Reviewerly/exHarmony huggingface dataset:
Comparison between GRU and BP Neural Networks for Short-Term Prediction of Solar Irradiance

In this study, we present a data-driven approach for predicting solar irradiance by applying dimensionality reduction techniques to a dataset comprising of ground meteorological station data and FY-4A remote sensing data collected from January 1st to December 31st, 2018. Specifically, we use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset. Next, we employ a Gated Recurrent Unit (GRU) neural network-based short-term irradiance prediction model to realize the short-term prediction of solar irradiance. We then evaluate the performance of the GRU model by comparing its prediction results with those obtained using a traditional Backpropagation (BP) neural network model with measured solar irradiance. The results indicate that the root mean square error (RMSE) of the GRU neural network model is 41% lower than that of the BP neural network model, indicating the improved performance of the proposed model.
Vehicle Automation Using YOLOv7 and Computer Vision

In this paper we strive to achieve real-time vehicle and lane detection on low powered consumer hardware. The main goal of this work is to reduce the response time without sacrificing accuracy. For a system to be considered realtime the FPS should be above 25. This work addresses the low performance of previous systems by designing a pipeline and compiling libraries for it specific to the target machine. Previous works on this like Hybridnets produce good results but take up too much computing resources. For this paper we have used YOLOv7 as the core for vehicle detection. YOLOv7 is a state-of-the-art object detection framework released in July 2022. It will be custom trained on a traffic specific dataset for maximum accuracy. In addition to that a computer vision based lane intelligence system would be there to support it. The lane system will complement Yolo by predicting the lanes and calculating the threat of a collision from nearby vehicles. The pipeline serially runs the model and then the lane detection system. For the lane detection system OpenCV library was compiled on the target machine to maximize the efficiency.
Breast Cancer Identification Study Using Improved VGG

A classification recognition algorithm based on improved VGG16 for breast cancer histopathology images is proposed to deal with the problem of binary recognition of breast cancer pathology images. Because some pathological images are very similar to each other, which leads to the problem of false detection. Therefore, attention mechanism is applied to increase the weight of effective feature map to make the training model get better effect and improve the accuracy of the algorithm. Verified by comparative experiment, the convergence speed and accuracy of the improved VGG16 model are higher than those of the original DenseNet and VGG16 models, reaching 98.41% of the recognition accuracy.
A Novel Silhouettes Cluster Internal Evaluation Index Based on Granular-Ball

Cluster internal evaluation index is used to evaluate and guide the results of clustering, which has been considered as one of the vital issues in the application of clustering. Granular-ball is the multi-granularity characterization of the data set, In this paper, the classic Silhouettes indexes were improved by using granular-ball to represent the grain, we proposed a Silhouettes cluster internal evaluation index based on granular-ball(GSCVI), GSCVI can effectively obtain the optimal number of clusters for arbitrary-shaped and noisy data sets, and it is superior to most of the existing indexes for both artificial and real data sets.
VQA-Classification-Algorithm-Oriented Performance Benchmarks for Quantum Computing

Quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. This benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. The benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.
Impact of Social Sentiment Changes on People’s Consumption Behavior during COVID-19 Outbreak

A better understanding of people’s consumption behavior during the COVID-19 pandemic plays a significant role in providing informative decision support for the consumption supply of the overall society and government planning for different resources. This paper proposes to link the social sentiment and people’s consumption behaviour, and to understand how the social sentiment changes affect people’s consumption behaviour. In this study, Sina Weibo (a Chinese social networking platform where people communicate in short messages) text data is collected to extract the social sentiment situations with machine learning techniques, while Dianping (a Chinese social networking platform where people share consumption experiences) text data is collected to extract people’s consumption behaviour with machine learning approaches. The statistical analysis found that there was a significant correlation between social sentiment changes and consumption behavior during the COVID-19 epidemic in the studied city. The research results can be used to provide reasonable suggestions for government departments to make informative decisions and guide the supplies for people’s consumption.
Hand Gesture Recognition based Application using 60 GHz FMCW MIMO Radar

Millimeter wave radar (mm-Wave radar) technology has paved the way for non-contact human-machine interaction using gestures. The proposed work uses a 60 Giga Hertz Frequency Modulated Continuous Wave Multiple Input Multiple Output Radar (FMCW MIMO radar) to perform hand gesture-based applications. In the paper, 2 different gestures namely, forward and backward are classified and used in the application. Data for the aforementioned gestures was extracted using Texas Instrument's 60 GHz FMCW MIMO radar from a group of 10 people. For data classification, the Micro-Doppler signatures of the gesture data are extracted. The classification of these micro-Doppler features is done by using a vanilla Convolutional Neural Network (CNN) algorithm.
UT-MT: A Semi-Supervised Model of Fusion Transformer for 3D Medical Image Segmentation

The training of 3D medical image segmentation model requires a large amount of labeled data, but the availability of labeled data is difficult, and the scarcity of labeled data makes the prediction quality of unlabeled data cannot be effectively guaranteed. To solve the above problems, the 3D medical image segmentation model UT-MT proposed in this paper combines ViT and CNN, and consists of a student model and a teacher model, and the student model learns the teacher model by minimizing the segmentation loss and consistency loss. By combining the feature learning advantages of CNN and ViT, this method enables the model to enhance the learning ability of taking into account both local and global aspects in feature extraction, and further improves the model accuracy and performance. The evaluation was performed on a public left atrial benchmark dataset, and the results at 10% of the labeled images show that the proposed method improves the Dice coefficient by 6.69% over the fully supervised method and has better segmentation of the boundaries, while our method outperforms five advanced semi-supervised segmentation methods.
Smart Contract-Based Multi-Stage Service Auction for Refraction Networks

As a promising Internet censorship circumvention technique, refraction networks have attracted the wide attention of researchers in recent years. However, it remains a crucial issue to be tackled, i.e., how to motivate more service providers, known as decoy routers, and clients to join refraction networks, especially with guaranteed security and privacy. To address the issue, we develop a decentralized service auction framework based on the blockchain constructed through clients and decoy routers. Further, we design a multi-stage service auction (MSSA) smart contract composed of multiple functions and events, which are triggered and executed autonomously to complete the tasks, including registration, double auction, first-price sealed auction, and payment clearing. Moreover, we implement the designed MSSA smart contract in the Remix integrated development environment and run a test case on Ethereum virtual machines. The experiment results verify the modest gas and ether cost of the smart contract-based solution and its effectiveness.
Energy Modeling Method for Containerized Applications on Physical Hosts

The energy consumption problem in data centers is becoming increasingly severe, and energy consumption models can reveal the relationship between data center energy consumption and its corresponding factors, which is crucial for reducing energy consumption. Containerization is the current mainstream trend in data centers. Therefore, this paper proposes energy consumption models for physical hosts, containers, and containerized applications. First, we constructe a physical host energy consumption model based on polynomial regression with lasso. Then, by analyzing the resource utilization, we propose a simple and accurate method for modeling the container energy consumption. Finally, based on the container energy consumption model, we propose a method for constructing a parameter-based energy consumption models for containerized applications. The experimental results show that our modeling methods are valid. The advantages of our modeling method are that it can analyze data center energy consumption from different levels, such as physical hosts, containers, and applications, and it has the advantages of simplicity, high accuracy, ease of implementation.
TransABR: A Deep Reinforcement Learning Approach Based on Transformer for Adaptive UHD Video Streaming over Mixed Band 5G

Adaptive bitrate (ABR) algorithm is a significant transmission method of video streaming to ensure the quality of user experience (QoE). This paper propose TransABR, an intelligent ABR algorithm for ultra-high definition video (UHD) streaming in the mixed band 5G network scenario. Specifically, we first converts the adaptation issue into Markov decision process (MDP), and then select the Soft Actor-Critic (SAC) algorithm for autonomically learning to reach the goal of maximizing QoE from the perspective of deep reinforcement learning (DRL). Based on this, we utilize the Transfomer structure to build the feature extractor of policy and value network of DRL agent. Finally, in order to solve the problem of over-fitting of large model DRL algorithm in training process, we design a matching learning experience data filter, called EXP-filter, to guide agent to use high-value experience data through compact rules. The experimental results show that our TransABR achieves a higher average reward than Pensieve, which is an advanced DRL-based ABR algorithm, after the same number of rounds of training. Moreover, compared with Pensieve and other baselines, TransABR also shows a stable performance in terms of average bitrate, rebuffering time and etc.
Bayesian Network Structure Learning Algorithm Based on Score Increment and Reduction

Most score-based approaches of the Bayesian networks typically employ greedy search strategies, which optimize the local structure unconsciously and get stuck into the local optimum easily. Inspired by the decomposability of scoring function, this paper proposes a structure learning algorithm based on score increment and reduction. Firstly, the edge with the highest score increment is added under the guidance of the profit table. Because the previous operation ignores the acyclic constraint, it is necessary for some strategies, such as depth-first search to find all cycles. Then, the current structure should be thinned by deleting edges and clearing cycles on the basis of the loss table with score reduction. The optimal structure is acquired by repeating the above search process until the profit table is empty. Experiments show that the proposed algorithm has better performance of scoring results and graphical accuracy than some state-of-the-art structure learning algorithms in seven networks with different sample sizes.
Mining High Utility Sequential Patterns in Dynamic Databases

High-utility sequential pattern mining(HUSPM) has become a popular problem in the field of data mining. Many algorithms have been designed for mining high-utility sequential patterns(HUSPs), but most of the sets deal with static databases. In dynamic database mining, whenever new data comes in, the entire database needs to be rescanned to update the acquired information, thus taking up a lot of time and resources in the process of maintaining and updating the discovered information. In order to solve this problem, in this paper, we propose an incremental mining algorithm called Pre-HUSPM, based on the concept of pre-large for inserting new sequences in dynamic databases to maintain the discovered high-utility sequential patterns.
A Master-Slave Searching Method of Radar and Dome Camera Linkage System

In this paper, we proposed a master-slave searching method of radar and dome camera linkage system which is aimed to solve the problem that the target image captured by the dome camera deviates from the center of the picture or even deviates out of the picture in practical application. We analyzed the basic principle of radar and dome camera linkage and provided reasons why the dome camera grasped the target image off-center. The master-slave searching method is actually the target deviation correction method, in which radar guides the dome camera tracking is the master tracking method and video independent search tracking is the auxiliary tracking method. The method which is based on the improved YOLOv7 deep learning model and can be transplanted to domestic AI chips, detects target classification and target location just look once. An example in coastal defense of fighting against smuggling and evidence collection is given to illustrate effect of the method. It is great significance to improve the tracking effect of the existing radar and dome camera linkage system because the method can effectively reduce the false alarm rate and missed alarm rate of the target.
Automated ICD Coding Based on Neural Machine Translation

With the rapid development of the medical field, the electronic medical record (EMR) is a rich source of clinical information for medical study. However, electronic medical records are full of redundant information, which increases the difficulty of statistical analysis. The International Classification of Diseases (ICD) is widely used to describe the diagnosis of patients. A reliable automated ICD coding system can improve the quality of clinical decision support. Manual coding is time-consuming, expensive, and error-prone. To reduce coding errors and cost, we aim at developing an ICD code assignment system that automatically and accurately assigns the diagnostic description(DD) to ICD codes. Automated ICD coding is the multi-label classification task, which is to assign the ICD codes to the diagnostic description. In this paper, we apply the Neural Machine Translation(NMT) to the multi-label classification task of the code assignment problem. We proposed RAANMT(Recurrent-Based Encoder and Average Attention-Based Decoder for Neural Machine Translation), which can extract the relationship between the text and the labels to improve the automated ICD coding. Moreover, we implement experiments on Chinese dataset CDTD-TP and English dataset MIMIC III. Extensive experiments show that our RAANMT model can improve the performance of the automated code assignment.
Research on Re-recognition Method of Multi-branch Fusion Attention Mechanism for Occluded Pedestrian

Due to the problem of feature misalignment between pedestrian images, illumination, posture, occlusion and other factors have a great impact on re-recognition, an attention-based multi-branch fusion pedestrian re-recognition framework is proposed. Firstly, Resnet50 is used as the backbone network to extract the initial features, and then the extracted initial features are entered into the network in parallel, and the final features are obtained through feature fusion. The network was trained by cross entropy loss, and experiments were performed on Occluded-ReID data sets, P-ETHZ data sets and Partial-ReID data sets with serious occlusion. The accuracy was significantly improved, which proved that this method could improve the recognition rate in practical application scenarios.
Chinese-Mongolian Machine Translation Combining Sentence Structure Information

Neural machine translation have achieved state-of-the-art accuracy on widespread used language pairs, but falls short on low-resource or complex-morphological language pairs. Mongolian is a typical agglutinative language which of complex morphological changes lead to the misunderstand of machine translation. In this work, we improve the quality of Chinese-Mongolia translation by combining double syntactic structure information. Convolutional neural network follows with interest is clause structure in the sequence, and extract the clause features through window sliding. The dependency structure of sentences affects the distribution of attention between words, we construct the dependency score to upgrade the attention mechanism for deeper encoding. We show the efficacy of our model not only in Chinese-Mongolian but also English-German, and the results prove that the fusion of double syntactic structure information helps machine translation.
Research on Emergency Search and Event Evolutionary Graph Construction in Social Networks

[Purpose] With the rapid development of social networks, the public opinion environment on the Internet is changing rapidly. Nowadays, social networks have become one of the important platforms for internet users to understand current political news and discuss hot news, and the generation and dissemination of emergencies are inseparable from the important channel of social networks. It is necessary to effectively channel public opinion, reduce the malicious spread of users, and resolve the crisis of public opinion when an emergency occurs. [Method] This paper collects the comment data of three emergencies at the national security level under the microblog platform, and uses the natural language processing model to process the data, and constructs the emergency event evolutionary graph and the emergency abstract event evolutionary graph. [Conclusion] The results show that when a major emergency occurs, the severity, details and background of the event attract the most attention from the public, and they are the core elements that constitute the event evolutionary graph. In emergencies involving all aspects of national security, politics and medical care, the relevant departments shall guide a positive and favorable public opinion atmosphere in society and create a harmonious public opinion environment.
A Comparative Study of Machine Learning and Automatic Machine Learning Models for Facial Mask Recognition

In this study, we compare the performance of traditional machine learning models and automatic machine learning models in facial mask recognition. We use a dataset of images of individuals wearing masks and not wearing masks to train and test the models. We evaluate the models based on accuracy, precision, recall, and F1 score metrics. Our results show that automatic machine learning models achieve similar or slightly better performance than traditional machine learning models but at the cost of longer training time. This study's results can help practitioners select the appropriate model for facial mask recognition based on the trade-off between accuracy and efficiency. Additionally, this study provides insights into the potential of automatic machine learning models in computer vision tasks, specifically in facial mask recognition. The study concludes that while the traditional machine learning models may be more computationally efficient, the automatic machine learning models can offer comparable or better performance in facial mask recognition.
Tabular GAN-Based Oversampling of Imbalanced Time-to-Event Data for Survival Prediction

Class imbalance causes an underestimation (overestimation) of the hazard of minority class in survival prediction. A common strategy to handle class imbalance is to oversample the minority class by generating synthetic samples. This paper explores the potential of tabular Generative Adversarial Networks (GANs) for oversampling based on real world survival datasets and simulated imbalanced datasets. We compare GAN-based oversampling methods with traditional methods on generation of minority instances and balanced survival prediction. Experimental results show that balanced survival prediction after GAN-based oversampling can outperforms baseline in some situations, and also demonstrate that traditional oversampling methods perform better than GAN-based methods on both minority samples generation and balanced survival prediction.
Mining Skyline Frequent-Utility Pattern with Threshold Filtering

In the past, skyline pattern mining algorithms have achieved extensive research. The current algorithms for mining skyline patterns are inefficient and time-consuming. In practical applications, they may be inadequate because traditional skyline patterns can result in points that are extremely biased towards one dimension. Therefore, this paper introduces a novel approach for mining skyline patterns that incorporates threshold settings. Unlike traditional methods, this new approach uses multiple thresholds to eliminate extreme points that don't meet the user's requirements in certain dimensions. By doing so, the number of candidate search items is greatly reduced, resulting in a more efficient and effective search process. The proposed algorithm was evaluated through extensive experiments on various databases, and its performance was compared with that of popular existing skyline pattern mining algorithms. The results demonstrate that the new approach not only yields a more streamlined and optimized set of results, but also outperforms previous algorithms in terms of both runtime and search space.
Open Set Action Recognition Based on Skeleton

Human action recognition based on skeleton has received increasing attention due to the compact pattern of human skeleton data. Because skeleton data only captures human action information and is not interfered by contextual information such as background and illumination, it is more stable for action recognition. In the real world, we cannot find a dataset that includes all the categories of actions, and there will be many human actions out of dataset, which requires the network to make correct judgment for known actions and reject unknown actions that are not within the scope of cognition. In our work, we propose an Open Set Action Recognition based on Skeleton (OSARS) method to recognition human action in open-set field. We use evidential deep learning to predict category probability and calculate category prediction uncertainty to transform Open Set Action Recognition (OSAR) problem into prediction uncertainty measurement problem.