Reviewerly/exHarmony huggingface dataset:
Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters

Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The back-propagation network, support vector machine, and light gradient boosting machine (LightGBM) models were used to establish the prediction model, and Shapley additive explanations were used to analyze the relationship between the input variables and the prediction results to simplify the structure of the prediction model. Taking Lanzhou New District in Gansu Province as an example, the results showed that the LightGBM model performed the best, with the root mean square error of 126.1 W/m2. Shapley additive explanations analysis showed that weather type was not a significant factor in the LightGBM model. Therefore, the weather type was removed from the LightGBM model and the root mean square error was 135.2 W/m2. The results showed that extra-terrestrial radiation and limited weather forecast parameters can be used to predict hourly global solar radiation with satisfactory prediction results.
A Convolutional Recurrent Neural-Network-Based Machine Learning for Scene Text Recognition Application

Optical character recognition (OCR) is the process of acquiring text and layout information through analysis and recognition of text data image files. It is also a process to identify the geometric location and orientation of the texts and their symmetrical behavior. It usually consists of two steps: text detection and text recognition. Scene text recognition is a subfield of OCR that focuses on processing text in natural scenes, such as streets, billboards, license plates, etc. Unlike traditional document category photographs, it is a challenging task to use computer technology to locate and read text information in natural scenes. Imaging sequence recognition is a longstanding subject of research in the field of computer vision. Great progress has been made in this field; however, most models struggled to recognize text in images of complex scenes with high accuracy. This paper proposes a new pattern of text recognition based on the convolutional recurrent neural network (CRNN) as a solution to address this issue. It combines real-time scene text detection with differentiable binarization (DBNet) for text detection and segmentation, text direction classifier, and the Retinex algorithm for image enhancement. To evaluate the effectiveness of the proposed method, we performed experimental analysis of the proposed algorithm, and carried out simulation on complex scene image data based on existing literature data and also on several real datasets designed for a variety of nonstationary environments. Experimental results demonstrated that our proposed model performed better than the baseline methods on three benchmark datasets and achieved on-par performance with other approaches on existing datasets. This model can solve the problem that CRNN cannot identify text in complex and multi-oriented text scenes. Furthermore, it outperforms the original CRNN model with higher accuracy across a wider variety of application scenarios.
Owner name entity recognition in websites based on multiscale features and multimodal co-attention

Identifying the owners of online devices on the Internet can enable numerous network security applications. For example, fast and accurate Owner Name Entity Recognition (ONER) of websites is critical to find influenced owners in light of new security threats. In this situation, as a specific task of Multimodal Named Entity Recognition (MNER), ONER is essential and helpful for network security. Currently, most of the existing MNER models only use texts and images, so they cannot effectively utilize the multimodal data of devices to achieve ONER accurately and fast. In order to improve performance and training speed simultaneously, the paper proposes a framework MFMCA: Multiscale Features and Multimodal Co-Attention. MFMCA is based on a two-step gated co-attention with fewer transformer blocks and simultaneously uses texts, images, and domains. Also, MFMCA extracts multiscale image features to get a fine-grained hint for ONER. Moreover, due to the lack of MNER datasets, the paper manually labels a multimodal dataset containing texts, images, and domains for MNER research. The experiments show that MFMCA achieves 0.8211 F1 scores on the recognition of owner entities, which is competitive compared with 0.8288, the best performance of existing state-of-the-art MNER models. However, MFMCA saves about 34% training time on the proposed dataset.
A photovoltaic surface defect detection method for building based on deep learning

The inspection and diagnosis of building engineering involve health monitoring of buildings and related facilities, and the utilization of renewable energy, such as solar energy, is crucial for smooth operation of modern construction projects. The detection of solar panel defects is related to the reliability and efficiency of building photovoltaics and has become a field of concern. Using deep learning to detect defects can improve the stability of building photovoltaics. However, achieving a balance between algorithm accuracy and reasoning speed requires further study. This paper presents an improved algorithm based on YOLO-v5, named YOLOv5s-GBC, which improves accuracy and inference speed. This demonstrates the advantages of fast and accurate photovoltaic defect detection. Based on the classical YOLO-v5 algorithm, the attention mechanism and bidirectional feature pyramid network were adopted to improve the accuracy of defect detection. Then, the lightweight module GhostConv and the Gaussian error linear unit activation function were used to reduce the number of model parameters and improve the reasoning speed. Further, the defect dataset of electroluminescence images proposed by the 35th European Photovoltaic Solar Energy Conference and Exhibition was used to verify the effectiveness of the proposed method. The experimental results show that YOLOv5s-GBC is superior to the original method in many evaluation indices, i.e., the accuracy and inference speed were increased by 2% and 20.3%, respectively. In conclusion, YOLOv5s-GBC exhibited better performance compared to other deep learning methods.
DBCU-Net: deep learning approach for segmentation of coronary angiography images

Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.
Fabry–Perot Cavity Control for Tunable Raman Scattering

The Fabry-Perot (FP) resonator is an intuitive and versatile optical structure owing to its uniqueness in light-matter interactions, yielding resonance with a wide range of wavelengths as it couples with photonic materials encapsulated in a dielectric cavity. Leveraging the FP resonator for molecular detection, a simple geometry of the metal-dielectric-metal structure is demonstrated to allow tuning of the enhancement factors (EFs) of surface-enhanced Raman scattering (SERS). The optimum near-field EF from randomly dispersed gold nano-gaps and dynamic modulation of the far-field SERS EF by varying the optical resonance of the FP etalon are systematically investigated by performing computational and experimental analyses. The proposed strategy of combining plasmonic nanostructures with FP etalons clearly reveals wavelength matching of FP resonance to excitation and scattering wavelengths plays a key role in determining the magnitude of the SERS EF. Finally, the optimum near-field generating optical structure with controlled dielectric cavity is suggested for a tunable SERS platform, and its dynamic SERS switching performance is confirmed by demonstrating information encryption through liquid immersion.
The black box problem revisited. Real and imaginary challenges for automated legal decision making

Abstract This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.
Digitalisation in accounting: a systematic literature review of activities and implications for competences

The digitalisation of processes is a current topic in accounting. New technologies can change activities which in turn may require different skills from accounting graduates. This paper aims to shed light on the changes that digitalisation brings about in various areas of accounting by assessing the types of activities (non-routine and routine) and corresponding competences in the context of progressing stages of digitalisation. In addition, it is analysed how different technologies are used in these activities and where their execution is placed within the supply chain. The systematic literature review shows a lack of expertise in the field of digitalisation that enables graduates and employees to successfully manage respective processes in the workplace. While routine activities are continuously being automated or digitalised, non-routine activities and the corresponding skills have a similarly increasing importance for employees in accounting as the acquisition of general digital competences.
Highway Traffic Surveillance Over UAV Dataset via Blob Detection and Histogram of Gradient

Systems for traffic monitoring often rely on local platforms. Aerial images provide the flexibility to sense the vehicle location and movements with appropriate resolution over a broader area via mobile platforms. This research study presents a method to monitor traffic flow effectively using aerial images. A novel dataset was used to evaluate the research study. In total 100 images were used, and further each image was divided into a burst of three images. The detection is being done on the first image of each burst whereas the other two images were used for tracking. Vehicle detection mainly depends on blob detection techniques with dynamic thresholding methods. To track vehicles, a shape model is generated for each of the detected vehicles. The model is used for template matching to locate all possible positions of the vehicle. The tracking results are improved by selecting only those matches having the direction and magnitude above a threshold. The accuracy of the detection algorithm in terms of correctness and completeness is 86% and 79% respectively. Through geometric computation, 73.4% of vehicles' shape model was correctly estimated. Further, the estimated shape models were used for tracking which tracked 74.9% of vehicles correctly.
Vehicle Detection and Tracking Using Kalman Filter Over Aerial Images

For intelligent transportation systems (ITSs) and planning that makes use of exact location intelligence, accurate vehicle classification, and tracking are topics that are becoming more and more vital. This paper presents a model for the detection and tracking of vehicles in roundabout aerial images. The detection is being done using a combination of blob detection and improved occlusion handling technique based on geometrical points of the vehicle model. The detected vehicles are assigned ID based on IoU matching, similarity matching, and centroid of the vehicle bounding box. The moving cars are then passed onto the tracking algorithm which implements the Kalman filter and vehicle re-identification methods. The trajectories of each detected vehicle are derived. The preciseness of the detection and tracking algorithms are 87% and 90% respectively. The experimental findings showed that the proposed detection and tracking model had consistent results for complex environments having heavy traffic flow conditions.
Spatio-temporal intention learning for recommendation of next point-of-interest

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An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach to completing complex MSR tasks, especially when the environment is dangerous and changeable. However, how to effectively plan paths for multi-UAVs under severe weather, e.g., to rescue the most urgent targets in the shortest time, is a challenging task. In this study, an improved NSGA-II based on multi-task optimization (INSGA-II-MTO) is proposed to plan paths for multi-UAVs in the MSR tasks. In the INSGA-II-MTO, a novel population initialization method is proposed to improve the diversity of an initial population. Further, two tasks are introduced during the execution of the search algorithm. Namely, one assistant task, which solves a simplified MSR problem through multi-task optimization, is implemented to provide necessary evolutional knowledge to a main task that solves an original MSR problem. The performance of the proposed INSGA-II-MTO is compared with other competitors in three MSR scenarios. Experimental results indicate that the proposed algorithm performs best among the compared ones. It is observed that the INSGA-II-MTO can find a set of shorter total paths and handle the most urgent task in the shortest possible time. Therefore, the proposed method is an effective and promising approach to solving multi-UAVs MSR problems to reduce human causalities and property losses.
An Integrated Method for the Vibroacoustic Evaluation of a Carbon Fiber Bouzouki

An integrated method, which combines Electronic Speckle Pattern Interferometry, impulse response measurements, finite element method simulations, and psychoacoustic tests, is proposed to evaluate the vibroacoustic behavior of a carbon fiber bouzouki. Three of the carbon fiber instruments are manufactured, and one is qualified via interferometric experimental measurements with reference to a traditional wooden bouzouki, which was evaluated for its sound and playability by the proposed method. Psychoacoustic tests were used to evaluate the sound and playability of the newly qualified carbon fiber bouzouki, which was further modeled by the finite element method and simulated. The simulation results agreed well with the experimental measurements. Furthermore, finite element simulation results of the qualified carbon fiber bouzouki were demonstrated with reference to the traditional wooden bouzouki experimental results, providing new findings crucial for the optimization of the manufacturing and the vibroacoustic behavior of the carbon fiber instrument. The proposed integrated method can be applied to a variety of carbon fiber stringed musical instruments.
Mapping the Emergent Trends in Industrial Augmented Reality

Augmented reality (AR) is a rapidly developing technology with the potential to revolutionize various sectors of industry by integrating digital information with the real world. This paper presents an overview of the emergent trends in industrial augmented reality (IAR) over the past five years. The study utilizes a comprehensive literature review analysis of industrial studies (searched on two scientific databases: Scopus and Clarivate Web of Science) to map the evolution of IAR trends from 2018 to 2022. The results revealed ten trending topics of AR application: Industry 4.0, artificial intelligence, smart manufacturing, industrial robots, digital twin, assembly, Internet of Things, visualization, maintenance, and training. Each topic is discussed in detail, providing insight into existing applications and research trends for each application field.
2s-GATCN: Two-Stream Graph Attentional Convolutional Networks for Skeleton-Based Action Recognition

As human actions can be characterized by the trajectories of skeleton joints, skeleton-based action recognition techniques have gained increasing attention in the field of intelligent recognition and behavior analysis. With the emergence of large datasets, graph convolutional network (GCN) approaches have been widely applied for skeleton-based action recognition and have achieved remarkable performances. In this paper, a novel GCN-based approach is proposed by introducing a convolutional block attention module (CBAM)-based graph attention block to compute the semantic correlations between any two vertices. By considering semantic correlations, our model can effectively identify the most discriminative vertex connections associated with specific actions, even when the two vertices are physically unconnected. Experimental results demonstrate that the proposed model is effective and outperforms existing methods.
A Neural Topic Modeling Study Integrating SBERT and Data Augmentation

Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in short texts, the quality of the topics obtained by the models is low and incoherent, which is caused by the reduced word frequency (insufficient word co-occurrence) in short texts compared to long texts. To address these issues, we propose a neural topic model based on SBERT and data augmentation. First, our proposed easy data augmentation (EDA) method with keyword combination helps overcome the sparsity problem in short texts. Then, the attention mechanism is used to focus on keywords related to the topic and reduce the impact of noise words. Next, the SBERT model is trained on a large and diverse dataset, which can generate high-quality semantic information vectors for short texts. Finally, we perform feature fusion on the augmented data that have been weighted by an attention mechanism with the high-quality semantic information obtained. Then, the fused features are input into a neural topic model to obtain high-quality topics. The experimental results on an English public dataset show that our model generates high-quality topics, with the average scores improving by 2.5% for topic coherence and 1.2% for topic diversity compared to the baseline model.
Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm

The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is used to maintain the vacuum chamber inner components damaged by heat loads, electromagnetic fields, and nuclear radiation. The CMOR is a redundant robot whose configuration does not meet the condition of the Pieper criterion. Redundant kinematic problems can only be solved by numerical methods, not analytical methods. Conventional numerical iterative methods include considerable computational load, accumulated errors, and the Jacobian matrix's singularity. To solve redundant inverse kinematics efficiently, we compared the behaviors of 25 versions of particle swarm optimization (PSO) algorithms with 12 one-dimensional chaotic maps under unimodal and multimodal test functions. Moreover, we selected three chaos-driven PSO algorithms with optimal convergence performance to address CMOR inverse kinematics. The experimental results indicated that chaos-driven PSO algorithms have higher computational efficiency and can effectively improve the speed and accuracy of algorithm convergence. This proposed algorithm delivers a novel and efficient method for inverse kinematics of redundant robots based on chaotic maps.
SAR-to-optical image translation using multi-stream deep ResCNN of information reconstruction

SAR image is a high-resolution radar image with ground penetration capability. However, SAR has been affected by signal noise and image distortion, which are not conducive to human eye recognition. To obtain high quality optical images, we study the SAR-to-Optical (S2O) image translation techniques. In this paper, we first propose a S2O image translation method using multi-stream deep ResCNN of information reconstruction, called MDRIR. First, MDRIR constructs two network branches, namely gradient path and contrast path, for obtaining rich multidimensional image features to reduce the effects of distortion and noise in SAR images. Second, inspired by the information fusion, MDRIR design a reconstruction network for optical images, which is parallel to the path network. Third, we design the perceptual feature loss function using the reconstruction information. It can correct the parameters of the path network and make the generated results closer to Ground truth image. The experimental results show that the superiority of our method over the state-of-the-arts in subjective and objective evaluation metrics.