Reviewerly/exHarmony huggingface dataset:
The Impact of Educational Development on Economic Growth: Evidence from Different Regions of China

Education development has always been a significant influencing factor in fostering a strong economy. The level of education development greatly impacts a nation's quantity of skilled individuals, technological and military advancements, and industrial growth, among other factors. A robust education level and abundant educational resources can promote and stabilize a country's overall economic progress. This article takes China as a primary example, conducting a specific comparative analysis between the education development levels and the richness of educational resources in the northwestern and eastern regions of China. Furthermore, from an educational perspective within the context of education development, the article explores how education influences the current state of economic development in the region. Simultaneously, it deduces that there exists a proportional relationship between the degree of education development and economic advancement and concludes that education's impact on the economy is both long-term and positive.
Applying Ethereum blockchain and IPFS to construct a multi-party used-car trading and management system

The main causes for the risks of used-car trading lie in the information asymmetry between buyers and sellers and the absence of a trust mechanism. This study proposed applying the Ethereum blockchain and InterPlanetary File System (IPFS) to construct a used-car trading and management information system that supports decentralized data storage services. This mechanism could support the permanent storage, immutability, and traceability of car maintenance data through the operation of smart contracts. Furthermore, car information is stored and managed by IPFS. Slither monitors the security of this system by detecting security bugs in the operation of smart contracts.
Interpretation of the concept through the prism of quantum theory

Changes in the field of quantum theory taking place in modern science have also affected linguistics. Quantum mechanics is one of the major achievements of past difficulties. The object of study of quantum theory is a minimal, weekly particle - a quantum. The functional liquidity of quanta and linguistic units has been revealed, which makes it possible to transfer research from quantum mechanics to the field of cognitive linguistics. The quantum-cognitive approach expands the view of the concept of nature, highlights its quantum properties and allows us to consider its dynamics as a quantum wave process. In the article, the subject of research is the concept, its dynamics as a change in the states of the concept and its interaction in various combinations. The work pays attention to such phenomena as quantum entanglement, quantum nonlocality, measurement, and contextuality. Modeling in the quantum-cognitive aspect involves considering changes in the state of a concept under the influence of context. The state of the concept through the prism of quantum mechanics is considered as a wave function that changes under the influence of context. To apply general methodological approaches to describing both phenomena, we will focus on such phenomena as “quantum uncertainty” and “quantum entanglement.” The article explores the concept through the prism of quantum theory. The material for the study was scientific publications and articles selected by a continuous sampling method on the topics “Quantum Physics”, “Quantum Mechanics”, “Quantum Physics”, “Cognitive Linguistics”. The object of study of the article is the concept as a quantum immaterial cognitive formation that exists in the human mind. The study of the concept is based on a synthesis of the principles of two scientific directions: quantum mechanics and cognitive linguistics.
Overview on reinforcement learning of multi-agent game

Abstract Game intelligence is an emerging hot topic in the field of artificial intelligence in recent years, and multi-agent learning is a frontier topic in the field of the intelligent game, which has a huge development prospect in all fields. This paper introduces the origin of reinforcement learning (RL) from the law of effect in animal experimental psychology and the optimization theory of optimal control. Then, the author describes the systematic composition of multi-agent reinforcement learning (MARL), and summarizes the classification of its research methods. The existing problems of MARL are discussed from three aspects: non-stationarity of the environment, partial observability, and the dimensional explosion problem. Finally, an outlook on the future is given based on the current development status of MARL and the important and difficult issues in the research field.
Review on data imputation methods in machine learning

Abstract Data is an important element in the analysis of machine learning. It is usually measured based on observations and is also an indispensable element in training a model. Good preparation of data helps enhance the performance of analysis and is able to deliver reliable final results. However, lots of factors influence the dataset and some lead to the loss of some data. When some portion of the data is missing, it causes biases in the final prediction outcomes. In order to minimize the consequences of missing data, several data imputation methods are established to solve the problem. This paper will first talk about some basic concepts about missing data. In the following sections, the paper will present several popular data imputation methods, including complete case analysis, single imputation, and multiple imputations. Applications of some methods will be presented to see how they can be used in real analysis situations. Finally, the paper will talk about the limits of current data imputation methods.
Traffic sign detection based on classic visual recognition models

Abstract This paper aims to assess the effectiveness of various object detection high-level architectures, including Faster R-CNN, R-FCN, SSD, and YOLO, in recognizing traffic signs. Since traffic sign recognition is a critical element of driver-assistance and autonomous driving systems, this study explores how five distinct architectures for feature extracting (ResNet V1 50, ResNet V1 101, Inception V2, Inception-ResNet-V2, and Darknet-19) can enhance the performance of these meta-architectures. To evaluate the models, the authors fine-tuned a pre-trained object recognition algorithm on the German Traffic Sign Detection Benchmark (GTSDB) dataset. While the Faster R-CNN Inception Resnet v2 proved to be the most accurate among the models, the unbalanced distribution of mandatory, prohibitory, and danger signs in the GTSDB dataset may have caused biased results in object detection. Overall, the result obtained from this study can help improve the accuracy of driver-assistance and autonomous driving systems by providing insights into the performance of various object detection models in recognizing traffic signs.
Combining feature pyramid and CycleGAN for image generation

Abstract Image generation has always been a hot topic in computer vision community, which aims to learn the data distribution from a give image dataset and then generates new images obeying this distribution. Thanks to the rapid development of convolutional neural networks, breakthroughs have been made in the accuracy and speed of image generation. Currently, image generation is mainly based on the framework for generating countermeasures networks. However, limited by the quality of features, the generated images still have problems such as edge blurring, which restricts large-scale practical applications. In this paper, we propose an image generation algorithm that combines feature pyramid network (FPN) and CycleGAN. Specifically, the FPN containing two upsampling and feature fusion operations is added after the residual blocks and before the decoder of the CycleGAN generator, which can help the generator produce more detailed images and learn from features at different scales. Qualitative and quantitative experimental results show that the CycleGAN model with FPN outperforms the original CycleGAN model in terms of image generation.
Prediction of breast cancer based on RF, SVM and PCA

Abstract Breast cancer is often called the pink killer. The most recent statistics from the 2018 International Agency for Research on Cancer (IARC) survey show that the incidence rate of breast cancer is 24.2%, placing it first among women’s cancers globally, with 52.9% of cases occurring in developing nations. When cells in the breast start to grow abnormally, it can lead to the formation of tumours. These tumours can be detected through X-ray imaging or felt as lumps in the breast area, so early detection and treatment are the key to reducing the mortality rate of breast cancer. In this paper, machine learning techniques are employed to construct a breast cancer prediction model using the breast cancer dataset. Different algorithms were utilized in this paper, e.g., 3 machine learning algorithms - Support Vector Machines (SVM), Random Forest (RF), and Principal Component Analysis (PCA) - were utilized to build models for this task. The resulting models achieved impressive performance metrics, with accuracy rates of 94%, 97%, and 96% respectively. The author also finds that some features in the data are insignificant for prediction, and discarding them can lead to faster training time. However, more data is needed to achieve better performance in actual scenarios. These models can help doctors diagnose breast cancer more quickly and accurately.
For higher accuracy: 3D eye movement gaze depth estimation method based on Mix-TCN

Abstract Currently, the biggest obstacle in applying eye tracking technology in Virtual Reality (VR) and Augmented Reality (AR) scenes is the particular difficulty in choosing distance and object distance in 3D scenes. In previous research, geometric calculation methods using vestibular ocular reflex (VOR) and research on binocular visual angle have been studied to some extent, but unfortunately, their effects have not reached a practical level. The paper proposes a new research idea to estimate the depth of binocular gaze using a method of time series eye movement data analysis based on depth learning, and proposes a Mix-Temporal Convolutional Network (TCN) optical time series network. By combining VOR and deep learning theory, the paper has realized the current state-of-the-art technology for estimating the depth of gaze from the movement of the gaze.
An Adaptive Dehazing Network based on Dark Channel Prior and Image Segmentation

Abstract Image dehazing has always been a popular research topic in the computer vision community, which aims to analyze and process foggy images to enhance detailed information and obtain better visual effects. Previous efforts mainly follow the idea of image enhancement or image restoration. Though the performance had improved, they rely on prior knowledge and often over-processed some specific areas, especially for the sky. In this paper, an adaptive parameters optimization dehazing algorithm based on the dark channel prior was proposed. Specifically, we adopted PSO_FCM to segment the image into the foreground area, the sky area, and the edge mutation area, and assign different patch values to different regions. Extensive experiments demonstrate the effectiveness of our proposed method.
Transformer-based visual question answering model comparison

Abstract The potential use of visual question and answer (VQA) models in fields including robotics, autonomous driving, and health care has recently attracted a lot of attention. However, the development of the multi-modular learning VL (Vision-Language Learning) model itself in the “pre-training + fine-tuning” mode is challenged under the rapid development of large-scale language models. Therefore, optimizing the value and feasibility of the single-task model itself is also worth thinking more cautiously. With both model principles, structures, and experimental contrast analysed in this paper, the two excellent models, LXMERT and UNITER, are evaluated to demonstrate the reasons for performance differences on specific downstream tasks VQA deeply. In addition, this paper provides insight into the possibility of further model optimization in the future for multi-modular tasks, especially VQA. In comparison to UNITER, the findings reveal greater accuracy in the training set and verification set from beginning for LXMERT, which indicates that the better model architecture and pre-training method is where LXMERT’s higher adaptability probably comes from. However, the UNITER, which has fewer parameters and smaller architecture, can also achieve fine results after fine-tuning. This paper highlights the differences in accuracy shown by existing Transformers-based VQA models in local test sets and explores the possibility of optimizing VQA systems through fine-tuning.
Virtual flight-based design and simulation of a quadrotor UAV

Abstract Lately, Quadrotor UAVs control the attitude and position of the vehicle by adjusting four motor speeds to vary the rotor speed and achieve changes in lift. A quadrotor UAV is a six-degree-of-freedom vertical elevator. As drone technology matures, the demand for drones is increasing and virtual drone control technology offers new ways to skillfully control drone flight techniques, with multiple scenarios virtually minimising the variety of situations encountered in real-world situations. At the same time, the gradual maturation of 5G technology opens up even more possibilities for how drones can be controlled.
YOLO sparse training and model pruning for street view house numbers recognition

Abstract This paper proposes a YOLO (You Only Look Once) sparse training and model pruning technique for recognizing house numbers in street view images. YOLO is a popular object detection algorithm that has achieved state-of-the-art performance in various computer vision tasks. However, its large model size and computational complexity limit its deployment on resource-constrained devices such as smartphones and embedded systems. To address this issue, we use a sparse training technique that trains YOLO with L1 norm regularization to encourage the network to learn sparse representations. This results in a significant reduction in the number of parameters and computation without sacrificing accuracy. Furthermore, we apply a model pruning technique to the sparse-trained model to reduce the model size and computation. We evaluate our proposed method on the SVHN (Street View House Numbers) dataset. We show that it performs comparably to the original YOLO model while reducing the model size by 5% and the computation time by 7%. Overall, our proposed YOLO sparse training and model pruning technique provides an effective solution for deploying YOLO-based object detection models on resource-constrained devices.
Learning to self-adaptive robust dehazing network for video detection in drainage pipeline

Abstract With the continuous urbanization and expansion of the built-up area of cities, the requirement for regular maintenance of urban underground drainage pipeline will be further increased. However, the drainage pipeline environment is humid and the water quality conditions are complex, which leads to the existence of uneven concentration of haze in the images captured by CCTV (closed-circuit television). To address these problems, this paper suggests a self-adaptive robust dehazing network for video detection in drainage pipeline. The method extracts feature in the encoder by self-adaptive module and fully multi-scale feature alignment module, and the dehazed image is gradually recovered in the enhanced decoder after the feature recovery module. The results of the experiments indicate that this method significantly improves the robustness and generalization ability of model dehazing by capturing global hazy features at multi-scale resolution, and it is an effective method for applying to drainage pipeline dehazing.
Colorization of gray scale images based on convolutional block attention and Pix2Pix network

Abstract Image colorization is a key research direction in the field of computer vision, which aims to predict the color information of grayscale images and convert them into color images. Traditional image colorization methods need to provide a large number of color points to mark each object in the image. As a result of the rapid development of convolutional neural networks, image colorization methods have also improved, with deep learning methods benefiting the most. However, due to the lack of feature quality, there are still many challenges in practical applications, such as boundary blurring. In this paper, we propose a gray image colorization method based on the Convolutional Block Attention Module (CBAM) and Pix2Pix network. Specifically, we add the CBAM module to the U-net generator network, which helps adaptively capture sensitive features, thereby enhancing the quality of features and image coloring. The effectiveness of the proposed model is verified by experiments.
Study on video key frame extraction in different scenes based on optical flow

Abstract Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.
CycleGAN-based image translation from MRI to CT scans

Abstract In recent years, the application of deep learning techniques to medical image analysis has shown promising results in improving diagnosis and treatment of diseases. One such technique is CycleGAN, a variant of Generative Adversarial Networks (GANs) that enables unpaired image-to-image translation. This paper presents a CycleGAN-based approach for transforming CT and MRI scans, which can provide doctors with more diagnostic information and assist in the prediction and diagnosis of tumors. Our experiments are based on brain scan images collected from the Kaggle dataset, with no paired information available. The generator and discriminator models of the CycleGAN are trained with the Adam optimizer and a cycle consistency loss weight (λ) of 10. The total training time is about 12 days, and the model is tested for 75 epochs with a fixed learning rate of 0.0002. The results demonstrate the effectiveness of the proposed method, achieving high-quality image translation from MRI to CT scans. The advantages of CycleGAN in medical image analysis include its ability to handle unpaired data, perform cross-domain image translation, ensure cycle consistency, and generate diverse outputs. Future work can further explore the use of CycleGAN for other medical image analysis tasks and investigate how to optimize the model performance.
Facial emotion classification based on the modified VGG and MobileNet

Abstract Emotion classification is an essential area of face recognition, which is widely applied recently, especially in information security. This study focus on model performance to explore a more efficient one that can produce a more precise prediction. In this paper, this study implement two distinct models, which are modified to classify emotions. The adaption models are added by one convolutional layer, one flatten layer, followed by activation layer and loss function layer. In order to estimate and compare them more straightforwardly, the metrics such as precision, loss, accuracy, recall and auc are introduced. Also, data augmentation to enlarge data sample size is achieved by rotation, width and length shift as well as horizontal lift. In the end, VGG16 shows a better performance with 0.9238 accuracy and 0.7780 loss in training, whereas MobileNet is slightly less outstanding with 0.9009 accuracy and 1.0280 loss in training. This study deal with choices of specific emotion classification models and attain a conclusion that VGG16 performs better in the given dataset.
Experimental evaluations of model-based reinforcement learning combined with MPC

Abstract Model-based reinforcement learning has reached outstanding effects in control and decision areas with high sample efficiency compared with model-free algorithms. However, how to combine learning and planning, aiming at improving effect and reducing the cost of planning at the same time seems to be challenging in the model-based area. In this paper, several state-of-the-art model-based algorithms combined with MPC: TD-MPC, PlaNet and Dreamer, are discussed and compared. Theoretical background and features of these methods are analyzed based on the continuous control environments. This work also covers how to design model-based method for different tasks and future directions for improving performance for model-based reinforcement learning research.
End-to-end comparative opinion quintuple extraction as bipartite set prediction with dynamic structure pruning

Comparative Opinion Quintuple Extraction (COQE) is an emerging task focused on identifying comparative relationships within sentence-level comments. COQE involves extracting a quintuple that encapsulates a specific comparative relation, encompassing a subject, object, comparative aspect, opinion, and preference. Previous approaches to COQE relied on pipeline methods, which unfortunately suffered from error propagation during the sequential process. In our work, we present an efficient and highly effective end-to-end solution for COQE, improving three key aspects: encoding, decoding, and learning. First, in terms of feature encoding, we introduce external syntactic dependency structural features to enhance the modeling of opinion elements. We also propose a novel dynamic structural pruning (DSP) mechanism to refine and optimize the raw syntax structure, ensuring alignment with the end task. Second, regarding decoding, we address the challenge of non-coherent relationships among quintuple constituents by formulating COQE as a set prediction problem. We employ a non-autoregressive decoding scheme to simultaneously generate all possible quintuples in parallel, increasing decoding speed while retaining the advantages of a generative approach. Finally, from a learning perspective, we introduce three advanced training targets: bipartite matching learning loss, cross-element contrastive loss, and mention boundary learning loss. These targets promote interaction learning between quadruples and elements. Through extensive experiments on three benchmark datasets, our end-to-end COQE system demonstrates significant performance improvements over both current pipeline baselines and state-of-the-art joint systems by 2 to 3 points. We also demonstrate that our proposed dynamic pruning strategy over syntactic dependency features is important to the overall performance, and meanwhile our system is capable of producing explainable predictions. Furthermore, the non-autoregressive decoding greatly enhances the inference efficiency, and multiple advanced training losses help achieve better learning process.