Reviewerly/exHarmony huggingface dataset:
Research on stitching technology based on enhanced images of substations

The digital, intelligent and visual operation of substations has become a current research trend. In order to obtain all-weather substation image information, this paper proposes to enhance the low-light image at night based on gamma correction, then match the enhanced image with feature points, transform the image by Fourier-Mellin transform (FM) to obtain the overlapping area; use the fast approximate nearest neighbour (FLANN) matching algorithm for coarse matching, introduce the progressive sample consensus (PROSAC) to finely filter the feature points to improve the correct rate; finally, the stitched image is weighted fusion processing to remove the stitching seam. (PROSAC) to finely filter the feature pairs to improve the correct rate; finally, the stitched images are weighted and fused to remove the stitching seams. The experimental results show that this algorithm can enhance the night image, improve the matching accuracy and meet the requirements of stitching quality.
A foreign body intrusion detection method for power lines based on Transformer

The power system is constantly exposed to outdoor environments, which makes it susceptible to invasion by foreign body such as tree branches and garbage bags. Currently, most deep learning-based detection methods assume the presence of foreign body in the image, and there is still room for improvement in detection accuracy. In this paper, a foreign body detection method for the power system is proposed based on Inception-V3 and Trans-former. The method first classifies inspection images according to whether foreign bodies are present, and then detects foreign body that have invaded the power system. This method does not use pre-defined datasets and converts object detection into a direct bounding box prediction problem, which greatly optimizes existing detection methods. Experimental results on actual datasets show that our research effectively improves the accuracy and efficiency of foreign body detection compared to detection algorithms based on Faster R-CNN and YOLOv3.
Research and implementation of fast image style transfer

Image style transfer refers to the process of merging the content of a source image with the style(s) of one or more reference images, thereby creating images that combine the original content with other styles. This dissertation focuses on using a convolutional neural network (CNN) to achieve this goal. The image style transfer is completed with data augmentation, a loss network, and an image transformation network. The VGG-19 network is used to extract features in the loss network, and the content loss function and style are optimized iteratively through gradient descent. Additionally, a custom residual module network is trained to enable a specific style conversion of the image. As a result, the final model shows significant improvement, with the final style loss reduced to 2000E+4, and the total loss reduced to 6000E+4, thus achieving good results.
Underwater image saliency detection based on refined attentional feedback mechanism

Underwater image saliency detection makes the terrestrial saliency detection model less effective in the application of underwater vehicles due to factors such as turbid water and unstable light, resulting in a degradation of the model's performance. We offer a model for underwater saliency detection based on an improved attentional feedback mechanism to overcome the aforementioned issues. The features at the top and bottom levels are effectively fused by forming a cascaded feedback decoder through the cross feature module and adding channel space attention, after which the residual refinement module is added for further refinement. The training and testing process uses underwater open datasets. The experimental findings demonstrate that our method is superior to other ways in comparative analysis with four general saliency detection methods and two underwater saliency detection methods on four underwater image datasets, proving the model's viability and efficacy.
Design of a multi-level domain knowledge graph reasoning framework based on temporal data

As a hot research direction in current academic studies, knowledge graph reasoning is aimed at solving the many challenges and pain points of knowledge graphs. This paper centers around temporal data prediction and presents a multi-level framework that leverages causal knowledge graphs. Our framework seamlessly integrates causal knowledge graphs with temporal data to enhance prediction accuracy. The framework is composed of two key components: causal knowledge graph construction and multi-level gated graph neural network prediction. By representing facts and relationships within the domain using causal knowledge graphs, the framework enhances the capability of the temporal data prediction model. The proposed framework design can provide better knowledge understanding for researchers in the field and achieve accurate prediction of temporal data.
An analysis of the acoustic features of English intonation for English professionals by integrating image and video analysis

Speech recognition has made breakthrough progress and been widely used. Along with the development of speech recognition, new requirements are constantly put forward. First, acoustic parameters are related to the natural attributes of speakers; second, the calculation of acoustic parameters depends on a large range of corpus resources; and in the aspects of language recognition, speaker recognition, speech visualization and automatic speech annotation, more effort needs to be put into research. English contains 48 phonemes, and the correct recognition of phonemes is an important basis for the analysis and study of the acoustic characteristics of continuous intonation. In this paper, the convolutional neural network is first used to extract visual features of different scales, and the image features of different scales are fused effectively, so that the fused feature vector contains more detailed image information, and effectively alleviates the problem of image information loss. Then, an intonation acoustic feature recognition model based on attention mechanism is constructed, which takes into account the early and late fusion of features and improves the effectiveness of information fusion. The experimental results show that the training error of the model in this paper decreases gradually with the increase of the number of iterations and tends to be stable after 1000 iterations. The model basically converges and has reliability and feasibility. In the phoneme recognition experiment, for sentences with more phonemes and sentences with fewer phonemes, the recognition rate of the model in this paper is more than 60% and the loss rate is less than 5%, and about 60 phonemes can be recognized per minute. Therefore, the model presented in this paper improves the results of English intonation acoustic feature recognition to a certain extent, which is successful.
Hash encoded neural radiance field with view-dependent mapping

Neural Radiance Fields (NeRF) constructs the connection from 3D position and 2D direction to voxel density and color in the scene by learning the implicit expression of the scene space through a multi-layer perceptron. It is capable to output high-quality images in the task of novel view synthesis. Although NeRF performs well under the ideal conditions of static scenes with precise camera calibration, it can hardly handle freely-shot images, and the low training efficiency also hinders its application in reconstructing real-world scenes. This paper proposes a NeRF model extended by hash position encoding and view-dependent mapping, which is able to better deal with image sets collected from the real-world under complex lighting conditions while improving the learning speed and the effectiveness in recovering scene details. Through experiments, it has been proven to achieve more ideal results than the classic NeRF and its variants.
Research on 3D ink scene rendering design based on Unreal Engine 5

3D ink scene rendering is a kind of non-realistic rendering, and this non-realistic 3D ink effect is the focus of this paper. This paper first introduces the production characteristics of 3D ink and wash rendering and Ureal Engine5, then based on the traditional Chinese ink and wash art characteristics, the 3D ink and wash rendering technology with reference to manual drawing mapping and cartoon strokes is designed to realize the workflow of 3D ink and wash effect in Unreal Engine 5, then the proposed design method is verified through practice, and finally the conclusion and summary are presented for the design concept and implementation, and the Finally, we conclude and summarize the design concept and implementation, and look forward to the future application of 3D ink scenes produced by UE.
Real-time peripheral vision metamer computing method

Virtual reality (VR) is a new type of media that can provide users with an unprecedented sense of immersion. In the process of achieving this sense of immersion, the spatial information perception of human vision plays a crucial role, which is one of the key requirements for human perception of the environment and virtual reality. The existing virtual reality display rendering often adopts a foveated rendering method, which utilizes the characteristics of human vision to save computational resources. Against the background of human visual characteristics, this thesis proposes a real-time computing method for peripheral vision metamer images based on the encoding of peripheral vision to address the lack of peripheral vision encoding in current foveated rendering methods. Our method renders visual metamer images more efficiently and achieves real-time computing with limited additional computing resources.
Tibetan-Chinese bilingual text detection in scene images integrating spatial attention features

Aiming at the problems of false detection and missing detection of texts in the process of text detection caused by random distribution of Tibetan texts, various scales and shapes in natural scenes, this paper proposes a natural scene Tibetan text detection algorithm based on feature enhancement of spatial attention mechanism. The spatial attention mechanism is introduced into the pyramid network module of feature extraction to extract richer local and overall information and enhance the ability of feature extraction; feature kernel clustering can better distinguish adjacent text instances, and the predicted similarity vector is accurate Aggregate text pixels to the corresponding text kernel, further improve the accuracy of scene Tibetan detection, and effectively reduce false detection and missed detection. The model is evaluated on the TCSD scene Tibetan dataset, and the results show that the F-measure comprehensive index of this method reaches 81.09%, which is better than the previous scene Tibetan detection algorithm.
Research on MobileNet-based lightweight face recognition algorithm

A lightweight face recognition algorithm based on MobileNet is proposed in this paper to address limited computational power and storage resources in patient recognition by mobile nursing robots. Firstly, MobileNet-v2 is used as the backbone network, and redundant Block blocks are pruned to reduce the number of parameters. Secondly, ShuffleNet's spatially separable convolution is introduced in the residual blocks to increase network parallelism. Finally, the original Softmax loss function is replaced with an improved ArcFace loss function, which includes a Taylor expansion in the Target logit value, to enhance network constraint and achieve better separability. Experimental results show that the improved face recognition algorithm achieves a combined recognition rate of 97% and a combined average speed of 0.725 s, fulfilling the goal of designing a lightweight and efficient deep learning network.
ResNet+LSTM-based gesture recognition method for elderly care robots

The incorporation of human-computer interaction technology into senior care robots in recent years has pushed the development of HCI technology in a more compassionate direction, with gesture interaction being the emphasis of research in this area. This paper analyzes the usage of a gesture recognition system based on ResNet+LSTM on an elderly care robot that is challenging to comprehend due to poor gesture detection accuracy, misjudgment, and gesture interaction intention. The process begins with the creation of a gesture dataset, followed by the extraction of gesture features using ResNet, the training of a neural network model using LSTM, and lastly the application of the trained model to the Pepper robot for algorithm evaluation and verification. According to the testing findings, the algorithm significantly improves the aging care robot's ability to recognize gestures.
Improved pedestrian detection based on YOLOv5s

As one of the most fundamental areas in computer vision, pedestrian detection aims to locate each object instance with a bounding box to represent its position and boundary in an input image. Recently, pedestrian detection has been attracting extensive attention in academia and industry with its essential role in high-level vision research and actual downstream task. In this paper, we build a pedestrian detection algorithm based on YOLOv5s to locate the position of a pedestrian in the input in the image. To address the problem of complex background and the human body is not easily detected by occlusion, an improved Inception module is added to YOLOv5s to extract different scale feature information. In contrast, CBAM attention is added to the CSP structure, and the EIoU loss function is used to improve the accuracy and generalization of the network and achieve accurate bounding box regression. The model is trained on the pre-processed VOC 2012 dataset to build a pedestrian detection model. Compared with the original YOLOv5s model, pedestrian recognition's accuracy and average precision are improved by 2.5% and 3.1%, respectively.
Adaptive effective class suppression loss for long-tailed object detection

Object detection is an important research in the computer vision area, the mainstream object detection tasks are generally performed on class-balanced datasets, and have made great progress. However, the data in real scenarios are usually presented as long-tailed distributions, there is an imbalance between the number of classes samples, which causes a significant decrease in the performance of object detection. Most of the current long-tailed object detection algorithms enhance the performance of the tail classes at the expense of the accuracy of the head and common classes. In this paper, we adopt Adaptive Effective Class Suppression Loss (AECSL) to adjust the attention of the model to the tail classes by allocating different weight costs to different classes during the training process. We conduct comprehensive experiments on the challenging LVIS benchmark, AECSL achieved the competitive results, with 28.6% segmentation AP and 28.5% box AP on LVIS v0.5 and 27.5% segmentation AP and 27.6% box AP on LVIS v1.0 based on ResNet-101.
Summary of image caption methods

The image caption is simply to input an image to the computer model, and the model outputs an accurate text caption according to the image to be described. This paper summarizes image caption methods based on template, retrieval and deep learning. The development history of different methods is introduced respectively. Among these, image labeling methods that use Deep Learning are effective, with the encoding code model being the most commonly used. Then it introduces the data sets and evaluation indicators that are widely used in the field of image caption. Finally, the current challenges of image caption methods are analyzed, and the future development direction is prospected.
Scale normalization and lightweight FPN for small object detection in aerial images

The detection of small objects by oriented bounding box in aerial images is a recent hot topic. However, since the aerial images are not collected at the same height, the Ground Sample Distance (GSD) is different for each image, so that small objects are easily overlooked. Existing algorithms are designed for multi-scale object detection, and feature fusion is time-consuming, resulting in a large amount of model parameters that is not easy to deploy on embedded devices. We propose three methods to address the above problems. First, we scale the collected aerial images to the same scale according to the GSD value. Second, we change the structure of Feature Pyramid Network (FPN) and only keep the necessary low-level feature maps. Finally, we rescale the anchor for the specific scene. We validate our proposed method on the DOTA dataset. The results show that the modified model using our method can identify more small-scale objects, and the maximum number of model parameters can be reduced by 2.7%, the inference speed can be increased by 13.24%, and the model size was reduced by up to 28% when the detection accuracy is the same as the original algorithm.
Allure of Simplicity

This paper develops an account of the opacity problem in medical machine learning (ML). Guided by pragmatist assumptions, I argue that opacity in ML models is problematic insofar as it potentially undermines the achievement of two key purposes: ensuring generalizability and optimizing clinician–machine decision-making. Three opacity amelioration strategies are examined, with explainable artificial intelligence (XAI) as the predominant approach, challenged by two revisionary strategies in the form of reliabilism and the interpretability by design. Comparing the three strategies, I argue that interpretability by design is most promising to overcome opacity in medical ML. Looking beyond the individual opacity amelioration strategies, the paper also contributes to a deeper understanding of the problem space and the solution space regarding opacity in medical ML.
Opening up the future field of Chinese written culture: the case of the Wordle game

With the rapid development of the internet industry, the influence of the internet context on the language habits of young people has gradually increased, and there is a tendency to gradually decouple the internet language from the discourse of real life. Contemporary Chinese is the result of thousands of years of accumulation, with a complete grammatical structure, a clear logic of usage, and a concise presentation. However, some internet users are now using a newer, not yet fully validated, collective discourse system initiated by internet users, which is not directly integrated into the learning and transmission of the Chinese language system because of its confusing grammatical knowledge. In order to enhance communication between internet users of both languages and to facilitate the integration of the new internet language system into the contemporary Chinese language system, a crossword puzzle of Chinese words may be of help to internet users. As such, this paper takes the popular foreign game Wordle as an example, quantifies its features and, through modelling, investigates, from the perspective of a game designer. Through the creation of the model and the estimation of the model results, we can see more clearly how to make adjustments to the game in order to give players a better gaming experience and make them more willing to exercise and learn contemporary Chinese in this way, triggering them to think deeply about the relationship between contemporary Chinese and online terms. Some further study specifically pay attention to the Latvian version and look into how people form their guesses given any already uncovered hints. They analyses guess patterns, easy and difficult word characteristics, and player behaviour and response.
Improved NBB cross-domain image matching algorithm based on SuperGlue

Neural Best Buddies (NBB) has been proposed to tackle the challenge of identifying semantically related features or geometrically similar parts between pairs of images that exhibit substantial variations in appearance and semantic categories. However, NBB does not have good robustness when facing rotating scenes. To address this limitation, we integrate SuperGlue, a matcher that can successfully match image pairs with significant rotation differences in the same scene, into the nearest neighbor finding process of NBB. This integration enables our improved NBB algorithm has better rotational robustness in cross-domain image matching. Experimental results demonstrate that our approach outperforms NBB.
Deep image acquisition technology based on deep learning

Depth image estimation is an important technology in integral imaging display system and its image quality has been widely concerned by researchers. In recent years, more and more researchers extract the depth images of RGB images by deep learning. To obtain high-quality depth images and solve the problem of unclear edges and incomplete outlines, we add semantic segmentation module (SSM) to the depth estimation network (DEN) to share parameters of depth estimation and semantic segmentation (DE&SS). The SSM extracts multi-level semantic feature information, fuses global feature information and local feature information effectively, and guides depth estimation with more abundant semantic feature information to improve depth image quality. Experiments are carried out on the general datasets NYU-Depth V2 and KITTI. According to the experimental results, the depth images obtained by the proposed method have clearer edges and more complete outlines than that obtained by other advanced methods.
Migrating 120,000 Legacy Publications from Several Systems into a Current Research Information System Using Advanced Data Wrangling Techniques

This article describes a complex CRIS (current research information system) implementation project involving the migration of around 120,000 legacy publication records from three different systems. The project, undertaken by Tampere University, encountered several challenges in data diversity, data quality, and resource allocation. To handle the extensive and heterogenous dataset, innovative approaches such as machine learning techniques and various data wrangling tools were used to process data, correct errors, and merge information from different sources. Despite significant delays and unforeseen obstacles, the project was ultimately successful in achieving its goals. The project served as a valuable learning experience, highlighting the importance of data quality and standardized practices, and the need for dedicated resources in handling complex data migration projects in research organizations. This study stands out for its comprehensive documentation of the data wrangling and migration process, which has been less explored in the context of CRIS literature.