Reviewerly/exHarmony huggingface dataset:
Novel Federated Learning by Aerial-Assisted Protocol for Efficiency Enhancement in Beyond 5G Network

Federated Learning (FL) is an important Machine Learning (ML) technique in a decentralized computing environment. For FL in the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation (5G) and beyond networks, there is a need to improve the link capacity, latency, and reliability for every iteration. This would help the user equipment (UE) to efficiently download the global ML model from the FL server, upload the partially trained model from the UE, and update the global ML model hosted on the FL server. However, due to limited coverage areas of terrestrial mobile networks and limited bandwidth at the periphery of a terrestrial cell, the UEs may be out of coverage or have low bandwidth to support the iterations for FL, lowering the performance of FL. To enhance the performance, we propose using aerial cells to augment the existing terrestrial infrastructure and offer enhanced coverage and capacity to the operators. There has been limited work on the management of aerial links in an augmented deployment to improve the performance of FL. To the best of our knowledge, for the first time, we present and analyze a novel Federated Learning by Aerial-Assisted Protocol (FLAP) that selects and manages the aerial link for the federated UEs in each training iteration. FLAP will improve the reliability and latency of the FL process. Through call flow analysis and extensive simulation, we have shown an increase of 50% in the number of federated UEs taking part in FL. Our results reveal that FLAP enables a much faster convergence for an image classification model with much higher accuracy.
A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming

The growth of streaming data originating from Internet of Things (IoT)-based Industry 4.0 opens doors to real-time analytics of time-sensitive services. However, this ever-increasing amount of data inevitably leads to anomalies, resulting in considerable risks for time-sensitive applications. Thus, real-time detection of anomalies is critical to prevent impending failures and resolve them in time. Given that the problem is to detect application-level anomalies in real time, we develop a deep learning-based technique, which integrates time-series data inference with a Long-Short Term Memory (LSTM)-based prediction model. Our proposed method relies on a novel metric called Sequence Inconsistency Distance (SID), which determines the abnormality likelihood of a target record in real time. Our trace-driven evaluations indicate that the proposed method achieves up to a 92.6% performance gain compared to the current state-of-the-art anomaly detection methods in terms of true positive and false positive rate while meeting the essential efficiency requirements.
Architecture Design of Intelligent Blockchain Consensus Algorithm in Digital Development of Sports Industry Chain

Blockchain has become a new technology development trend, and consensus algorithm, as the core technology of blockchain, has also received more and more attention and attracted extensive interest in the academic community. In order to solve the shortcomings of the existing research on the digital development of the sports industry chain, this paper discusses the concept of the sports industry, the composition of the sports industry chain, the blockchain architecture and the RDPoS consensus algorithm, and then investigates and discusses the consensus algorithm computing platform and node evaluation. The digital platform of sports industry chain is designed. And the application scheme of the intelligent blockchain consensus algorithm in the fund side privacy protection of the sports product industry chain sales system was designed. Through the experimental analysis of the algorithm throughput in the transaction process, the experimental data showed that the RDPoS algorithm throughput reached 1000 when the number of nodes was 100 and 125, which was better than the other two consensus algorithms. Therefore, the application of this algorithm in the framework of digital development of sports industry chain has a good effect.
Human Activity Recognition with Anomaly Prediction for E-Health Systems using Lightweight AI

Computing devices should really be mindful of human actions in order for flourishing human-assistive systems to be used effectively in medical services and human-robot collaboration operations. It is necessary to create a trustworthy real-time action recognition mechanism for the uninterrupted and error-free functioning of such smart devices. This can only be done with simple, intelligent methods that make advantage of all-around sensors. With both the connection of time series information in mind, this work presents a fresh way to information architecture for improved extracting features. Using the assistance of a specially created iOS application, the movement data were gathered using a smartphone. In order to identify significant both spatial and temporal elements from the signals, data from eight operations were structured into single and double channels. The Fourier and wavelet domains were used to depict raw data alongside the time domain. Other CNN models that were employed to meet the deep-learning classification of the tasks fared worse than a deep neural network that utilized a double-channeled time-domain input. Several public datasets were used to further examine this strategy, and improved results were obtained. Ultimately, the learned model's applicability was evaluated in real time on a PC and a smartphone, yielding encouraging results.
Online Aesthetic Education Information Teaching System Based On Big Data Cloud Platform

With the continuous in-depth integration and innovation of emerging information technologies represented by big data, cloud computing, artificial intelligence and mobile Internet with higher education, higher education has ushered in unprecedented strategic opportunities in the field of innovating education and teaching methods and building high-quality online teaching resources. Aesthetic education in Colleges and universities undertakes the important task of improving students' aesthetic and humanistic accomplishment and cultivating their aesthetic appreciation, expressiveness and creativity on the basis of specialization. Its teaching reform is also imperative. The big data cloud platform-based online aesthetic education information teaching system is a web-based application that offers online training and instruction for aesthetic education specialist. The system uses the latest technology to easily access various courses, including beauty, massage therapy and other related subjects. In addition, it allows users to create their own courses by uploading content from any source they want.
Adversarially Trained Variational Auto-Encoders With Maximum Mean Discrepancy based Regularization

In recent times, generative modeling (GM) has gained much popularity in machine intelligence methods, based on its similar likeness to human intelligence. They have demonstrated a remarkable capacity for creating very realistic bits of information in a variety of formats, including texts, images, and sounds. Generative models such as Generative adversarial networks (GANs) and variational auto-encoders (VAE) are the two methods that provide better solutions for generative tasks. However, VAE have certain limitations. But it might be more useful to learn using feature space distributions, rather than the more direct input space distribution. In this work, maximum mean discrepancy based auto-encoders with generative adversarial networks (MMDAEGAN) model is proposed. The proposed work introduces a novel set of loss functions for training such a network on generative tasks. The experimental results show that the proposed model produces better accuracy and less error value on reconstruction of sampled the MNIST and fashion datasets than the baseline model considered for comparison.
The Innovation of Enterprise Management Mode of Digital Economy Based on Blockchain Technology

Blockchain is becoming an important leader in a new round of technological and industrial changes. Blockchain technology is in a rapid development stage, and its application scenarios are expanding and now cover many fields. Effectively promoting the deep integration of blockchain and the real economy and promoting blockchain technology to empower the real economy has become a new engine to accelerate the transformation and upgrading of enterprises. The purpose of this paper is to study the innovation of enterprise management mode of digital economy based on blockchain technology. Integrating the principles of blockchain technology into the management activities of enterprises, the impact of blockchain technology on the management activities of e-commerce enterprises is analysed for the e-commerce industry, which is currently a relatively popular sector in the digital economy enterprises, and corresponding suggestions are put forward. By defining variables and constructing theoretical models, conducting research hypotheses on the impact of blockchain on innovation in digital economy enterprises, using questionnaires and analyzing data using SPSS and AMOS software, we obtain that the impact of blockchain on innovation in digital economy enterprises has a positive influence role, in which the integration of innovation resources plays a mediating role.
An Improved Nginx Dynamic Load Balancing Algorithm

To solve the load skew problem caused by the built-in load balancing algorithm of Nginx, this paper proposes an improved dynamic load balancing algorithm based on weighted polling. The algorithm monitors the actual load of the background server and adjusts the weight dynamically to achieve load balancing. Firstly, the indicators such as CPU usage and memory usage are collected periodically, and the number of connections and the number of processes are introduced as statistical load factors. The connection number weight and the number of processes weight are calculated according to the connection number and the number of processes. Finally, the three weights are added together to adjust the server weight. The performance of other algorithms built in Nginx is compared with the algorithm proposed in this paper. The results show that the dynamic algorithm proposed in this paper has higher actual concurrency and lower average response time under high concurrent requests, and can better achieve load balancing and improve the performance of the server cluster.
Digital Music Copyright Protection System Based on Blockchain

With the development of global informatization and digitalization, a large number of digital content works are produced, such as e-books, music, videos and so on. The copyright registration mechanism provided by the traditional third-party copyright management platform can no longer meet the needs of the rapid development trend of digital works. This paper proposes a digital music copyright system based on blockchain. On the Ethereum platform, smart contracts are designed to achieve copyright registration, music authentication and music crowdfunding. Digital signature technology is used to realize the authentication and authorization of transaction information. The Shazam algorithm is used to extract the audio fingerprint. The Interplanetary File System (IPFS) stores the extracted audio fingerprint, and the blockchain stores the hash value generated by IPFS. Compared with the existing system, the function of this system is more perfect. And innovatively through smart contract and digital signature technology, this system realizes the authentication and authorization of transaction information. In particular, it ensures the authenticity of the crowdfunding campaign.
Image Repair and Restoration Using Deep Learning

The art of image restoration lies in finding missing information inside the image and returning it back to its original state. While the field of fine arts and photographic restoration has been known and studied for years, recent technological advances have eliminated time-consuming processes and made image restoration far more feasible and easier to do. A novel method known as image inpainting has become a visionary tool for researchers in the field of image processing as digital picture editing has become more adaptable and image processing tools have improved. To understand how this method has become invaluable especially in the field of computer vision, we choose a deep learning technique that accepts multiple damaged photos as input undergoes training to construct a model to recognize missing information. We then validate the performance of the model to identify how well it can restore an image using multiple performance metrics. The process generates a series of successfully remastered images.
A Neural Network Pruning Method by the Sum of Matrix Similarity Measures

The development of today's intelligent communication 6G is closely related to deep learning. In semantic communication, images need to be transmitted selectively in combination with image semantic information, but the large-scale parameters of neural networks exacerbate the computational complexity. Parameter pruning is one of the predominant approaches for compressing deep models, which can reduce redundant parameters, reduce communication overhead, and improve the timeliness of information transmission while maintaining the performance of neural networks. Currently, most pruning methods focus on the importance of pruned objects in the overall task. Unlike previous methods, this paper ranks the similarity of the filters in the neural network using the sum of matrix similarity measures based on cosine similarity, removing the most similar and most replaceable filters. We validate the pruning method on CIFAR10 and ImageNet ILSVRC2012 using different network architectures. The results show that the sum of matrix similarity measures has state-of-the-art performance with less model performance loss and better compression.
Design and Implementation of Electronic Data Blockchain Judicial Deposit Platform for Power Grid Gusiness

In the process of power grid digital transformation, electronic data such as electronic contract, electronic signature and electronic bidding are widely used. The electronic data is easy to tamper and forge, which makes it difficult to form effective legal effect.To solve this problem, this paper proposes to use blockchain technology to build a judicial certificate storage platform for power grid business, and use blockchain identity certificate mode to realize business-based user authority management;The domestic password and smart contract technology are used to realize the encrypted transmission, complete preservation and trusted sharing of electronic data in power grid enterprises, judicial authentication centers and other institutions;Through the high-performance keyword matching algorithm, the rapid query and accurate evidence collection of electronic data are realized, which not only promotes the development of power grid digitalization, but also improves the legal quality and efficiency of electronic data depository.
An Effective Pruning Strategy for Performance Enhancement in Deep Neural Network (DNN)- with Long Short-Term Memory (DNN-LSTM)

Over-parameterization of neural networks aids in generalization and optimization, but it has a huge price in a real-world application. Pruning is a post-processing approach that tries to eliminate pointless parameters from neural networks with a minimal performance impact. It is widely accepted that the final deep neural network (DNN) cannot be similarly trained from scratch, and the problem is handled using a pruning strategy. Despite the positive developments, it has not yet been determined how to integrate the new pruning trends with conventional pruning. By offering a comprehensive pruning framework, this research aims to close the gap between existing and new pruning paradigms, allowing them to coexist. This work considers DNN with Long Short-Term Memory (DNN-LSTM), and the pruning strategy is incorporated. This approach uses a genetic algorithm (GA) to accomplish network channel pruning.
Transformed Deep Spatio Temporal-Features with Fused Distance for Efficient Video Retrieval

For the goal of video retrieval, this research proposes wavelet transformations on deep spatiotemporal characteristics. The component-wise similarities between the query video feature and prototype video feature are calculated because level 1 wavelets extract two components from any signal or feature vector. The ultimate dissimilarity for determining the top 1 and top 5 accuracy is created by fusing these differences. The outcomes demonstrate that the suggested technique performs better than a baseline strategy. The following strategy for improvement can be investigated further by employing fast learning networks that are trained on the training sets of both data sets to provide better classification of the query as well as the prototype feature vectors, which would enhance the retrieval accuracy.
Research on spam message recognition algorithm based on improved naive Bayes

Research shows that in 2018, there are about 8.4 billion spam messages in China (360 Internet Security Center), which focuses on bank fraud, false online shopping on the Internet, telecommunications fraud, etc. China's current status of spam messages, the black interest chain of spam messages, the lack of legal protection, the increasingly changeable types of SMS, the continuous improvement of delivery methods, the changeable contents of spam messages and the various types of spam messages. Some bad organizations even collect spam messages through various channels Collect personal mobile phone information and sell mobile phone information to businesses and business personnel to obtain illegal benefits. At the same time, businesses seek benefits by sending spam short interest such as fraud and advertising, these methods endangers the information security and broke the daily life of the users. Based on the text content of SMS, this paper establishes a recognition model to accurately identify spam SMS, so as to solve the problem of spam SMS filtering. Using python programming, Gaussian naive Bayes is used to realize text classification, and a high classification accuracy is achieved.
Equivalence of Coupled Parametric Oscillator Dynamics to Lagrange Multiplier Optimization

Physical machines can solve optimization problems, employing the maximization or minimization principles that are built into physics. We present a dynamical solver for the Ising problem that is comprised of a network of coupled bistable parametric oscillators and show that it implements Lagrange multiplier constrained optimization. We show that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\omega$</tex> pump depletion effect, which is intrinsic to parametric oscillators, automatically enforces the binary Ising constraints. This enables the system's continuous analog variables to converge to high-quality binary solutions to the optimization problem. Moreover, we establish an exact correspondence between the equations of motion for the coupled oscillators and the update rules in the primal-dual method of Lagrange multipliers. Though our analysis is performed using electrical LC oscillators, it can be generalized to any system of coupled parametric oscillators. We simulate the dynamics of the coupled oscillator system on benchmark problems and demonstrate that its performance is comparable to the best-known results.
Language Model Based Related Word Prediction from an Indian Epic-Mahabharata

The “Mahabharata” is the most well-known of several Indian works of literature that are mentioned in numerous contexts for quite varied reasons. This literature itself has a variety of dimensions and elements that benefit people in both their careers and personal lives. Sanskrit was the original language used to write this Indian epic. Thanks to advancements in areas such as Natural Language Processing, Intelligent Systems, Machine Learning, and Human-Computer Interaction, this content can be processed in accordance with the domain's requirements. The difficulty that people have when analysing the Mahabharata is that they cannot help but have an emotional reaction to the story being told. It is intriguing to analyze this book and get insightful knowledge from the Mahabharata. In addition, the human brain is incapable of memorizing statistical or computational data, such as how frequently two words occur together in a phrase. The average sentence length in all of literature? What is the problem with the terms utilized throughout phrases, and which word appears most frequently in the text? Therefore, in this research, provide an NLP pipeline to obtain some mathematical and statistical findings as well as the most pertinent word-searching technique from the epic “Mahabharata.” To identify the best outcomes that may be applied further in the numerous domains where Mahabharata has to be referred to, layered the textual algorithms.
Image Processing Based Security Management System

Many industries nowadays struggle with serious security issues, thus hiring security guards is necessary to accomplish security. People make many mistakes as human beings, such as missing targets, and they only have so much energy (they are not able to monitor your environment continuously). These factors may have an impact on the level of security. The methods used worldwide to acquire facial detection and facial recognition also fall under the category of image processing, which is a subset of machine learning and is classified as an aspect of artificial intelligence. In security-sensitive regions, the facial recognition technology may identify a person and lessen the need for men to patrol. A series of photos are taken by the camera sensor as soon as a person enters a highly protected location, and these snapshots are analysed and compared to any previous images in the data set of an authorised individual. The key reason for image processing is that, in the future, this system will handle identification and verification for improved security problem accuracy and transparency. This project is being developed to let authorised users access to restricted locations.
Blockchain Technology: Reinventing the Security and Efficiency posture of the Indian Banking System

Since its inception in 2008, Blockchain technology has been touted as a disruptive force that has the panache for changing business models in this ever-evolving technological landscape. Blockchain is an immutable and ever-growing digital ledger that can keep a permanent record of all the past transactions that have been performed on a network in chronological order. This research paper will primarily focus on the problem of financial fraud prevalent in the Indian Banking System and how we can use Blockchain to prevent it by driving the system towards a decentralized network. The paper will also briefly touch upon problem areas in the Indian Banking System like double taxation and poor status of documentation. Research Methodology: Qualitative Research will be used to conduct the research. The paper is a conceptual study of the capabilities of Blockchain in making the Indian Banking System more robust. Practical implications: This research paper is aimed at benefiting both the users and banking institutions. It is not only confined to the aforementioned parties, rather extends its impact on a wider range of stakeholders e.g. businesses, NBFCs, shareholders of businesses, etc. Financial institutions can further study their security posture, helping them to fine-tune their transaction mechanism. Originality: The Indian Banking System has long been driven by the centralized network approach which has certain problems associated with it like a single point of failure. The paper will outline the impact of Distributed Ledger Technology (DLT) on various financial institutions and applications.