS-MF model in the two data sets are 0.095 and 0.113, respectively, which are better than the other three algorithms in both accuracy and recall. This study can present more rich resources to students through modern Internet technology, which can help students learn effectiveness.
A Maximum Semantic Reservation Mapping Method Based on Ontology-to-graph Database

&lt;p&gt;Ontology is a core concept model in a knowledge graph which describes knowledge in the form of a graph. With the increase in knowledge graphs, the semantic relationships between concepts become more and more complex, which increases the difficulty of reserving its semantic integrity when storing it in a database. In this paper, we propose an ontology-to-graph database mapping method, which can reserve maximum semantic integrity and reduce redundant information simultaneously with high storage efficiency and query efficiency. In detail, the mapping method uses an anonymous class storage strategy to handle indefinite long nested structures, a multivariate functional relation storage strategy for multivariate semantic analysis, and an SWRL (Semantic Web Rule Language) storage strategy for disassembling inference structures. We develop an ontology-to-graph database prototype Neo4J4Onto to implement the mapping method. Experimental results show that our method achieves the maximum semantic integrity with the lowest complexity compared to the 6 baseline methods. Besides, compared to graphDB, Neo4J4Onto has better storage and query efficiency, and the concept models retrieved by Neo4J4Onto are more complete.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Recognition of Indian Gestural Language Through Neural Networks: Narrative Approach

Gesture-based communication acknowledgment is useful in correspondence between marking individuals and non-marking individuals. Different research projects are underway on various communication via gesture acknowledgment frameworks around the world. The examination is restricted to a specific country as there are countrywide varieties accessible. In this paper, the hand signals compared to ISL English letters in order are caught through a webcam. In the caught outlines, the hand is divided and the neural organizations are utilized to perceive the letters in order. The elements like points made between fingers, the number of fingers that are completely opened, completely shut, or semi-shut, and recognizable proof of each finger are utilized as contributions to the neural organization. Trial and error were accomplished for single-hand letter sets, and the results are summed up. In this paper, we have mentioned a comparison of various techniques for using recognition of gestural language like a neural network, CNN, deep learning, artificial neural network, and autoencoder work done for determining better results.
Bridging Education and Sustainable Development: Assessing Future Psychologists’ Perspectives

Education serves as a dynamic interplay between generations, encompassing dialogue and mutual enrichment. It embodies the spiritual essence inherent in individuals, communities, eras, and cultural contexts, shaped by moral and spiritual values specific to cultural circles. Education bridges the gap between the ‘values of the environment’ and the ‘values of life experience’, defining the persona of individuals, communities, and societies. It functions as a repository of intellectual potential, dismissing the notion of ‘excessive’ education and highlighting its indispensability for human growth. Values, pivotal to human intellectual exploration, encapsulate the initiation, propagation, or negation of philosophical paradigms within fluctuating contexts. Education emerges as a distinctive manifestation of self-cultivation and a testament to human accomplishments. This study aimed to assess sustainable social development awareness and implementation readiness among future psychologists. The investigation involved 160 Ukrainian psychology students, employing written surveys and statistical analysis. Findings unveiled a limited comprehension of sustainable societal development among respondents, often narrowed to environmental concerns and overlooking the social facet. Despite this, over half of the participants sought alignment with their interpretation, integrating these ideals into their university environment. The ongoing conflict in Ukraine intensified the urgency of instilling sustainable development values, though a third of the respondents fully embraced these concepts. Implementation challenges included consumeristic attitudes towards natural resources and low environmental consciousness, highlighting the need for value propagation and social responsibility cultivation. Respondents recognized the social dimension’s salience in their future professional roles, albeit with a tendency to overlook intergenerational continuity concerns. The study exposed limited and fragmented competence in understanding sustainable societal development among future psychologists, with value cultivation appearing spontaneous and disjointed. The university environment presents an opportunity to nurture sustainable development values, urging educators to augment students’ competency and motivational preparedness for their roles in implementing these concepts.&#x0D; Keywords: sustainable development, education, values, future psychologists, professional training, awareness, societal progress, professional roles.
Continuing to evolve: opportunities to share technology enhancements with health sciences library peers through the Virtual Projects Section

Beginning in 2012, the Virtual Projects section of the Journal of the Medical Library Association has provided an opportunity for library leaders and technology experts to share with others how new technologies are being adopted by health sciences libraries. From educational purposes to online tools that enhance library services or access to resources, the Virtual Projects section brings technology use examples to the forefront. Virtual Projects highlighted in this year's section include new ways to use virtual reality for library instruction, podcasting to share important health care messages with the Latino Community, enhancing findability by using options in a library management system, and developing a research profiling system. After a hiatus due to publishing changes in 2022, 2023 will bring some major changes for the section. The new publication issue for future Virtual Projects sections will be January and the call for submissions and Virtual Projects deadline will now take place in June and July.
The First Workshop on Quantum Systems and Computations (2023)

It is of great pleasure to host the first Workshop on Quantum Systems and Computation, which was held on June 19, 2023 at Orlando, Florida.
Special Issue on The Workshop on MAthematical performance Modeling and Analysis (MAMA 2023)

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and tradeoffs at the core of performance problems in the design and implementation of complex computer systems, networks and applications.
A NetAI Manifesto (Part I): Less Explorimentation, More Science

The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving realworld network security and performance problems (NetAI, for short) has generated great excitement among networking researchers. However, network operators have remained very reluctant when it comes to deploying NetAI-based solutions in their production networks, mainly because the black-box nature of the underlying learning models forces operators to blindly trust these models without having any understanding of how they work, why they work, or when they don't work (and why not). Paraphrasing [1], we argue that to overcome this roadblock and ensure its future success in practice, NetAI "has to get past its current stage of explorimentation, or the practice of poking around to see what happens, and has to start employing tools of the scientific method."
Detection of Co- Existing RF Signals in CBRS Using ML: Dataset and API-Based Collection Testbed

Opening up of spectrum for shared use, such as the Citizen Radio Broadband Service (CBRS) band, offers unprecedented opportunities for allowing commercial operators to operate in frequencies otherwise reserved for federal use only. Specifically in the CBRS band, the challenge of detecting the highest priority incumbent radar reliably forces severe restrictions on the transmit power for operators deploying LTE networks. While Machine Learning (ML)-based solutions have demonstrated the potential for detecting weak radar signals in fully overlapping secondary signals, there exists a fundamental gap in porting these methods for practical, real-world conditions due to a key reason: There are no accessible data-sets or even controlled methods to generate such datasets today over-the-air (OTA), where radar and LTE ‘overlap’ in a number of challenging SINR conditions. This article makes three contributions: It describes the first publicly available CBRS over-lapping and non-overlapping LTE and radar OTA dataset in the 3.5 GHz band using an experimental testbed composed of software defined radios; It describes the first-of-its-kind open source Application Programming Interface (API) that can configure automatically multiple transmitters and receiver radios, synchronize them, remove the Tx local oscillators-induced artifacts, and carefully set their parameters such as sampling rates, center frequencies, and time duration for sample collection, ultimately resulting in high-fidelity data in the Signal Metadata Format (SigMF); It demonstrates the utility of the CBRS dataset by adapting the well-known ML model called “You Only Look Once” (YOLO) for detecting and localizing the radar and LTE signals with near-perfect accuracy, pointing to the possibility that current FCC-mandated power thresholds can be lowered for cellular operators in the CBRS band.
Trainable unravelling for quantum state discrimination

We propose a way for partial compensation of maladaption of decoder to non-ideal quantum communication channel by means of optimal choice of unravelling of the decoding operation. No physical modification of the decoder itself is required. We show that it is sufficient to add an interface that modifies the decoder environment interaction. Tuning of this interface can be done by methods of (quantum-inspired) machine learning. We suggest a search algorithm for an optimal unravelling, as an alternative for the classical gradient descent method.
Forecasting Bitcoin Prices using Deep Learning for Consumer Centric Industrial Applications

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision.
Welcome from IEEE AIC 2023 General Chair

Welcome to Varanasi, India and to the IEEE World Conference on Applied Intelligence and Computing (AIC 2023), jointly organised by Rajkiya Engineering College, Churk, Sonbhadra and Ashoka Institute of Technology, Varanasi, India, on the June, 29-30, 2023 in mixed mode. This conference is financially sponsored by IEEE Madhya Pradesh Section, and Technically Sponsored by UP Section. The theme of the conference is related to the fast developing technology in the area of Soft Computing, AI, IOT, Data analytics and Intelligent Systems. A fundamental stimulus to the investigations of Intelligent Systems is the awareness in the academic communities and industry, the combined approaches will be necessary if the remaining tough problems in Artificial and computational intelligence are to be solved. Recently, Hybrid Intelligent Systems are getting popular due to their capabilities in handling several real world complexities involving imprecision, uncertainty and vagueness. It will be fruitful if recent developments in the communication technology are taken into consideration and are put on test by using global network available and the advancements are taken care of.
A Heterogeneous Computer Architecture Accelerating Reinforcement Learning-based Design for Silicon Photonic Devices

This paper proposes a framework to substantially accelerate Reinforcement Learning (RL)-based design method for Photonic Integrated Circuit (PIC) devices. PICs are widely anticipated to underpin the forthcoming generation of semiconductor chips. However, the complexity of PIC design, which includes hundreds of degrees of freedom (DOF), presents considerable challenges. Machine Learning (ML) techniques, inclusive of RL, have demonstrated their effectiveness in the domain of PIC design. The primary hurdle, however, is the extended training duration, primarily constrained by the sluggish electromagnetic (EM) solver, specifically, the Finite-Difference Time-Domain (FDTD) solver. We have engineered a novel computational architecture that can be deployed on cloud-based systems using a cluster of Central Processing Units (CPUs), Field-Programmable Gate Arrays (FPGAs), and Graphics Processing Units (GPUs). An FPGA-FDTD accelerator, which capitalizes on the high memory bandwidth of on-chip memory (OCM), has been specifically designed to simulate planar PIC devices. Each FPGA-FDTD accelerator, also denoted as an FPGA kernel, functions as an autonomous RL environment. The host machine, in conjunction with the FPGA kernel, is designated as a worker node within the cluster. A functional prototype has been successfully implemented on the Amazon Web Services (AWS) cloud. The framework has effectively designed numerous PIC devices, and experimental results show the architecture outperforms existing methods significantly in design speed while maintaining or exceeding their design quality. Notably, the framework's versatility requires minimal adjustments for a broad range of devices, significantly reducing design time and promising to expedite PIC innovation.
Architecture Optimization of a CNN Media Noise Estimator for TDMR

We investigate several architectures for a convolutional neural network (CNN) media noise estimator (MNE) that sends media noise estimates to a three-track Bahl-Cocke-Jelenik-Raviv (BCJR) TDMR detector. While novel CNN architectures have previously been proposed for image classification problems, here the CNN estimates media noise based on inputs from the BCJR detector and from a linear partial response equalizer. The CNN architecture used in our previous work on CNN-based TDMR detection is used as a comparison baseline. Experiments show that an optimized CNN MNE with three residual path connections achieves up to a 3.2% reduction in mean squared estimation error and a 71.9% reduction in computational complexity compared to the baseline.