Reviewerly/exHarmony huggingface dataset:
Research and application of path planning algorithm based on deep learning

This paper mainly introduces the research and application of path-planning algorithms based on deep learning. Firstly, it introduces the background significance of path planning, the current status of domestic and international research and the content and methods, and then analyzes the application methods of deep learning in path planning, including deep reinforcement learning, convolutional neural network and recurrent neural network, and the comparison between different algorithms. Then, the implementation of a deep learning-based path-planning algorithm is introduced in detail, including system architecture and design, data preprocessing and model training, path-planning implementation and optimization, etc. In the Experiments and Results Analysis section, the analysis and comparison of experimental results, experimental conclusions and expansion directions are summarized. Finally, in the conclusion and outlook section, the research contributions and application values of this paper are described, and the research deficiencies and future expansion directions of deep learning in path planning are proposed. In addition, because of the continuous development of deep learning technology, there are many other possibilities for applying deep learning methods to build more optimal path-planning algorithms in the future.
Research analysis of blockchain IOT domain according to knowledge graph

The purpose of this paper is to analyze and organize the research in the field of Blockchain Internet of Things (BIoT) using knowledge mapping technology, in order to get a more comprehensive understanding of the research status and development trend in this field. By organizing and statistically analyzing the relevant literature in the field of blockchain IoT, information on keywords, topics, and hot issues of research in this field is obtained and presented in a visual way. At the same time, this paper also provides an in-depth analysis of the research in the field of blockchain IoT, including technical applications, development trends and other aspects. The research results show that the research in the field of blockchain IoT shows a rapid development trend and has received wide attention from academia and industry sectors. In terms of technical applications, the combination of blockchain technology and IoT technology has brought new ideas and technical means for the development of BIoT field. Meanwhile, with the application of knowledge graph technology, the research in the BIoT field has gradually developed in a more refined and in-depth direction. This study has certain reference value and practical significance for the research in the field of blockchain IoT, and can provide more scientific and accurate guidance for the development and application of this field.
Research on identification of floating garbage using improved YOLO v7 algorithm

The floating garbage is becoming more and more serious, but little research has addressed recognition of these floating garbage. Intelligent target recognition of floating garbage using deep learning techniques is therefore essential. The YOLOv7 algorithm has strong ability of extracting target features and is significantly faster than its previous version at the same accuracy. The technique is provide based on the YOLOv7 algorithm for identifying floating garbage in this paper, as a result, develop and implement a target monitoring function for floating garbage identification. Specifically, the combination of YOLO v7 and SE attention mechanism was used to improve ability of target sensing. The training process was optimized by using EIOU loss, resulting in a significant improvement in the efficiency of the final model compared to the normal YOLOV7 algorithm model, significant improvements of 20% and 25% in the model metrics mAP_0.5:0.95 and mAP_0.5, respectively.
Application of neural network in object motion tracking in the context of big data

In the context of big data, the advancements in computer analytics and computational capability have promoted transformative opportunities. At the forefront of this progress, neural networks have emerged as an essential instrument across diverse domains, catalyzing enhancements in operational efficiency. This paper embarks on an exploration of the applications of neural networks within the realm of object motion prediction and tracking, with the primary objective of improving the efficacy and precision of tracking mechanisms. Amidst the escalating complexities of data proliferation, the imperative for streamlined tracking methodologies has intensified. In response, this study investigates the amalgamation of neural networks with object motion prediction and tracking processes. By using the powerful abilities of neural networks, the research aims to bring a significant change to the field. This change will be seen in improved accuracy and faster tracking efficiency. Combining neural networks with motion prediction and tracking creates a cooperative partnership that develops a new system based on data-driven insights. By merging computational power and advanced algorithms, the paper anticipates a fresh direction for tracking technology.
Effectiveness of masked autoencoder in vision transformer models for image classification

This paper uses the literature reading method to systematically sort out and introduce the basic principles of the three algorithms of the transformer model and their application in the field of image classification, which has high theoretical value and social value, and has strong reference for the development of the transformer model in the future. ViT is simply an innovation in computer vision based on the transformer model. It first separates an image into several local patches (16x16), and then maps each one to a feature vector. These vectors will be delivered to an encoder for polishing. Finally, a special token is appended to these vectors for integrating location information. The final prediction is based on these tokesn. Swin-T is a new Transformer architecture, which is proposed by Microsoft Research to improve the performance of computer vision tasks. It adopts a new windowed feature extraction strategy, which can maintain high accuracy while significantly reducing the amount of computation and memory consumption. It has achieved leading performance in multiple computer vision tasks, becoming one of the most advanced visual Transformer models. In computer vision image classification, the information is highly redundant, the lack of an image piece, may not make the model produce much confusion, the model can be inferred from the surrounding pixel information, masked autoencoder (MAE) is to mask a high proportion of image pieces, create a difficult learning task, the method is simple but extremely effective.
CHALLENGES, STRATEGIES AND QUALIFICATIONS OF AUDITORS IN THE SOCIETY 5.0 ERA

We will soon live the Era of Society 5.0. An era that is integrated with AI in all professions, including auditors. The research aims to obtain an overview of the auditor's profession in the future. First, the challenges that auditors will face and second, the strategies that auditors need in the Society 5.0 Era. This research uses a literature study approach with content analysis. Data sources from journal articles, proceedings, online news, e-books and YouTube. Data processing consists of four stages, namely data input, coding, visualization, and determining conclusions. Data processing uses the NVivo 12 Plus software. The results of the research succeeded in providing an overview of the six challenges and seven strategies needed to compete in the era of society 5.0. The implication is that the research results can be used as a reference for auditors to prepare themselves for the Era of Society 5.0.
Determination of basketball interference ball violation based on visual recognition technology electronic referee artificial intelligence algorithm

This article set up an electronic referee system based on monocular vision recognition. The system requires only two cameras to operate. By establishing a multi-camera real-time video monitoring system, the system cooperates with artificial intelligence automatic recognition judgment, overcomes many blind spots of human eye judgment, completes the judgment efficiently and accurately, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game. This project overcomes many blind spots of human eye judgement, efficiently and accurately completes the penalty, improves the accuracy of the referee's ruling on basketball interference ball violation, and enhances the fairness and spectacle of the game by establishing a multi-camera real-time video monitoring system with artificial intelligence automatic identification judgement.
Personal health record storage and sharing using searchable encryption and blockchain: A comprehensive survey

Abstract Personal Health Records (PHRs) allow patients to have full control over their health data. However, storage and sharing of PHRs still remains a difficult but necessary task, especially when health data is one of the major targets of cyber attacks worldwide. Searchable Encryption (SE) is a feasible solution for this problem and can be augmented by Blockchain to address some of its issues, such as verifiability. Therefore, SE using blockchain is a promising technologies to tackle the challenge of PHR storage and sharing. In this survey, we have explored the research works that use SE and blockchain technology for the same. The work starts with an introduction of cloud, searchable encryption and blockchain. Subsequently, we present a literature survey of the corresponding technologies. We then describe SE in detail and how it fits with blockchain. This is followed by description of noteworthy existing solutions for secure storage and sharing of PHRs. Even though there have been a number of surveys related to SE, none of them have surveyed the use of blockchain with SE or use of SE and blockchain in PHR sharing. The work concludes with a comparative study of these existing solutions and future scope in this direction.
Adaptive block level bilateral filtering algorithm

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.
Energy optimization using virtual machine migration for power aware

Using cloud computing, the cloud service can be delivered to the user via an internet connection. A major concept in cloud computing is virtualization, which allows for the dynamic sharing of physical resources. The deployment of virtual machines (VMs) in the cloud is a difficult problem since they are placed on top of real machines in the data center. A good VM placement policy should increase resource utilization and also provide energy optimization, as saving energy has become crucial due to the high demand for the cloud and its data centers consuming high power. In this work, an approach is made to optimize the energy consumption during VM migration called, Power-Aware Energy Optimized VM Migration (PAEOVMM). The approach uses the maxPower of the host to allocate VM to the host. Our proposed approach is analyzed using CloudSim. As per the simulation results, PAEOVVM performs better in energy consumption than existing baseline CloudSim algorithm. PAEOVVM performs improvement in energy consumption on an average of 27-40%.
Transformer model: Explainability and prospectiveness

The purpose of Artificial Intelligence(AI) is to simulate learning process of human brain by strong computing power and appropriate algorithm, so that the machine can develop judging ability at work as human. Current AI mainly relies on Deep Learning model which is based on artificial neural network, like Convolutional Neural Network(CNN) in computer visualization, but that also takes with some defects. This paper introduces defects of CNN and discusses Transformer model in solving unexplainability of traditional CNN algorithm. To discuss why the Transformer model and attention mechanism are considered as the way to AI intelligibility.
Challenges and strategies for sentiment analysis of irony and humor in social media based on machine learning

Sentiment analysis of irony and humor in social media poses a formidable challenge owing to the complexity and context-dependency of such expressions. This report offers a comprehensive overview of diverse machine learning techniques utilized in the analysis of irony and humor, encompassing traditional machine learning algorithms and deep learning approaches. Moreover, this review scrutinizes the challenges and future prospects for the sentiment analysis of irony and humor in social media. Future research pathways involve cross-lingual and cross-cultural analysis, multimodal information integration, autonomous identification of novel patterns, adversarial training, and augmenting the explainability and interpretability of sentiment analysis models. The report highlights the importance of these challenges and potential directions, unveiling their impact on this advancing field of research.
Applying machine learning models to breast cancer prediction problem

Cancer has become the number one killer of human life and health. Therefore, a model that can predict cancer is able to help doctors to diagnose whether a patient has cancer or not, which can boost the accuracy of the diagnosis and enhance diagnostic efficiency, thus reducing the chance of misdiagnosis and other situations. This paper focuses on breast cancer prediction and adopted three machine learning based methods, including logistic regression, K-Nearest Neighbor, and decision tree models to build automatic solutions and investigate which model is more suitable for such a simple prediction problem. In this study, the detailed features, data collection and pre-processing approaches are presented to better understand such medical data. Then extensive experiments show that the accuracy scores of the three models are 97.08%, 94.89%, and 93.43%, respectively. Through comparison, it is concluded that the logistic regression model achieves the best performance for the breast cancer prediction task.
Linguistic mastery: Advances in natural language processing

NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as computational linguistics, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.
The application and challenges of artificial intelligence in speech recognition

This paper provides an overview of artificial intelligence (AI) and speech recognition technology, including its history, applications, challenges, and future prospects. AI-powered speech recognition technology has significantly improved over the years, and it is used in various applications, such as virtual assistants, voice-activated devices, and dictation software. The technology leverages machine learning algorithms that are trained on vast amounts of speech data to recognize and interpret human speech with accuracy levels that are comparable to those of humans. However, the technology still faces many challenges, such as speech variability and background noise, which make it challenging to develop speech recognition algorithms that can accurately recognize all types of speech. The article provides a comprehensive review of the technical aspects of automatic speech recognition, including the process involved, the algorithms used, and the challenges and opportunities for future research in this area. The paper also discusses the architecture of automatic speech recognition (ASR) systems and the main components that make up the system. The authors explain that ASR systems consist of three main components: the acoustic model, the language model, and the decoder. They also discuss the challenges that ASR systems face, such as speaker variability, noise, and limited vocabulary. Overall, this paper provides a detailed introduction to AI and speech recognition technology and its potential for various industries.
Research on improving the generalization ability of NLP categorization models: A comparison of Mixup and generative adversarial networks

The application of deep neural networks in tasks related to natural language processing has grown in recent years. However, training deep learning models often requires a large amount of data to produce accurate and generalizable results. When the data are insufficient, categorization models may suffer from overfitting or poor performance. This paper compares two methods for enhancing the generalization ability of natural language processing categorization models when data are insufficient: Mixup and Generative Adversarial Networks (GANs). This paper uses the Internet Movie Database (IMDB) dataset for a binary classification task, Mixup method, and Relational Generative Adversarial Networks (RelGAN) model to generate new data, respectively, and compares them with the original dataset. Experimental results indicate that Mixup method can reduce overfitting risk effectively and enhance the models robustness, while the GAN model does not show obvious advantages. This paper supports the idea that using noise-adding operations to enhance the dataset is feasible in the case of small samples rather than using generative models.
Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches

In today's information age, from natural language processing to audio signal processing, from time series analysis to machine translation, the application of sequence data involves various fields. Encoder-decoder models, as a powerful approach to sequence modeling, have attracted extensive attention and research. This review paper aims to explore encoder-decoder models, focusing on the principles and operational steps of recurrent neural networks (RNN) and long short-term memory (LSTM), aiming to provide researchers and practitioners with a deep understanding of the fundamentals and applications of these models Condition. Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data. But there is a problem of gradient disappearance, which makes it difficult to deal with long-term dependencies. To solve this problem, the LSTM model introduces different gating mechanisms, which effectively solve the gradient problem while better capturing long-term dependencies. Through the review of the principles and applications of the two, it provides a useful reference for further research and application.
Push mechanism and its applications in learning and entertainment

Nowadays, people often use the push mechanism in daily life, especially on websites and the applications such as video applications. People often spend a lot of time using it because of the push mechanism. The appearance of the push mechanism deeply affects peoples daily life whether in socializing or entertainment, but most people dont know how the push mechanism works. This paper uses the literature analysis method, case study and review to focus on the development of the push mechanism, principle and its applications. The purpose of this paper is to increase public awareness of the application of the push mechanism and its principle. It can be found that the push mechanism is effective and helpful for all people whether what the identity of the people is.
Optimizing application and algorithm complexity of machine learning methods in traffic classification

As the Internet continues to evolve, it has become crucial for Internet Service Providers to analyze and classify their network flows. This enables them to identify suspicious activities and offer personalized services. Machine learning has been extensively deployed in network traffic classification, presenting a promising but challenging avenue. One of the primary challenges in applying machine learning to network traffic classification is reducing the computational resources used in training and implementing the model. By devising lightweight algorithms, traffic flow can be classified using fewer computational resources, effectively curtailing the escalating costs associated with the growing volume and transmission rate of traffic. In this study, we compare the performance of three classic machine learning algorithms - logistic regression, support vector machine, and shallow feedforward neural network - by employing them to classify mobile countries of origin, aiming to use as few features as possible. Remarkably, by utilizing only four features from the dataset, these three algorithms achieved an accuracy rate of 89%. This underscores the potential for computational and cost efficiency in network traffic classification with optimized machine learning methods.
Problems, solutions and improvements on federated learning model

The field of machine learning has been stepping forward at a significant pace since the 21century due to the continuous modifications and improvements on the major underlying algorithms, particularly the model named federated learning (FL). This paper will specifically focus on the Partially Distributed and Coordinated Model, one of the major models subject to federated learning, to provide an analysis of the models working algorithms, existing problems and solutions, and improvements on the original model. The identification of the merits and drawbacks of each solution will be founded on document analysis, data analysis and contrastive analysis. The research concluded that both alternative solutions and improvements to the original model can possess their unique advantage as well as newly-emerged concerns or challenges.