Reviewerly/exHarmony huggingface dataset:
CGS-Net:Classification-guided Segmentation Network for Improved Gland Segmentation

The diagnosis of colorectal cancer depends on the analysis of pathological images, and it is of great sig-nificance to accurately segment the shape of glands in pathological images. Accurate gland segmentation is extremely challenging, such as adhesion between adjacent glands, huge morphological differences between benign and malignant glands and so on. This paper proposes a classification-guided segmentation network (CGS-NET), which uses the char-acteristics related to benign and malignant glands after classification to improve the segmentation accuracy of glands. A local feature attention module and a multi-feature fusion module are introduced to enhance the encoder features and fuse the outputs of different scales, respectively. Experiments show that the proposed method can segment different types of glands well, and its performance on the 2015 MICCAI Gland Challenge is better than some existing methods compared in this paper.
Prediction for Power Supply Area Interrupted by Heavy Rainfall in Guangxi Power Grid

In view of the problem of flood disaster caused by heavy rainfall, which affected power supply reliability, a dynamic prediction approach for power supply area interrupted by heavy rainfall is proposed. First, under the spatial correlation rules, the power supply area within the surface rainfall boundary is judged by multidimensional data, such as digital geography. Secondly, with the distribution transformer of the power supply area as the geometric center, the area and the scale of power outage users are solved. Thirdly, the supervised learning algorithm is used to predict the disaster area out of service, on the basis of the binary classification of prior data. Finally, the dynamic changes of the predicted results are calculated according to the intensity changes of the rainfall process. Using the confusion matrix and the receiver operation characteristic curve to verify the prediction method. The numerical results show that this approach can dynamically predict the power supply area interrupted by heavy rainfall, which can be used for the production command in the process of “water rise and power failure, water back and power recovery”.
A Model for Predicting the Level of Motion Sickness Caused by Virtual Reality Content by Using Attention and Effective Visual Field Mechanism

To ensure the security of viewing virtual reality (VR) content is an urgent problem at present. VR-related diseases may occur at the time of watching immersive VR content and usually cause serious discomfort. To solve this problem, it is essential to accurately evaluate the severity of VR disease. In this study, human attention and effective visual field mechanism are applied to propose a novel model intended to predict the level of motion sickness caused by virtual reality content. This model starts by learning the characteristics of information in consideration of human vision and simulating the learning process when VR content is accepted. The accelerated and rotating motion patterns are the key factors to induce a series of VR diseases. The proposed prediction model consists of two parts, namely, virtual content learning reconstructor and disease score predictor. Firstly, by training the learner with ordinary video for the tolerance of ordinary motion sickness, the difference between the original information and the generated information can be used to indicate the severity of VR disease caused by VR video. In the score prediction of virtual reality disease, it is mapped to the subjective score through the generated information difference. To evaluate the performance of the predictor, the datasets composed of several full-motion videos and subjective questionnaires corresponding to subjective evaluation experiments are collected. According to the research results, the proposed model is applicable to accurately predict the level of motion sickness caused by virtual reality content, and the prediction score is closely correlated with the subjective questionnaire.
Chinese Traditional Wheelbarrow Restoration and Game Design Based on Virtual Reality Technology

The digital protection of the cultural heritage of traditional agriculture and water conservancy equipment, as well as the integration and dissemination of culture and tourism, are important issues facing the relevant academic circles at present. Taking the representative Chinese traditional wheelbarrow as the research object, this paper proposes a cultural heritage protection and inheritance path through historical research, design analysis and virtual reality technology development. That is, the shape, function, material, technology and principle of the artifacts are verified by historical methods, and the original appearance is restored by combining 3D digital scanning and modeling. On this basis, the immersive interactive experience game platform is built by virtual reality engine, so as to realize the effective practical exploration of interdisciplinary digital protection of cultural heritage and the integrated transmission of culture and tourism
Multi-Attribute Decision Web Service Selection based on Optimal Ant Colony Algorithm

This paper focuses on issues related to the selection of Web services in multi-attribute states. With the rapid development of the Internet, how to choose a quality service for users under the constraints of multi-attribute factors is a question worth thinking about. To select the optimum combination of services for the user among a large number of multi-attribute services is our goal. User requirements can be viewed as a workflow, and a complete requirements workflow contains multiple tasks. Each task consists of different services, and different tasks are used to satisfy different user requirements. How to select the optimal services from each task and combine them to form a complete service portfolio is the work we need to accomplish. In this work, we optimize the candidate service attributes and calculate the attribute weights using a weighted arithmetic mean operator, and then select the optimal service combination using an optimal ant colony algorithm. Compared with the original method in the service selection process, MDM based on OACO Algorithm can solve the multi-attribute service selection problem, while the service data after the weight calculation avoids the constraints of multiple attributes on the service selection results. The complexity of the algorithm traversal is greatly decreases by using the Cartesian product idea to group the services based on the same attribute values and then using the ant colony algorithm. For the experimental results, the multi-attribute optimization based on the optimized ant colony algorithm has significant benefits over the original algorithm in terms of time performance.
IQR-MAD-Based Anomaly Detection of Voltage Data in the Distribution Network

Due to the influence of the natural environment, measuring instruments and other factors, the anomalous measurement data will lead the dispatchers to make wrong decisions, threatening the safe and stable operation of the power system. To improve the speed and accuracy of identifying anomalous voltage data of distribution network voltage monitoring points, and avoid the influence of anomalous values on statistical parameters (mean value and standard deviation), a two-layer progressive anomaly detection method for distribution network voltage data based on interquartile range (IQR) and mean absolute deviation (MAD) is proposed. In the first layer, the voltage data collected by the distribution network voltage monitoring system is firstly filtered with zero value and null value, and then the significant outliers are detected according to the IQR criterion to obtain the data set to be further detected. The second layer proposes the MAD-based Hampel algorithm as an outlier evaluation criterion, and the anomaly judgment coefficient can be adjusted according to the needs of power grid operators. Finally, in order to evaluate the proposed method, two real distribution network voltage data are taken as examples to compare traditional statistics-based anomaly detection algorithms, such as the Three Sigma Rule and IQR, through simulation. The results show that the proposed algorithm has a higher accuracy rate than the comparison algorithm.
3D Human Pose Estimation via Graph Extended Spatio-Temporal Convolutional Network

3D human pose estimation is an important premise for human behavior analysis and understanding, which has a wide range of applications in intelligent transportation, human-computer interaction, and animation production. Most existing works focus on extracting the feature relationship between frames by combining spatio-temporal information to reduce the error of attitude reconstruction. However, the majority of them often suffer from insufficient joint correlation characteristics. To address this problem, we propose a Graph Expand Spatiotemporal Convolutional Network, named GESC-Net, to improve the limitation of extracting human spatial structure features. To better enrich the feature of extracting local information, we develop a learnable symmetric connection (LSC) block in the spatial structure. Moreover, a CbAttantion block is also designed to obtain a larger view of the acquisition of global structure and get more effective features. We evaluate our approach on two standard benchmark datasets: Human3.6M and HumanEva-I. The quantitative and qualitative evaluation results demonstrate that the GESC-Net can achieve better 3D human posture estimation than existing state-of-the-art methods.
PSA-CLNet: Pedestrian Search Method Based on Polarized Self-Attention and COIM Loss

Pedestrian search is a combined task of pedestrian detection and pedestrian re-identification, i.e., how to find a specific pedestrian in a panoramic picture. However, due to the impact of different imaging conditions and environments, the accuracy of pedestrian search is still low at present, which is difficult to meet the needs of practical applications. To improve the accuracy of pedestrian search, this paper proposes PSA-CLNet pedestrian search method, which integrates polarized self-attention mechanism (PSA) and cosine online instance matching (COIM) loss. Firstly, the channel attention is added to enhance the pedestrian feature information and improve the expression ability of the feature information. Secondly, spatial attention is added to enhance the areas that have more contribution to the pedestrian search task to increase the correlation between the pictures and improve the recognition ability. Meanwhile, this paper proposes a COIM loss based on the online instance matching (OIM) loss. The loss function can enlarge the class distance between pedestrians and background, and reduce the class distance between pedestrians. This could improve the accuracy of pedestrian search algorithm effectively. Finally, this paper uses ResNet as the main backbone network to verify the proposed method. The pedestrian search method proposed in this paper could achieve 96.60% of mAP index and 96.83% of top-1 index in CUHK-SYSU dataset, and achieve 48.78% of mAP index and 88.38% of top-1 index in PRW dataset.
Fault Detection Method of Infrared Image for Circulating Pump Motor in Valve Cooling System Based on Improved YOLOv3

Timely maintenance of the key equipment in valve cooling system plays an important part in maintaining stable operation of a flexible DC converter station. In order to accurately locate and recognize the defects from infrared images of a motor of circulating pump, a motor fault detection method based on improved YOLOv3 is proposed in this paper. First, an improved Multi-Scale Retinex with Chromaticity Preservation (MSRCP) image enhancement algorithm based on Y component is proposed to increase the infrared image contrast, which makes the target more prominent. Then the convolutional block attention module (CBAM) is applied to feature pyramid network (FPN) to improve the YOLOv3 network. In order to improve the accuracy of model as much as possible, various kinds of training strategies are employed, which include mosaic data augmentation, mixup data augmentation, label smoothing, exponential moving average (EMA) and transfer learning. Finally, comparative experiments are carried out to test the effectiveness of the employed methods. The experiment results show that the network improvement methods could effectively increase the detection accuracy of the model. The mean of average precision (mAP) of the final model reaches 96.09%, and the average fault detection accuracy improves to 94.98%. The detection speed of the improved model can reach 42 frames per second (FPS), which meets the real-time monitoring requirements of the valve cooling system equipment.
Towards a VR Moot Court for Law Students in Enhancing Practice Experience

Law students (Undergraduate) usually use a moot court on-campus to practice individual legal skills before they start their careers. The study addresses the limited availability and high cost of moot court facilities for law students in Guangdong’s universities. The study proposes utilizing virtual reality (VR) technology to solve the current issue. The VR solution aims to enhance the students’ legal skills through a virtual moot court system designed to mimic a real-life scenario. The system comprises a Case Study Mode that enables law students to practice classic cases and a Visiting Mode that allows non-law students to explore the VR moot court and acquire basic legal knowledge. Through an interactive and seamless learning experience, this study demonstrates the scalability and potential of VR technology in online education for law students. The design of this system was based on serious gaming theory and previous VR educational projects, including those outside the legal field. The results suggest that VR technology has the potential to benefit practical courses by attracting students through its realistic graphics and interactive elements.
A robust variable projection algorithm for RBF-AR model

The radial basis function network-based autoregressive (RBF-AR) model is a powerful statistical model which can be expressed as a linear combination of nonlinear functions and frequently appears in a wide range of application fields. Variable projection algorithm is designed for solving smooth separable optimization problems with least squares form and has been used as an efficient tool for the identification of RBF-AR model. However, in real applications, the observations are usually disturbed by non-Gaussian noise or contain outliers. This often leads to nonlinear regression problems. Since there are both linear and nonlinear parameters in such problems, how to optimize such models is still challenging. In this paper, we design a robust variable projection algorithm for the identification of RBF-AR model. The proposed method takes into account the coupling of the linear and nonlinear parameters of RBF-AR model, which eliminates the linear parameters by solving a linear programming and optimizes the reduced function that only contains nonlinear parameters. Numerical results on RBF-AR model to synthetic data and real-world data confirm the effectiveness of the proposed algorithm.
Use of Mixed Reality in HVAC System Equipment Fault Detection and Diagnosis Method

In the traditional building HVAC system equipment operation and maintenance process, some of the HVAC system equipment installation locations are hidden in the building space, so many HVAC equipment repair and maintenance operations have limited maintenance space and low visualization, and the building air conditioning system fault diagnosis process is often plagued by on-site work collaboration factors resulting in low fault repair efficiency. Based on the above problems, this paper proposes to superimpose Mixed Reality (MR) technology on top of the Building Information Model (BIM) to develop a BIM+MR-based building HVAC system equipment fault diagnosis system to improve the immersive and remote visualization interaction capability in the HVAC system equipment maintenance process through the digital twin technology. The BIM+MR fault diagnosis system is developed to improve the immersive and remote visualization interaction capability in the HVAC system equipment maintenance process using digital twin technology. The technical verification shows that the efficiency of HVAC’s air conditioning equipment fault diagnosis in the pilot project has increased by 1.88 times after applying MR technology.
Graph Context Target Attention Graph Neural Network for Session-based Recommendation

Session-based recommendation is nowadays increasingly popular in e-commerce, aiming at predicting the next action of a user under anonymous sessions. Previous research methods on session recommendation model the temporal information inherent in a session as a sequence or graph, however, they disregard the session's graph context information, as well as the relationship between the user and the target object, which affects the accuracy of the recommendation. To obtain the rich graph context information in session recommendation and the intrinsic connection between target items and users, we propose a graph context target attention graph neural network for session-based recommendation, which uses a self-attentive network and graph neural network to extract the item embedding of graph context information; the target attention then adaptively stimulates various user interests. Experimental results on two real-world datasets demonstrate that our proposed model outperforms other comparison algorithms on the evaluation metrics of Recall@20 and MRR@20 in session-based recommendation.
Using Augmented Reality to Enhance Learning and Understanding of Abstract Programming Concepts

This paper presents the implementation of an Augmented Reality (AR) application in order to enhance the understanding of Python collection data types. AR is a technology which has gained popularity in recent years. This technology has the potential of completely transforming how we learn, work and think. In this context, the importance of this technology has increased visibly in terms of engineering education. One of the challenges encountered is that students often struggle with abstract programming concepts and find programming hard to conceptualise. More specifically, first-year electrical and electronic engineering students may have difficulty in understanding and learning Python collection data types (List, Tuple and Dictionary) and their main differences. AR is one of the solutions to solve this problem. An AR application has been implemented by making this software concept more visible and easier to comprehend through AR and 3D visualisation. A user study was defined in which two groups were respectively provided the AR application and the same information contained in a printed booklet. Comprehension tests on the subject before and after they used the learning resource were used to gauge how effective each intervention was. The t-test method was used to analyse the user study results. It was concluded that using the AR application (post-test score mean: 8.8) rather than reading the booklet (post-test score mean: 7.5) resulted in a higher test score mean.
Outlier Detection of Traction Energy Consumption Based on Local Density and Cluster for Time Series Data

Traction energy consumption accounts for 40%-50% of the total energy consumption of urban rail transit. Outlier detection of traction energy consumption data is the key technology of traction energy consumption fluctuation analysis. To accurately detect abnormal traction energy consumption, this paper first proposes a calculation method of the typical value of traction energy consumption indicator based on the combination of the ARIMA model and XGBoost algorithm. Its core idea is to extract residual item information based on the XGBoost algorithm, and then integrate all information for modeling; Then, based on the calculated typical values, this paper uses an incremental local density and cluster-based outlier factor (iLDCBOF) method to detect outliers. The experimental results show that the prediction effect of the ARIMA+XGBoost hybrid model is better than that of a single model. The proposed method can effectively detect abnormal energy consumption values in data streams.
A New Text Mining Method of Dispatching Operation Ticket System Based on Graph Partition Spectral Clustering Algorithm

The dispatch operation ticket system contains much real-time information about the power system. To make this information available for better dispatch control for power systems, a new text mining method of dispatching operation ticket system is constructed in this paper, which is based on a graph partition spectral clustering algorithm. According to the content of order tickets, they are divided into different types, and the same type of order tickets are stored in a group. The relationship between each group is modeled and analyzed, and the bipartite graph is introduced to achieve a good clustering effect among heterogeneous objects. The Laplacian matrix in the spectral clustering algorithm is used for the Graph Segmentation, and the matrix feature vectors and data point set are obtained, completed by dimension feature data set. After that, the clustering result is obtained, which represents the result of dispatching operation ticket checking text mining used for the real-time power system dispatching. The simulation results show that the proposed method can accurately mine the operation content of the power system overhaul and repair management process, which is efficient and convenient to meet the regulation needs of power system operation.
Detecting Zero-Shot Human-Object Interaction with Visual-Text Modeling

Most existing Human-Object Interaction (HOI) detection methods focus on supervised learning, but labeling all interactions is costly because of the enormous possible combinations of objects and verbs. Zero-shot HOI detection emerges as a promising approach to address this problem but encounters challenges when facing unseen interactions. To this end, we propose a novel two-stage Visual-Text modeling HOI detection (VT-HOI) method which can effectively recognize both seen and unseen interactions. In the first stage, the features of the humans and the objects are extracted by DETR and concatenated as the query sequences. In the second stage, local and global memory features from the Visual Encoder are fused into the corresponding query sequences by our proposed Semantic Representation Decoder with the cross-attention mechanism. Then we perform cosine similarity computation between visual features and text features, which are extracted or label-generated by Visual Representation Head (VRH) and Text Feature Memory (TFM) module respectively. Finally, the similarity matrix is fused with the results of the classification head for training or inference. The comprehensive experiments conducted on HICO-DET datasets demonstrate that the proposed VT-HOI significantly outperforms the state-of-the-art methods.
Industrial Time Series Prediction Based on Incremental DBSCAN-KNN with Self-learning Scheme

Industrial time series data are usually time-varying due to multiple factors such as environmental and human disturbances. As traditional time series predicting methods are often based on offline training ignoring the changes in working conditions, the prediction results may be inaccurate. In this paper, a time series prediction model based on incremental DBSCAN and KNN with self-learning scheme is proposed to address the problem of time-varying working conditions. The proposed model uses the incremental DBSCAN to automatically identify and expand working conditions with adjusting the number of clusters automatically, and then employs the KNN model to make predictions under different working conditions. Compared with the existing methods, the proposed method is more stable and improves the prediction accuracies of the model under different working conditions.