Reviewerly/exHarmony huggingface dataset:
6 The structured nature of prepositional meaning

Drawing on an analysis of the Dutch preposition over, the paper discusses an alternative to the popular description of prepositional meaning as a radial network of image schemas and image schema transformations. As a nuance w.r.t. the radial network model, the paper highlights the multidimensional nature of semasiological structure. As a nuance w.r.t. the emphasis on image schemas, the paper shows how any region in the semasiological range of a multidimensionally structured word may be the source for further meaning extensions.
Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective.
Sharded Blockchain Architecture Oriented to Multilateral Collaboration of Source-Grid-Load- Storage

The scalability of traditional blockchain is insufficient to meet the large-scale access requirements of Source-Grid-Load-Storage, and a large number of authentication and data processing requirements will bring intolerable delay to the business. This paper proposes a sharded blockchain architecture oriented to the multilateral collaboration of Source-Grid-Load-Storage, improves the scalability of the blockchain by using the sharding technology, and proposes a cross-shard transaction mechanism to meet the interaction needs of multiple agents. The experiment verifies that the proposed architecture can effectively improve the operational efficiency of the blockchain in the Source-Grid- Load-Storage large-scale access scenario, and the proposed cross-shard transaction mechanism can effectively ensure the cross-domain collaboration of multiple agents.
Investigation to Screen Region Division for Stable Input of Eye-controlled Cursor

Aiming at the problems of stable cursor hovering and "Midas Touch" that are difficult to achieve in the current eye-controlled cursor technology, this paper has carried out an experimental study on the stable input of the eye-controlled cursor: Firstly, divide the screen into several small rectangular areas. When the eye-controlled cursor appears in any rectangular region, the position of the cursor will be fixed at the center of the rectangular region, and there will be no shaking of the eye-controlled cursor in a small range due to the natural wobble of the eyeball pupil. Then, the screen interface UI and icon sizes are adjusted according to the screen division level, to ensure that there is only one click target in each small rectangular area of the screen, and to ensure that there is no ambiguity when clicking on the eye-controlled cursor. Finally, by combining the task completion time, task completion rate, error rate, task difficulty, performance indicators, user satisfaction and other test data information to determine the most appropriate screen division size.
MDNet: A Fusion Generative Adversarial Network for Underwater Image Enhancement

Underwater images are widely used in ocean resource exploration and ocean environment surveillance. However, due to the influence of light attenuation and noise, underwater images usually display degradation phenomena such as blurring and color deviation; an enhancement method is required to make the images more visible. Currently, there are two major approaches for image enhancement: the traditional methods based on physical or non-physical models, and the deep learning method. Inspired by the fusion-based idea, this paper attempts to combine traditional methods with deep learning and proposes a multi-input dense connection generator network (MDNet) for underwater image enhancement. Raw images and processed images are input into the network together, the shallow information is fully utilized by dense connection, and the network is trained in generative and adversarial manner. We also design a multiple loss function to improve the visual quality of the generated images. We conduct both qualitative and quantitative experiments, and then compare the results with state-of-the-art approaches comprehensively using three representative datasets. Results show that the proposed method can effectively improve the perceptual and statistical quality of underwater images.
3D Reconstruction of Celadon from a 2D Image: Application to Path Tracing and VR

We present a straightforward approach for reconstructing 3D celadon models from a single 2D image. The celadon is a historical example of the surface of revolution. Our approach uses a surface of revolution technique to generate the basic shape of the celadon and then applies texture mapping to create a realistic appearance. The process involves detecting the contour and corners of the celadon image, determining an axis of revolution, generating a profile curve, and finally constructing a 3D celadon model. Additionally, we create models as triangular meshes at multiple resolutions, employing a B-spline curve as the profile curve. It enhances the adaptability of the models for various purposes. We render various scenes using a path tracer to assess the suitability of the generated 3D celadon models and generate a VR celadon museum with the models. Overall, our approach offers a simple and efficient solution for reconstructing a 3D celadon model, generating VR content, and demonstrating extensive applicability across numerous disciplines.
US-GAN: on the importance of ultimate skip connection for facial expression synthesis

We demonstrate the benefit of using an ultimate skip (US) connection for facial expression synthesis using generative adversarial networks (GAN). A direct connection transfers identity, facial, and color details from input to output while suppressing artifacts. The intermediate layers can therefore focus on expression generation only. This leads to a light-weight US-GAN model comprised of encoding layers, a single residual block, decoding layers, and an ultimate skip connection from input to output. US-GAN has $3\times$ fewer parameters than state-of-the-art models and is trained on $2$ orders of magnitude smaller dataset. It yields $7\%$ increase in face verification score (FVS) and $27\%$ decrease in average content distance (ACD). Based on a randomized user-study, US-GAN outperforms the state of the art by $25\%$ in face realism, $43\%$ in expression quality, and $58\%$ in identity preservation.
University 4.0 concept: educational and scientific policies, innovative development of vocational education and training

The paper aims to generalize theoretical and practical aspects of educational and scientific policies and management of the vocational education innovative development within a University 4.0 model. The study of the University 4.0 continuum in this paper is based on a systematic approach. Our research content and construct validity, and reliability of its results were analyzed at the stage of developing a University 4.0 scale of environments using the SPSS (Statistical Package for the Social Sciences) and LISREL (Linear Structural Relations) software packages. Based on an explanatory factor analysis we developed a three-dimensional indicative scale of environments for an innovative entrepreneurial university. The efficient and reliable tool which is offered for assessing the internal environments of an innovative entrepreneurial university and its activities as a University 4.0 type is scaling. By scaling the University 4.0 environments our universities could succeed in upgrading their innovative entrepreneurial characteristics aimed to benefit their students, government agencies, and the entire economy at the macro level.
Polymorphic Types with Polynomial Sizes

This article presents a compile-time analysis for tracking the size of data-structures in a statically typed and strict functional language. This information is valuable for static checking and code generation. Rather than relying on dependent types, we propose a type-system close to that of ML: polymorphism is used to define functions that are generic in types and sizes; both can be inferred. This approach is convenient, in particular for a language used to program critical embedded systems, where sizes are indeed known at compile-time. By using sizes that are multivariate polynomials, we obtain a good compromise between the expressiveness of the size language and its properties (verification, inference).
Applying an AHP–GIS model to hybrid wind–solar energy site selection in a hot desert region: A case study of the Kuwaiti desert

The aim of this research is to present how sand encroachment and dust fallout criteria impact the siting of hybrid wind – solar energy in hot desert regions. Analytical Hierarchy Process and Geographic Information System techniques were used to generate a hybrid wind – solar energy suitability map for Kuwait. For siting solar farms, dominant aeolian geomorphic processes like sand encroachment and dust fallout rate, distance to urban areas and farms, proximity to main roads and transmission lines, and slope criteria were used. Wind speed, distance to urban areas and farms, and proximity to main roads and transmission lines were considered for siting wind farms. According to the findings, sand encroachment and dust fallout rate were the top two criteria in terms of weighting for siting solar energy. The lowest hybrid suitability class covers the smallest area, with an area of 19 km2 of the study area, and is located in the south of the study area. The highest hybrid suitability class covers 1349 km2 and is located in the northeast, midwest, southeast, and south. The sensitivity analysis results demonstrate that sand encroachment and dust fallout rate have a significant role in siting hybrid wind – solar farms in hot desert regions.
Application of a new Transformer-based model and XGBoost to improve one-day-ahead solar power forecasts

Forecasting solar photovoltaic (PV) generation has become important since solar power exhibits chaotic and intermittent behavior. With the development of artificial intelligence (AI) techniques, various deep learning algorithms have been shown to be very powerful for renewable power forecasts. This study aims to develop a forecasting model by applying a Transformer-based model for time series to predict one-day-ahead photovoltaic generation. More importantly, this study presents a new process that combines an advanced imputation technique with a post-processing technique and the newest Transformer structure. Unlike an original Transformer structure, the proposed Transformer model has more normalized and dropout layers between blocks, as well as utilizes multi-head attention mechanisms. In the pre-processing step, missing data were imputed using XGBoost with predictive mean matching (PMM) and bootstrapping, which preserves a complex relationship among the inputs. Finally, a combined post-processing technique based on daily numerical fitting curves and a Gated Recurrent Unit (GRU) was employed to enhance forecasting accuracy. The proposed model was trained using historical measurement data from ten solar farms in Taiwan and Numerical Weather Predictions (NWPs) data from the Taiwan Central Weather Bureau (CWB). Based on the forecasting results, the proposed model outperforms state-of-the-art prediction models.
A Spatiotemporal Federated Learning Based Distributed Photovoltaic Ultra-short-term Power Forecasting Method

Accurate distributed photovoltaic power prediction plays a crucial role in supporting power retailers in making optimal trading strategies and distribution network operators in making reasonable scheduling plans. The mining and utilization of distributed photovoltaic power correlation is an effective way to improve the forecasting accuracy of the target station. However, the current photovoltaic power forecasting methods must rely on data sharing in correlation mining, which may lead to serious data privacy problems. In order to protect the privacy of data during power modeling while ensuring the effective utilization of spatiotemporal correlations, the spatiotemporal federated learning based distributed photovoltaic ultra-short-term power forecasting method is proposed in this paper. In this method, the local server takes the Gated Recurrent Unit-based autoencoder as local model to extract local temporal features and forecast the local power. The central server uses Federated Averaging to aggregate and update the parameters of each local forecasting model and uses t-distributed Stochastic Neighbor Embedding to process the temporal features into the global feature which implies the spatiotemporal correlations between stations. Subsequently, the local model combines local temporal features and global spatial features to achieve power prediction. Through the doublelayer sharing of local model parameters and local temporal features, spatiotemporal correlations are effectively utilized while ensuring the privacy of data and the forecasting accuracy of each distributed photovoltaic is improved. Finally, the case study via real-world data is conducted to verify the effectiveness of the proposed method.
A Minutely Solar Irradiance Forecasting Method Based on Multidimensional Feature Extraction Using All-Sky Images

Accurate solar irradiance forecasting is the basis for accurate photovoltaic (PV) power forecasting. However, previous minutely irradiance forecasting methods based on allsky images have difficulty in adequately extracting the cloud features that are essential for future irradiance fluctuations. Therefore, in this paper, a minutely irradiance prediction method based on multidimensional feature extraction of all-sky images is proposed. The raw images are first pre-processed and classified into four weather types by cloud-sky identification. Then the cloud displacement vector is calculated using the optical flow (OF) method, and capture the subimages of the cloud domain that will cover the sun in the future dynamically according to the calculation results. Subsequently, we use the convolutional neural network (CNN) to extract multidimensional features including the local features and overall features. The multidimensional features are combined with relevant meteorological factors and historical irradiance to construct irradiance mapping models for each of the four weather types to achieve irradiance prediction on a ten-minute scale. Simulation results show that the proposed method can better introduce the key cloud information and improve the prediction accuracy.
Safety helmet wearing detection in thermal power plant based on improved YOLOv5

In industrial safety accidents, the failure of workers to wear helmets is a major cause. A deep learning detection model can now be developed using computer vision techniques to detect whether workers in thermal power plants are wearing helmets. An improved helmet-wearing detection algorithm for YOLOv5 is proposed in this paper which overcomes the shortcomings of existing algorithms and successfully detects small, obscured, and dense targets. The algorithm enhances the efficiency of detecting small targets by adding classification convolutional blocks. Furthermore, it integrates the MA channel attention mechanism into the Neck network to reconstruct channel weight, filter out erroneous channels, increase feature expression capability, and introduce FReLU to enhance the spatial sensitivity of the activation function. Under the SHWD dataset, the mAP of this model reaches 91.9%, an improvement of about 1.6% as compared to the original model. The study demonstrates that the improved algorithm can satisfy the practical requirements of thermal power plant work sites and performs well for the helmet detection task.
Non-oscillation Path Planning Based on Artificial Potential Field

Considering that the mobile robot is not flexible enough, in order to avoid the oscillating motion of the robot with repeated steering changes, when encountering obstacles in front, according to the rule that each deflection to the right is not more than 5 degrees, an improved forced resultant force deflection method and virtual obstacle adding method are proposed to solve the local minimum problem and avoid the occurrence of oscillation, which need not get the characteristics after the oscillation for the condition of virtually adding obstacles. The simulation results show that there is no oscillation in different obstacle environments, and the path is smooth without the local minimum problem. By virtually adding multiple obstacles, the robot can quickly leave the dense obstacles.
Algorithm design and application research of English picture recognition intelligent system based on convolutional neural network

With the development of technology and the advancement of educational concepts, the country has put forward new requirements for the training model in the context of the era of artificial intelligence. The advancement of science and technology has made big data and artificial intelligence the focus of exploration and research of the times. Among them, the deep learning network in the field of artificial intelligence can solve many complex pattern recognition problems by simulating the human brain, but there are still shortcomings. Most existing deep networks have complex structures, involve a large number of hyperparameters, and suffer from extremely time-consuming training processes. This research introduces the basic knowledge of augmented reality technology and artificial intelligence into the English teaching system, and explores the deep integration of science education and artificial intelligence based on augmented reality technology. A wide learning system model based on convolutional neural network is proposed. First, the image is extracted through the convolutional layer and the pooling layer of the convolutional neural network to obtain the extracted features. In order to extract the features more accurately, the Adam algorithm is used to iteratively update the parameters of the convolutional layer and the pooling layer for a certain number of times. Then, the extracted image features are input into the width learning system, and feature nodes and enhancement nodes are established. Using the SqueezeNet design principle and the SVD algorithm to compress the proposed network as a whole, a lightweight network is finally obtained.
Small object detection algorithm based on improved yolov5

The detection of small objects is always a difficulty in the field of object detection. This paper proposes an improved yolov5 algorithm to improve the performance of the algorithm for small object detection. In this paper, the BoT3 block is used to instead of the last C3 block in the backbone, which greatly improves the ability of network to feature extraction and fusion ability. Coordinate attention mechanism module is also added to strengthen feature extraction. In addition, EIoU_Loss replaces the CIoU_Loss function to solve the problems that penalizing failure with equal ratio of aspect ratio. At last, in order to better detect small targets in the image, a smaller size prediction head is added. Moreover, before starting the training, the paper uses K-means method to calculate the new anchors. Experimental results show that evaluation indexes of the model have been improved partially. To be specific, the Precision increases 3.8%; the Recall increases 5.9%; the MAP:0.5 increases 6.3%; the MAP0.5:0.95 increases 3.8%.
Pedestrian Detection Based on Improved YOLOv3 Network

Pedestrian detection has important applications in driverless and road detection. For the common problems of pedestrian detection such as multi-scale target and occlusion, this paper improves the YOLOv3 network structure by introducing CBAM attention mechanism and SPP structure to improve the expression ability of the output feature layer of the backbone network, and greatly improves the perceptual field to enhance the network's perception of features. By conducting experiments on the INRIA Person dataset, The experimental results show that the improved YOLOv3 network improves the detection accuracy from 89.95% of the original YOLOv3 network to 92.06%, an improvement of 2.3 percentage points, which is a significant improvement in accuracy. Compared with YOLOv3, the improved network in this paper has more outstanding effect on real-time detection.
Seam Carving Algorithm Based on Partitioning

The retargeting technique is essential for image resizing and greatly preserves regions of interest in the image. In view of the long operation time and the distortion of salient objects of the existing retargeting algorithm when many seams are removed, a new seam carving (SC) algorithm based on image blocking is proposed. Firstly, combine the Sobel algorithm with label decoupling framework (LDF) to fully extract the regions of interest in the image. Then image blocking reduces the range of dynamic programming, which can effectively reduce the running time of image retargeting. Finally, remove the seams in proportion to obtain the result. Experiments are conducted with RetargetMe and DUTOMRON image datasets. Experimental results show that, the proposed method preserves the integrity of significant regions, reduces the running time and improves the operational efficiency.
Clipping-based Neural Network Post Training Quantization for Object Detection

Convolutional Neural Networks (CNN) have achieved amazing breakthroughs in computer vision, speech recognition, and other fields. However, with the development of deep learning, the number of weights in the neural network is increasing, consuming a lot of storage and computing resources. The high storage cost and computational complexity severely restrict the deployment of deep learning on embedded mobile devices. Therefore, the compression and acceleration of convolutional neural networks becomes particularly important. Therefore, in order to compress the size of the target detection model and speed up the calculation, this paper proposes a post-training quantization method for clip limit operation, limits are performed on the input data stream and weight parameters during training, then uses the parameter quantization method to quantize the model parameters from 32-bit floating point to 8-bit Integer. Under the premise of no impact on the performance of the algorithm, the model can be effectively compressed and the calculation speed can be improved. On the public data set PASCAL VOC2012, the performance of this algorithm is evaluated by two classic target detection algorithms, Faster R-CNN and YOLO V3. The experimental results show that compared with the original model, it saves 75% of the storage space and improves the calculation speed 2 times faster; FPS is also improved compared to standard quantization.