Reviewerly/exHarmony huggingface dataset:
Classification of Various Factors That Have Caused Major Fluctuations in Cryptocurrency Markets

Cryptocurrency is a commonly used term in the current world, and the COVID-19 pandemic has indirectly increased the awareness and the investor base for cryptocurrencies. Various research has been conducted to understand the complex working structure of these investment options and to analyse the volatile nature of cryptocurrencies. There are multiple factors and triggers that impact the price movements in the crypto market. Classifying these factors would help streamline the process of analysing these factors for further studies. These factors cause both positive and negative impacts on the price fluctuations. Classifying the major factors under the period of impact will help understand each factor's role in the market. This classification would help in the diagnostic and prescriptive analysis of cryptocurrencies. In this research, well-cited and published research papers, journals, and articles have been studied to classify some of the major factors affecting cryptocurrencies carefully. A model has been created to easily comprehend the classification of factors based on time of impact. This model simplifies the understanding of the factors and would help conduct further analysis on these factors.
THE INFLUENCE OF PICTURES AS A MEDIA TO IMPROVE WRITING DESCRIPTION TEXT FOR STUDENTS

This study aims to determine the influence of picture as a medium to improve the skills of writing Descriptive at English Study Program, Faculty of Law and Business Duta Bangsa University Surakarta academic year 2021/2022. Research design is action research. Data collection in this study was carried out by means of conducting observations, individual written tests at the end of learning,. Analytical techniques data using descriptive analysis quantitative. Based on the analysis the results evaluation before and after using picture from cycle I to cycle II can be concluded that the learning of Descriptive Writing by using picture can improve student learning outcomes
TEACHERS’ PERSPECTIVES ON THE IMPLEMENTATION OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN ENGLISH LANGUAGE TEACHING

This research aimed to find out the kinds of information and communication technology (ICT) used by the teachers to teach English at SMPN 3 Sungguminasa and the teachers’ perspectives on the implementation of information and communication technology (ICT) in English language teaching. The data were collected in February 2022 from 3 teachers of the seven and eight at SMPN 3 Sungguminasa. This research used a descriptive qualitative method, and the instruments used were observation and interview. The observation was used to observe and interview the English teachers of seventh and eighth grades at SMPN 3 Sungguminasa. The findings of this research show that the teachers used the kinds of ICT in English language teaching likewise, LCD, laptops, Mobile Phones, Microsoft Word, PowerPoint, E-Book, and YouTube. The result of the interview of this research showed that mostly the teachers gave positive perspectives and agreed that teachers use Information and Communication Technology (ICT) in English language teaching, they are happy, they are easy to understand the lesson, enthusiastic about ICT, motivated to learn, and diligent doing assignment
Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos

This paper proposes a multi-modal reference-free objective quality assessment method for point cloud videos. The method extracts spatial and temporal features of distorted point cloud videos through three network branches, namely point cloud, projection, and video, respectively, and then fuses the spatio-temporal features using a multi-modal attention mechanism to improve the accuracy and generalization ability of point cloud video quality prediction. In this paper, sufficient experiments are conducted on a self-constructed database consisting of 36 distorted point cloud video sequences, and the experimental results show that the multi-modal quality assessment method proposed in this paper outperforms the current state-of-the-art 11 unimodal and bimodal methods, and can further improve the quality assessment performance.
Multimodal Semantic Segmentation Based On Improved Vision Transformers

Although semantic segmentation networks based on CNN or RNN can already perform the semantic segmentation task better, the introduction of multimodal input and Transformer can make the performance of semantic segmentation networks have further room for improvement. In this paper, we try to apply Transformer to the multimodal input scenario, but the ability of Transformer to handle multimodal inputs is not ideal, and how and where features from different modalities should interact with each other poses a great challenge to the design of the fusion scheme of the model architecture. In this regard, this paper improves Vision Transformer by using Token Fusion's model, and finally completes the image semantic segmentation task for RGB-Depth multimodal input efficiently.
Crowd counting in congested scene by CNN and Transformer: Crowd counting for converged networks

Crowd counting apply in many areas. In order to prevent stampede accidents, it is necessary to accurately forecast the number of individuals in some scenarios with large numbers. Among them, CNN and Transformer are the mainstream models to solve crowd counting in dense scenes. CNNs are good at capturing local features, but they are weak at capturing global features. Transformer uses self-attention module to extract global features, but the model will lead to the loss of local details. Therefore, both CNN models and transformer models have certain limitations in the task of estimating people in dense scenes. hence this text presents a method combining CNN and Transformer to forecast the number of individuals in dense scenarios. First, the density plot is regressed at the CNN layer, to determine the number of individuals, you can sum the values from the density plot. The headcount is directly generated at the Transformer layer. Secondly, a weight term is proposed to fuse the number of people generated by the CNN layer and the Transformer layer. In the departed methods of predicting the headcount, most of them were summed into density plots to get the headcount, but we have made an innovation in the Transformer counting branch to directly generate the headcount at the Transformer layer, reducing the number of parameters and speeding up training. Experiments show that the proposed method has been found to achieve satisfactory performance on the dataset.
Enhancing Graph Contrastive Learning via Correlation Reduction

Graph contrastive learning (GCL) has recently attracted great attention as a popular learning approach to reduce the label reliance in graph representation learning. However, current GCL models still encounter some issues, such as the representation collapse. In order to tackle this issue, we propose an innovative graph self-supervised learning method, named CRGCL. Specifically, we first employ random augmentation to create two views of the input graph, then we adopt the redundancy reduction principle to enhance the consistency between the two views. Besides, we add a regularization term to alleviate the over-smoothing phenomenon in graph convolutional network. As a result, our model can effectively maximize the invariant information between two views and reduce the redundancy between different dimensions of embeddings. We conduct extensive experiments and the results indicate that our model has quite competitiveness on the seven node classification benchmark datasets compared to other GCL baselines, particularly, we achieve a 0.5% performance improvement on the Cora dataset and a 0.24% performance enhancement on the Coauthor-CS dataset.
Research on traffic light target detection method based on improved YOLOv5 algorithm

Aiming at the real-time requirement of traffic light target detection in assisted driving, a traffic light target detection model based on improved YOLOv5 algorithm is established. The improved model is improved from two aspects of the original YOLOv5 model: C3 module and feature extraction network. The feature fusion mode of C3 module is changed from concat operation to add operation, and the Focus module in the feature extraction network is eliminated. The parameter configuration is optimized to avoid frequent slicing operations, and the parameters of the model are greatly reduced. The improved YOLOv5-lite model improves the processing speed while maintaining the detection accuracy, and is easier to deploy on the vehicle platform.
Knowledge Graphs Enhanced Large Language Model Prompt for Electric Power Question Answering

With the continuous development and digital transformation in the field of electric power, the application of large language models in the electric power industry has become a remarkable trend. The electric power industry is an information-intensive domain involving extensive data processing, predictive analysis, and decision-making. Therefore, the application of large language models in the electric power sector is of great significance. Current large language models such as GPT3.5 and GLM can perform well in tasks such as question answering dialogues. However, these models still face challenges such as answer hallucination and inaccurate responses. This paper proposes a method to enhance question answering in large language models using knowledge graphs, aiming to improve the accuracy and reliability of these models in question answering tasks in the electric power domain.The proposed method first utilizes local electric power data to extract triplets and generate a question answering dataset specific to the electric power domain using a large language model. Then, the relationships of the knowledge graph triplets are incorporated into the question prompt to enhance the quality of the model's answers. Furthermore, we fine-tune the large language model using the expanded question set derived from the triplets as knowledge enhanced data. Subsequently, we conduct experiments on both an electric power question answering dataset and a knowledge graph question answering dataset. The experimental results demonstrate that our method significantly improves various metrics of the large language model in the electric power question answering task. This research provides new insights and approaches to enhance the effectiveness of question answering systems in the electric power domain. Future studies can further explore and optimize this prompt expansion method for application in broader domains and tasks.
Knowledge Graph-Based Credibility Evaluation Method for Electric Grid Large Language Model Knowledge Question-Answering

In the field of electricity, specialized terminology is often intricate and complex, making it challenging for non-experts to comprehend. However, with the advancement of artificial intelligence technology, the emergence of large language models provides a new technological solution to address this issue. Large language models, based on deep learning techniques, have the capability to quickly understand and interpret specialized terminology in the electricity domain through learning from a vast corpus of professional literature and data. They can then be applied to various domains, including question-answering systems. However, existing large language models still face issues of unreliable outputs, necessitating a method to evaluate their results and improve the quality of their applications. We propose a knowledge graph-based credibility evaluation method for electric grid large language model knowledge question-answering. This method aligns the answers generated by large language models with the knowledge graph of a local knowledge base and calculates their cosine similarity and Pearson correlation coefficient. We batch-process the answers from the large language model into an electricity dataset and validate them using this method. Experimental results demonstrate that this method can accurately and efficiently reflect the relevance between texts, providing a reliable scoring basis for question-answering by large models in vertical domains. Future research can focus on exploring other embedding methods that can better extract semantic relationships between texts and validating the feasibility of this method in vertical domains other than electricity.
Design of secure AES encrypted IP cores for IoT environments

In this paper, a secure AES encryption IP core based on AHB-LITE (Advanced High Performance Bus) bus is designed to improve the ability of AES (Advanced Encryption Standard) to resist differential power attacks in resource-constrained environments such as the IoT environments. In terms of IP protection, a new dummy round logic protection method is proposed, and combined with full-mask protection, it increases the attack resistance of IP. According to the requirements and test methods of the cryptographic modules used in security systems according to the ISO19790 standards, one million sample information was collected for DPA (differential power analysis), and the t-value in Welch's test was a maximum of 3.24, and the results showed that the proposed secure AES encrypted IP core design passed the ISO17825 level 4 security test.
Research on Video Retrieval Technology based on Multimodal Fusion and Attention Mechanism

Feature extraction and matching are crucial in video retrieval tasks. However, existing algorithms often overlook motion features in action-related videos and focus only on global static features. Distinguishing between key action features and background features is challenging, which hinders capturing global dependency relationships during the convolutional process. This results in less expressive features and reduced accuracy in video retrieval.In this paper, we propose a video retrieval model that combines multimodal fusion and attention mechanism. Our model employs the SlowFast backbone network, extracting skeleton motion features and static image features from video sequences using the Slow and Fast networks respectively. To address feature fusion, we introduce a 3D residual attention structure between the two branches. By incorporating bilateral connections and hash encoding, we construct a hash layer to map features into binary codes, improving retrieval efficiency.Experimental results on UCF101 and HMDB51 datasets validate the effectiveness of our approach, demonstrating its advantages over state-of-the-art video retrieval methods.
Construction of Enterprise Talent Training System Based on Improved Particle Swarm Optimization Algorithm for Supply Chain Intelligent Platform

There are many problems and challenges in the cultivation of supply chain management talents currently, such as a lack of personalized training and practical operations. Based on these issues, this article aims to improve the efficiency and quality of supply chain management talent cultivation through the supply chain intelligent platform, and use the improved particle swarm optimization (PSO) algorithm to cultivate talents with supply chain management capabilities for enterprises. The main theoretical contribution of this article is to improve the particle swarm algorithm by improving parameter settings. By using inertia weights, particle swarm optimization algorithm can find new target regions. By introducing a contraction factor, the motion speed of particles tends to be stable and improve local optimization ability. By learning factors, the differences in search angles of particle swarm optimization algorithms were considered. The improved particle swarm optimization algorithm is used to solve optimization problems in talent training systems. This article explores the supply chain intelligent platform, including simulation roles, data collection and processing, intelligent decision support, and providing support and feedback through the intelligent supply chain platform. The innovation of this article is that in order to demonstrate the effectiveness of the talent training system analyzed in this article, a traditional training mode was adopted for 200 students, and a training mode based on an improved PSO algorithm supply chain intelligent platform was adopted for 200 students. The training effects of two groups of students were compared. The experimental results of this article show that the average scores of Course 1 in Group A and Group B before training are 72.04 and 71.89, respectively. After training, the average scores of Course 1 in Group A and Group B were 74.38 and 76.83, respectively; The average scores of personalized abilities in Group A and Group B in the first week before training were 64.62 and 64.67, respectively. After one week of training, the average personalized ability scores of Group A and Group B were 65.13 and 72.27, respectively. It can be seen that the improved PSO algorithm based supply chain intelligent platform can not only improve students' average grades, but also enhance their personalized abilities. This method has high effectiveness and feasibility in optimizing the talent cultivation system.
Aspect-based Sentiment Analysis with External Knowledge Embedding and Syntactic Information

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis. The current research mainly focuses on context-based and syntactic dependency-based approaches. However, they only consider the inherent syntactic connections and linguistic properties within the sentence, and much additional knowledge is not considered or utilized. To handle this concern, we propose a knowledge embedding and syntactic information enhancement network (KESE). KESE aims to fully utilize syntactic dependency and introduce external knowledge for assisting in mining the connection between aspect and sentiment words. We utilize an aspect-oriented attention mechanism to capture aspect features and enhance them with syntactic masking networks and graph convolutional networks. Furthermore, we embed knowledge graphs to improve the recognition of aspects and sentiments. We conduct experiments on several publicly accessible ABSA datasets, and the results demonstrate the effectiveness of our proposed KESE.
Intelligent medical consultation system based on the GPT model

To address the current problem of insufficient medical resources and the resulting difficulties in receiving timely medical treatment, this paper proposes the development of an intelligent medical consultation system using artificial intelligence technology. The system provides online medical advice and diagnostic suggestions to users. The system simulates the process of a doctor's consultation, interacts with users, and gathers information about their symptoms, medical history, and other relevant information. This paper utilizes the GPT model, and trained it with over 800,000 real medical dialogue data to achieve reliable symptom analysis and diagnostic suggestions. The intelligent medical consultation system can provide users with convenient, fast, and accurate medical advice and suggestions, helping to solve the current problems of limited medical resources and difficulties in seeking medical treatment. Additionally, it can serve as an auxiliary diagnostic tool to help users stay aware of their own physical condition in a timely manner.
Research on Deep Learning-based Semantic Segmentation Algorithm for UAV Images

Most of the current image semantic segmentation algorithms on UAV vision segment remote sensing images, which cannot represent ground detail information, resulting in obstacles to real-time autonomous environment perception of UAVs in low altitude flight missions. To address this problem, this paper proposes a real-time image semantic segmentation method for low altitude UAVs. The network designs a novel hypernetwork structure that incorporates a context header weight generation module at the last layer of the encoder, the weights of each block in the decoder are generated before the end of encoding in the encoder to reduce the number of parameters and computation of the model to achieve real-time segmentation. In the decoder, a dynamic patch-wise convolutional algorithm is designed using the locally connection layer mechanism to take full account of the contextual semantic information when targeting large segmented objects that are in more than one piece, so that the decoder's weights change with the spatial location of the input feature map, and at the same time, the dynamic weights are used to target the segmentation of different objects, to maximise the network's adaptive nature. In order to verify the effectiveness of the method, this experiment uses the transfer learning technique to carry out pre-training on Cityscape data set, and uses the UAVid data set to validate the method of this paper, and the experimental results show that the mean intersection over union of this method is 66.3% for the images of the categories of buildings, roads, and static cars, and the prediction speed reaches 37.9 FPS, which significantly improves segmentation accuracy under the condition of guaranteeing the real-time performance.
Design and implementation of football training system based on computer vision technology

In order to improve the science and quality of football training, a football training system based on computer vision technology is studied. Collect initial motion data through motion capture to form an action database, modify and design the actions in the action database, obtain standard technical actions, and arrange and simulate each standard technical action through motion splicing; By using video shooting technology to obtain the image data of athletes during physical exercise, the measurement deviation in sports training is discussed through the trajectory tracking and error judgment of the motion nodes, so as to provide more efficient sports guidance and improve the movement and training level. The simulation test results show that compared with other corresponding systems, the system is outstanding in the aspects of fast response and improved control accuracy, so as to realize the optimal monitoring effect of sports training errors.
Research on an improved yolov5s algorithm for detecting helmets on construction sites

Aiming at the situation of irregular wearing of safety helmets on construction sites, a work on the detection of the wearing of safety helmets on construction sites is proposed to improve the yolov5s (You only look once) algorithm. To improve the problem that a large number of small targets are difficult to accurately identify due to dense construction site targets, combined with the structured weighted bidirectional pyramidal feature network (BiFPN) to improve the process of feature merging; in the Yolov5s feature extraction network in the introduction of the CBAM attention mechanism of the convolution module to obtain more feature details; using SIoU instead of CIoU as prediction box regression loss makes the network training and inference process faster and more accurate. The experimental findings show that the model with improved YOLOV5S helmet detection has higher detection precision compared to the YOLOV5S model, with MAP increased by 6.6 percentage points, recall increased by 8 percentage points and accuracy by 0.2 percentage points, making the improved algorithm more satisfactory for the helmet-wearing detection challenge.