Reviewerly/exHarmony huggingface dataset:
Mel Power Spectrogram Approximation By Tiny Neural Networks for Home Appliances Classification

Mel power spectrogram has been widely deployed in audio preprocessing for signal conditioning and feature extraction. Between many available, one of the most used software libraries is named Librosa. This paper investigates how the Mel power spectrogram algorithm can be approximated by tiny neural networks. The proposed approach would allow to build an end-to-end multitask deep neural pipeline coded by using Keras layers. By connecting proposed approximator to a classifier, it is possible to estimate the Mel spectrogram and achieve adequate classification accuracies. These approaches have been tested with WHITED, COOLL and PLAID datasets for electric current classification of home appliances. The approximator achieved on the WHITED dataset the mean square error of 1.46 e-03, on COOLL was 0.396 and 1.84 on PLAID. The classification of the home appliance types achieved best accuracy of 96.43% for 7 classes on WHITED, 95.63% for 5 classes on COOLL and 76.61% for 6 classes on PLAID. The complexity of the tiny networks has been set and profiled with respect to the limited memory requirements of the off-the-shelf ARM Cortex M4 and M7 microcontrollers. The deployment on hardware boards has been conducted too. The inference time on the ARM Cortex M4 was 5092ms, while on M7 was 814.7 ms.
Fully Automatic Gym Exercises Recording: An IoT Solution

In recent years, working out in the gym has gotten increasingly more data-focused and many gym enthusiasts are recording their exercises to have a better overview of their historical gym activities and to make a better exercise plan for the future. As a side effect, this recording process has led to a lot of time spent painstakingly operating these apps by plugging in used types of equipment and repetitions. This project aims to automate this process using an Internet of Things (IoT) approach. Specifically, beacons with embedded ultra-low-power inertial measurement units (IMUs) are attached to the types of equipment to recognize the usage and transmit the information to gym-goers and managers. We have created a small ecosystem composed of beacons, a gateway, smartwatches, android/iPhone applications, a firebase cloud server, and a dashboard, all communicating over a mixture of Bluetooth and Wifi to distribute collected data from machines to users and gym managers in a compact and meaningful way. The system we have implemented is a working prototype of a bigger end goal and is supposed to initialize progress toward a smarter, more efficient, and still privacy-respect gym environment in the future. A small-scale real-life test shows 94.6% accuracy in user gym session recording, which can reach up to 100% easily with a more suitable assembling of the beacons. This promising result shows the potential of a fully automatic exercise recording system, which enables comprehensive monitoring and analysis of the exercise sessions and frees the user from manual recording. The estimated battery life of the beacon is 400 days with a 210 mAh coin battery. We also discussed the shortcoming of the current demonstration system and the future work for a reliable and ready-to-deploy automatic gym workout recording system.
Construction of a personalized learning platform based on genetic algorithm for specialized education in industrial colleges in the context of industry-education integration

Abstract Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education of industrial colleges has become one of its key contents. This paper mainly studies the necessary system design based on characteristics of genetic calculation for specialized education in industrial colleges under industry-education integration. This paper first introduces the principle of genetic algorithm. It then proceeds to introduce in detail the shortcomings of The advantage of this calculation method is that the standard calculation tendency is unable to move forward in an area extremes, the iteration is relatively slow, and the low accuracy when dealing with optimization problems. Then the design and construction ideas of the personalized platform and the database design from Learners, educators (teachers), and administrators are compared by experimentally analyzing the data diversity and convergence of the three algorithms on the database. Experimentally, the diversity of the self-adaptive genetic algorithm is maintained at 0.8, and the diversity, iteration time, and convergence standard are better compared with the comparison subject. Using a self-adaptive genetic algorithm to evaluate and rank learners’ personality differences and to provide and recommend suitable learning methods for learners helps develop education and improve teaching quality and has great historical significance for China’s development and international status.
Strategies for the implementation of aesthetic education in music teaching in colleges and universities under the threshold of big data

Abstract Under the background of big data development, students have diverse and multi-level demands for music aesthetic education teaching. This paper studies the implementation strategy of aesthetic education in college music teaching under the threshold of big data, mainly through data analysis, predictive modeling and simulation, multi-faceted analysis, and the design of personalized music teaching intervention. By training DNN model experimental conclusion and analysis, the DNN model in the original set R-squared reached 0.8037, the largest performance among all models, compared to multiple linear regression, in multi-level multi-neuron activation, through the sigmoid activation of each level and linear calculation between levels, in backpropagation to update the weights and bias, trained to be able to more The DNN is finally selected as the best model for music learners’ performance prediction. The DNN is finally selected as the best model for music learners’ performance prediction. The advantages of big data mining music aesthetic education resources are exploited to create an open aesthetic education teaching space for students.
The improvement path of international Chinese education management in universities based on big data technology

Abstract The short video under the wave of the big data era is as crazy as the rain, with all kinds of online hot words abuse, subvert the three views the pompous funny video, using Chinese text stems lead to many college students and teachers Chinese culture level decline, Chinese culture awareness is weak, Chinese education management along the traditional, education management can not keep up with the innovation of the times to study the problem. In this paper, the improvement path of international Chinese education management in colleges and universities based on big data technology is studied, and the incremental update model of data is constructed by calculating the dissimilarity of users and items and getting the dissimilarity matrix. According to the neighborhood rating data, the iu-User CF algorithm provides users with more qualified items. The experimental analysis results show that the algorithm maintains more than 90% accuracy and stability. It shows that the system library of the algorithm is stable and efficient. Education management should innovate development ideas, broaden horizons, adapt to the new era, new situation, and new trends, seize new development opportunities, explore new paths for reform, unite Chinese and foreign cooperation, serve world needs, and promote digitalization, intelligence, and modernization of international Chinese education.
Management path optimization of accordion music course education based on big data technology analysis

Abstract With the continuous development of information technology, especially the rapid development of big data technology, the coverage and effectiveness of information application and dissemination have increased substantially. With the current innovation and development of music management education mode, the traditional accordion course management mode also changed, this paper makes full use of big data technology to optimize the accordion course education management path, mainly through the construction of an accordion score database and retrieval system, the closer the data mining algorithm calculation result is to 1, it means that the data mining result is more similar to the actual result. The experimental analysis result is equal to 1, which indicates that the data mining result of this study is similar to the actual result, which is conducive to the intelligent management under the analysis of big data technology for music course education, improving students’ learning interest and passion, improving the teaching quality of learning, and facilitating the teaching management work of university leaders. And it can be further based on the big data era, grasp the trend of the big data era, and understand the level, learning needs, and personal characteristics of students, analysis can be seen that accordion course education management based on big data can make different learning plans for different levels of students, which is conducive to improving management quality and efficiency at the data level.
Research on the optimization of the path of the agricultural logistics industry under the innovative mode of “blockchain + supply chain finance”

Abstract To promote the development and application of “blockchain+supply chain finance” in the agricultural logistics industry, this paper uses the ECC algorithm in the blockchain model as the basis and implements an asymmetric encryption algorithm based on elliptic curve mathematical theory to optimize the ECC algorithm. Based on the ECC algorithm in the blockchain model, this paper implements an asymmetric encryption algorithm based on elliptic curve mathematical theory to optimize the ECC algorithm. Combining the dynamic variational particle swarm optimization algorithm and the AdaBoost integration algorithm, the DPSO-SVM algorithm is weighted and combined into a strong classifier, and an evaluation model based on AdaBoost-DPSO-SVM is established, and the model is compared with other models. The experimental results show that the data platform based on “blockchain + supply chain finance” can well solve the problem of loss of confidentiality in the process of identifying agricultural products in the agricultural logistics industry. This also shows that through the way of “blockchain + supply chain finance”, it can provide a better development path for the agricultural logistics industry and can truly achieve low-cost commercial communication, i.e. multi-functional integration development.
Research on the application of virtual reality technology in the teaching resource library of film and animation

Abstract With the rapid development of computer communication technology, virtual reality technology is getting more and more attention. The application of virtual reality technology allows participants to enter the virtual environment and can interact with people or objects in the virtual environment, from the process to producing real feelings, the current reform of university teaching has promoted the continuous improvement of many universities in the teaching process, the application of virtual reality technology to support film and television animation teaching activities is one of the ways. This paper combines the actual situation of the application of virtual equipment in the animation teaching resource library, and elaborates on the shortcomings of the traditional animation teaching mode and the application effect of modern virtual reality technology equipment in the animation teaching resource library, aiming to provide an effective reference for film and television animation teaching, to effectively improve the talent cultivation mode of colleges and universities and significantly improve the teaching quality in both teaching and learning.
BM3D denoising-based multi-target detection method for complex background radar images

Abstract For the complex background of traditional radar image target detection methods is affected by multiple interferers, which leads to a low recognition rate of multi-target classification and a high false alarm rate. In this paper, the current hot research BM3D denoising method is introduced into radar image target detection. Firstly, the detection process of the BM3D algorithm is constructed, and the image pixels are reasonably clustered by denoising so that the image blocks in homogeneous regions have a high matching degree and those in non-homogeneous regions have a low matching degree. Secondly, the clustering recognition feature is used to limit the search range of similar blocks to homogeneous regions, to improve the denoising efficiency of the algorithm. Finally, the simulation results of this paper show that compared with previous algorithms, the BM3D detection method has significantly improved in both the subjective visual effect map and objective numerical indexes. For example, the image denoising quality is improved by about 0.716dB on average and up to 1.031dB, the image denoising time is shortened by about 19.962s on average, and the algorithm runs 1.211 times faster than the original algorithm. It is proved that the algorithm can improve denoising efficiency and reduce computational complexity while enhancing detailed information.
Research and practice of blaze new trails and start an undertaking cultivate model based on big data technology in computer special field of study

Abstract With the advent of the era of big data and cloud computing, big data is more and more widely used in various industries, and the research and practice of using big data technology to realize blaze new trails and start an undertaking cultivate mode in computer special field of study will become an important focus of the blaze new trails and start an undertaking cultivate system nowadays. In this paper, we construct the blaze new trails and start an undertaking cultivate model based on BP ANN, construct the blaze new trails and start an undertaking cultivate evaluation index system through Delphi method, and finally design the model of blaze new trails and start an undertaking cultivate evaluation in order to analyze the results of blaze new trails and start an undertaking cultivate. The scoring results correspond to the data of 21 evaluation indexes of a student, and the various scores can map to the level of individual students’ blaze new trails and start an undertaking ability, and provide effective countermeasures for computer special field of study students’ blaze new trails and start an undertaking cultivate. This paper effectively evaluates the blaze new trails and start an undertaking ability of students, broadens the application value and research field of BP ANN, and actively explores the blaze new trails and start an undertaking cultivate of computer special field of study students, which helps the operation mode of blaze new trails and start an undertaking cultivate of computer special field of study special field of study, provides step foreward ideas, and is of great significance to promote the step forward of blaze new trails and start an undertaking cultivate mode.
A New Inquiry into the Ideological and Political Education of College Students in the Context of Free Trade Port

Abstract Ideological and political education is the fundamental guarantee to realize the goal and direction of college students’ talent training, and the key factor of Hainan free trade port construction is talent. Therefore, this paper carries out a new exploration of the ideological and political education of college students based on the background of free trade ports and selects the SPOC hybrid teaching model to carry out the ideological and political education of college students. Using the AHP-FCE evaluation mechanism, we construct the AHP three-level evaluation indexes and establish the FCE evaluation factor set. By calculating the affiliation matrix and hierarchical weight vector of the three-level index set, we make a comprehensive evaluation of the feasibility and effectiveness of the SPOC hybrid teaching model in ideological and political teaching and prove that it can improve students’ ideological and political performance and cultivate talents. In this paper, we conducted experiments in five colleges and universities and proved that students’ performance in ideology and politics under the SPOC hybrid teaching model increased from only 60 passing marks to more than 75 marks per capita. The teachers’ teaching time of ideology and politics also increased by at least 5 hours and at most 7 hours based on the original one. It can be seen that the SPOC teaching model applied to the ideological and political education of college students can significantly improve the ideological and political achievements of students, and can provide a steady stream of high-quality talents for the construction of the Hainan free trade port.
PFedSA: Personalized Federated Multi-Task Learning via Similarity Awareness

Federated Learning (FL) constructs a distributed machine learning framework that involves multiple remote clients collaboratively training models. However in real-world situations, the emergence of non-Independent and Identically Distributed (non-IID) data makes the global model generated by traditional FL algorithms no longer meet the needs of all clients, and the accuracy is greatly reduced. In this paper, we propose a personalized federated multi-task learning method via similarity awareness (PFedSA), which captures the similarity between client data through model parameters uploaded by clients, thus facilitating collaborative training of similar clients and providing personalized models based on each client’s data distribution. Specifically, it generates the intrinsic cluster structure among clients and introduces personalized patch layers into the cluster to personalize the cluster model. PFedSA also maintains the generalization ability of models, which allows each client to benefit from nodes with similar data distributions when training data, and the greater the similarity, the more benefit. We evaluate the performance of the PFedSA method using MNIST, EMNIST and CIFAR10 datasets, and investigate the impact of different data setting schemes on the performance of PFedSA. The results show that in all data setting scenarios, the PFedSA method proposed in this paper can achieve the best personalization performance, having more clients with higher accuracy, and it is especially effective when the client’s data is non-IID.
GraphMetaP: Efficient MetaPath Generation for Dynamic Heterogeneous Graph Models

Metapath-based heterogeneous graph models (MHGM) show excellent performance in learning semantic and structural information in heterogeneous graphs. Metapath matching is an essential processing step in MHGM to find all metapath instances, bringing significant overhead compared to the total model execution time. Even worse, in dynamic heterogeneous graphs, metapath instances require to be rematched while graph updated. In this paper, we observe that only a small fraction of metapath instances change and propose GraphMetaP, an efficient incremental metapath maintenance method in order to eliminate the matching overhead in dynamic heterogeneous graphs. GraphMetaP introduces a novel format for metapath instances to capture the dependencies among the metapath instances. The format incrementally maintains metapath instances based on the graph updates to avoide the rematching metapath overhead for the updated graph. Furthermore, GraphMetaP uses the fold way to simplify the format in order to recover all metapath instances faster. Experiments show that GraphMetaP enables efficient maintenance of metapath instances on dynamic heterogeneous graphs and outperforms 172.4X on average compared to the matching metapath method.
TurboHE: Accelerating Fully Homomorphic Encryption Using FPGA Clusters

With the burgeoning demands for cloud computing in various fields followed by the rising attention to sensitive data exposure, Fully Homomorphic Encryption (FHE) is gaining popularity as a potential solution to privacy protection. By performing computations directly on the ciphertext (encrypted data) without decrypting it, FHE can guarantee the security of data throughout its lifecycle without compromising the privacy. However, the excruciatingly slow speed of FHE scheme makes adopting it impractical in real life applications. Therefore, hardware accelerators come to the rescue to mitigate the problem. Among various hardware platforms, FPGA clusters are particularly promising because of their flexibility and ready availability at many cloud providers such as FPGA-as-a-Service (FaaS). Hence, reusing the existing infrastructure can greatly facilitate the implementation of FHE on the cloud.In this paper, we present TurboHE, the first hardware accelerator for FHE operations based on an FPGA cluster. TurboHE aims to boost the performance of CKKS, one of the fastest FHE schemes which is most suitable to machine learning applications, by accelerating its computationally intensive and frequently used operation: relinearization. The proposed scalable architecture based on hardware partitioning can be easily configured to accommodate high acceleration requirements for relinearization with very large CKKS parameters. As a demonstration, an implementation, which supports 32,768 polynomial coefficients and a coefficient bitwidth of 594 decomposed into 11 Residue Number System (RNS) components, was deployed on a cluster consisting of 9 Xilinx VU13P FPGAs. The cluster operated at 200 MHz and achieved 1096 times throughput compared with a single threaded CPU implementation. Moreover, the low level hardware components implemented in this work such as the NTT module can also be applied to accelerate other lattice-based cryptography schemes.
An Adaptive Hybrid Quantum Algorithm for the Metric Traveling Salesman Problem

In this paper, we design, analyze, and evaluate a hybrid quantum algorithm for the metric traveling salesman problem (TSP). TSP is a well-studied NP-complete problem that many algorithmic techniques have been developed for, on both classic computers and quantum computers. The existing literature of algorithms for TSP are neither adaptive to input data nor suitable for processing medium-size data on the modern classic and quantum machines. In this work, we leverage the classic computers’ power (large memory) and the quantum computers’ power (quantum parallelism), based on the input data, to fasten the hybrid algorithm’s overall running time. Our algorithmic ideas include trimming the input data efficiently using a classic algorithm, finding an optimal solution for the post-processed data using a quantum-only algorithm, and constructing an optimal solution for the untrimmed data input efficiently using a classic algorithm. We conduct experiments to compare our hybrid algorithm against the state-of-the-art classic and quantum algorithms on real data sets. The experimental results show that our solution truly outperforms the others and thus confirm our theoretical analysis. This work provides insightful quantitative tools for people and compilers to choose appropriate quantum or classical or hybrid algorithms, especially in the NISQ (noisy intermediate-scale quantum) era, for NP-complete problems such as TSP.