Reviewerly/exHarmony huggingface dataset:
Data Fusion-based graph representation learning for brain disease recognition

Self-supervised graph learning has attracted intensive attention due to attempts to eliminate the need for expensive data labeling, especially graph contrastive learning. However, the existing graph contrastive learning methods rely heavily on selecting negative samples or symmetric network structures. To overcome these limitations, we propose a Data Fusion framework based on Graph Representation Learning for brain disease recognition (DF-GRL), which integrates structure and attribute information extracted from an asymmetric dual branch network. To explore the structure representation of attribute embedding, we sample neighbor node features of attribute embedding. We apply a cross-correlation-based loss function to constrain the structural consistency of structural embedding and neighbor embedding representations. With state-of-the-art graph representation learning methods, extensive experiments on seven brain functional connectivity data demonstrate the effectiveness and efficiency of our method.
Investigating Requirements and Expectations of Wearable Telexistence Robotic Systems

Telexistence refers to various technologies that enable a high sense of embodiment and interaction capabilities with remote environments. Although numerous telexistence systems have been explored in previous works of literature, no prior studies have investigated the usability of telexistence systems for daily use. Therefore, this study focuses on Piton, which is a novel wearable telexistence robot which has a high degree of freedom in a snake-shaped design. Piton offers a unique opportunity for remote interaction and manipulation in diverse environments, owing to its flexible movement, which enables users to adapt to various scenarios with ease. The robot's wearability is also an essential feature that allows the user to wear it as a backpack. However, despite its promising features, deeper investigation is necessary to explore potential use cases for Piton in users' daily lives and the challenge of Piton.
Compression for the Pointwise Convolution - Feature Fusion

In this paper, we have proposed a method to measure the weight efficiency of pointwise convolution (weight sharing index), which finds that the weight efficiency of pointwise convolution decreases rapidly as the network becomes deeper. The redundancy of the feature maps is mainly reflected in the similarity of the feature maps. We achieve the compression of the pointwise convolution layers by quantifying the similar feature maps with the help of structural similarity (SSIM), reducing the input channels of the pointwise convolution by fusing the similar feature maps. The results on the datasets CIFAR-10 and CIFAR-100 demonstrate that the compression method based on fusing similar feature maps can significantly compress the MobileNet-V1(48.74% FLOPs and 13.64% parameters are reduced) with an increase of 0.54% in the top-1 accuracy, improving the top-1 accuracy of ResNet-50 by 0.09% (9.2% of FLOPs and 6.03% of parameters are reduced), and improves the efficiency of the weight utilization of the pointwise convolution layer.
Traffic Sign Detection and Recognition Based on An Improved YOLOv5

Intelligent driving requires the perception of the surrounding environment and the recognition of road signs to make informed driving decisions. Accurate and real-time traffic sign recognition is a crucial component of this process. Addressing this challenge, this study focuses on utilizing the TT100K dataset and employing lightweight YOLOv5s training, along with data augmentation techniques, to enhance data diversity. The resultant model achieves an average precision of mAP@0.5 reaching 0.848 on the PC platform, with a detection speed of 4ms on an RTX 4090. For localized deployment, a PC GUI application has been developed, offering functionalities such as model loading, image and video recognition, and real-time camera-based recognition.Considering the small-scale nature of traffic signs in driving scenarios, an improvement is proposed based on multiple comparative experiments. This enhancement involves computing new anchor boxes using K-means and adopting the Weighted IoU (WIoU) as a loss function. Furthermore, a Channel Attention (CA) mechanism is incorporated above the Spatial Pyramid Pooling Fusion (SPPF) layer of the backbone network to enhance the accuracy of detecting small-scale targets. Experimental results demonstrate that the improved YOLOv5s algorithm achieves an mAP@0.5 of 0.868, while maintaining the same detection speed.
Asymmetric Network based Dilated Convolution Transformer for Single Image Deraining

The recent dramatic progress of transformer-based methods over convolutional neural networks (CNNs) for single image deraining is attributed to the transformer's powerful ability to model non-local messages. Indeed, rich local-global information characterization is equally important to better satisfy the derivation requirements. In this paper, we present an efficient image deraining method that integrates CNN models into the Transformer backbone to accelerate network convergence, termed Asymmetric network based Dilated convolution Transformer (ADT), which leverages Transformer's ability to learn non-local features and seamlessly integrates local detail efficiency and global structural representations. Our framework is an asymmetric architecture because the encoder focuses on shallow features contains rain shadow features, the decoder focuses on deep features, and embedding Dilconv FeedForward Networks (DFNs) within its encoder, and Dilconv self attention (DSAs) and DFNs within the decoder preserves higher similarity, resulting in high-quality image deraining. Extensive evaluation results show that our model performs superiorly and significantly improves the quality of image deraining.
Improved Cascade Mask RCNN Single Character Detection Algorithm for Cast Pipe

Due to the interference of blurry background, character occlusion, and low background contrast in the Cast Pipe character images, the detection accuracy and real-time performance cannot meet the requirements of industrial scenes. To address these issues, this paper presents an improvement based on the Cascade Mask RCNN. Firstly, the Channel Attention Mechanism Module (Squeeze-and-Excitation, SE) is embedded into the ResNet structure. By constructing a bottleneck structure with two fully connected layers, the normalized weights are applied to each channel feature to enhance the network representation ability. Secondly, a bottom-up structure is introduced after the Feature Pyramid Networks (FPN) to transmit the strong localization information from lower-level features to higher-level semantic features. Thirdly, an adaptive pooling layer is added to allocate the candidate regions generated by the Region Proposal Network (RPN) to different scale feature maps. Additionally, skip connections are introduced between the bottom-level features and the highest-level features of each stage to reduce model parameters while retaining global representation capabilities. The improved algorithm is tested on a cast pipe dataset, achieving an average detection accuracy (mAP) of 98.7%, which is 2.7% higher than that of Cascade Mask RCNN. The accuracy indicates that the improved performance is superior to the original algorithm.
A Convolutional Neural Network based Method for Masked Face Detection

After the outbreak of COVID-19, wearing masks has become increasingly popular in people’s daily lives for keeping social distance. However, due to the occlusion of the mask, it makes some difficulties for the camera-based face detection applications in public environments. As a result, we propose a convolutional neural network-based method for masked face detection. This method firstly extracts and fuses the features of masked and unmasked faces using a CNN network. Then use a residual structure to pay more attention on the faces in the fused features which can improve the accuracy of masked face detection. We have conducted multiple experiments on multiple common benchmarks to verify the performance and effectiveness of our proposed method, which has reached a high accuracy in detection results.
Deep CNN- RNN with Self- Attention Model for Electric IoT Traffic Classification

Network traffic classification is crucial for network monitoring and analysis, including legitimate interception, quality of service assurance, application bottleneck prevention, and malicious behavior identification. Currently, the three main approaches for traffic classification are based on ports, payloads, and statistical features, i.e. machine learning. In the Electric Power Internet of Things domain, Modbus and MQTT are essential communication protocols. Modbus is widely used in industrial automation, while MQTT is designed for IoT ap- plications and offers faster response time, greater throughput, lower battery and bandwidth usage, and the ability to operate over low-speed network connections. This study focuses on network traffic classification in the context of power IoT. By simulating the IoT environment and capturing packets using Modbus and MQTT protocol communication, traffic data is processed into a pseudo-image format. A network architecture based on CNN-RNN with a self-attention mechanism is proposed to classify network traffic data and achieve higher classification accuracy. The Self-Attention module enables adaptive focus on different regions and feature capturing. Extensive experiments with multiple models and datasets demonstrate that the CNN- RNN model can achieve an accuracy of approximately 95%. Furthermore, the Self-Attention mechanism improves the CNN- RNN model's convergence speed, enhancing its training speed.
Multi-level Feature Aggregation Network for Person Re-identification

Current person re-identification works focus on the deep features, ignoring the loss of detailed features due to downsampling operations. We propose a parallel structured Multi-level Feature Aggregation network (MFA-Net), which not only aggregates the information of local features within the same level as that of global features but also aggregates the features between different levels in an orderly manner as a way to mine the overlooked features. Furthermore, experiments on three datasets Market1501, DukeMTMC-RelD and MSMT17, show that our MFA-Net can further mine the detailed features, enhance the feature representation, and achieve state-of-the-art results on three benchmark datasets.
A clustering algorithm of network short video based on text and video model

With the rapid development of video and network technology, the number of social short videos has increased significantly, and the traditional media processing methods cannot meet the actual application needs. To solve the problem that it is difficult to cluster social short videos effectively, a new idea of media fusion method, which is called a clustering algorithm of network short video, is proposed based on text and video model. Firstly, the text information of the video including the video title, related query words and total click video are extracted to construct the model. Then, according to the characteristics of short video, the video representation model is constructed to obtain the video content information. Finally, text information and video representation model are fused to achieve short video clustering.
Machine Learning. Correlational Convolution Method for Image Classification

When classifying images, in order to reduce the dimension of the feature space, it is necessary to convolve images to select a subset of pixels sufficient to solve the problem. An image convolution method based on the correlation of the pixels of images of the training sample with a teacher with an approximated function used for classification is proposed. An example of image convolution used to classify handwritten digits by evaluating the posterior probabilities of ten image classes using Anderson's discriminant function is given.
Models of Battle Dynamics in Strategic Computer Games

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game.
Fault Diagnosis Method for Flight Control System with small samples Based on Triplet Network

In view of the characteristics of modern flight control system with few fault data and many fault types, it is difficult to rely on deep learning for accurate fault diagnosis. In this article, a fault diagnosis method of flight control system based on Triplet Network was proposed and applied to the bearing data set of Case Western Reserve University and the Pre-flight Built-in-test(PBIT) fault data of a certain type of aircraft. For the bearing data set, the signals are firstly transferred into the frequency domain by wavelet packet decomposition. Then, build the Triplet Network model, and the network training is carried out in the form of triplet input. The model compare the similarity of characteristics of similar faults and different faults, and optimize the parameters to achieve the optimal classification effect. The PBIT fault data of flight control system is processed and network training is carried out to realize the fault diagnosis function. Aiming at small samples, I divided experimental data sets with different number of training samples to observe the diagnostic effect of the model, and compared with traditional deep learning models respectively. The experiment verified the unique advantages of Triplet Network model in fault diagnosis under small sample conditions, and the accuracy was significantly better than the traditional deep learning method.
Multi-scale Convolutional Neural Networks Based on Self-attention And Residual Network for Industrial Equipment Fault Diagnosis

Texts in industrial production contain massive amounts of faulty textual information, as traditional convolutional neural networks (CNN) have difficulties in extracting sufficient valid information in the multi-classification task of texts. A multi-scale CNN composite model based on a self-attention and an Inception residual connection module is proposed. The method preprocesses unstructured text, obtains a text vector from the input text by word embedding, extracts feature at more scales using the CNN and Inception modules, and assigns weights to the feature vectors through the self-attention so that the model can pay more attention to the key information in the text, thus improving the performance of the model in text classification tasks. Taking the industrial equipment fault data of an automotive company as an example, the proposed method outperforms the traditional model in terms of precision, recall and f1-measure evaluation indexes, and provides a new idea for the research of industrial production equipment fault diagnosis.
A UAV collaborative defense scheme driven by DDPG algorithm

The deep deterministic policy gradient (DDPG) algorithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration. Using the DDPG algorithm, agents can explore and summarize the environment to achieve autonomous decisions in the continuous state space and action space. In this paper, a cooperative defense with DDPG via swarms of unmanned aerial vehicle (UAV) is developed and validated, which has shown promising practical value in the effect of defending. We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process. The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently, meeting the requirements of a UAV swarm for non-centralization, autonomy, and promoting the intelligent development of UAVs swarm as well as the decision-making process.
Labeling Software Security Vulnerabilities

Labeling software security vulnerabilities would greatly benefit modern AI cybersecurity research. The National Vulnerability Database (NVD) partially achieves this via assignment of Common Weakness Enumeration (CWE) entries to Common Vulnerabilities and Exposures (CVE) entries. In this work, we explore utilization of the Bugs Framework (BF) formalism for systematic and comprehensive CVE labeling. We specify all memory-related CWEs via BF weaknesses and examine the suitability of these formalisms to describe the corresponding CVEs mapped by NVD. We also identify similarities and overlaps in CWEs that introduce ambiguities in NVD assignments.
Research on UAV power inspection trajectory optimization algorithm

This paper proposes a climbing trajectory optimization method based on an adaptive genetic algorithm to address the problem of slow convergence and susceptibility to local optimal solutions in the current UAV (unmanned aerial vehicle) climbing trajectory optimization algorithm. Firstly, an optimization model for the climbing trajectory segment is established based on the motion equation and performance index of the UAV climbing platform. Secondly, the adaptive genetic algorithm is improved to enhance the diversity of chromosomes and the convergence speed of the algorithm, making it more suitable for optimizing the climbing trajectory. Finally, specific optimization steps are provided according to the characteristics of the UAV climbing trajectory, and a simulation verification of the climbing trajectory of a certain type of unmanned aerial vehicle is conducted. The results indicate that this method can save operating costs to a certain extent.
A Chinese Word Segmentation Model for SCD Text in Smart Grid Station: An Attention-BiLSTM-CRF Approach

In this paper, a novel Chinese word segmentation (CWS) model, named as the optimized Attention-Bidirectional - Long Short-Term Memory-Conditional Random Field (Attention-BiLSTM-CRF), is specifically designed for substation configuration description (SCD) text data in smart grid station. In the proposed model, the Skip-Gram algorithm is utilized to convert word data into distributed vectors, then by integrating the attention mechanism, LSTM, and CRF, the new segmentation model named as Attention-BiLSTM-CRF is developed, which is more suitable for SCD text data in smart grid station. Finally, the effectiveness of the proposed approach is demonstrated through a case study based on SCD text data. Simulation results show that the proposed model can achieve a higher accuracy rate of 98.2%, and it can be observed that the proposed CWS approach had higher accuracy and speed in the field of smart grid.
A Robot Arm-based Haptic Feedback System for Augmented Reality Applications

Recent advances in Augmented and Virtual Reality show that they will play a critical role in our daily lives. However, interaction in such virtual environments is challenging as digital contents cannot be physically felt. Although numerous researchers proposed different methods to achieve haptic feedback, most implementations are cumbersome and unsuitable for daily use. Accordingly, we developed a wearable robotic appendage that can provide various types of haptic feedback in AR. Our system comprises a 4 DoF robot worn on the user's forearm and equipped with a square end-effector. We demonstrate potential applications of our system for providing haptic experiences with UI's in AR and conduct a preliminary evaluation to explore users' impressions of our robotic appendage. Overall, the system is well-received by participants, yet they indicated challenges in weight and hand-tracking. Lastly, we discuss future research directions in light of the design and evaluation of our system.
Research on Pumped Storage Capacity Allocation of Cascade Hydro-Wind-Solar-Pumped Storage Hybrid System Considering Economy and Operational Stability

Under the background of “carbon peaking and carbon neutrality” and the high proportion of wind and solar resources connected to the power grid, how to maximize the use of water resources in the southwest region to achieve the absorption of wind-solar power and the stability of fluctuations has widely attracted attention. Therefore, this paper proposes a capacity configuration method for hybrid pumped storage stations with a cascade hydro-wind-solar-pumped storage complementary system, which takes into account construction economy and running stability. Firstly, the steady-state output models of cascade hydro-wind-photovoltaic-pumped storage power stations are established considering the prediction error. Secondly, considering the investment cost recovery of pumped storage, a hybrid pumped storage capacity configuration model is constructed, which aims to maximize the daily benefit of the complementary system and minimize the peak-valley difference of the remaining load. Finally, the scenario optimization method is adopted to reduce the influence brought by the uncertainty of runoff-wind-solar. Three cascade hydropower stations and planned wind-photovoltaic power stations downstream of a basin on a typical day are taken as an example. The result shows that the peak-valley difference of the hybrid system is reduced by 1326.86MW and 35.68% with an extremely small cost compared with the system without pumped storage, which can provide theoretical support for the capacity configuration of hybrid pumped storage.
Short-Term Photovoltaic Power Prediction Based on Similar-Day Clustering and Multi-Layer Perception

Artificial intelligence plays an important role in improving the accuracy and reliability of photovoltaic power prediction. In order to better improve the accuracy of photovoltaic power prediction, present mainstream research focuses on using the optimizing algorithm to select the hyper-parameters of the prediction model to improve the prediction level of the model. This type of model uses an optimizing algorithm to achieve the improvement of prediction accuracy. However, it doesn’ t do too much work in the original data, which partly ignores the inner value of the original data. To solve that problem, an intelligent photovoltaic power prediction model based on similar-day clustering and multi-layer perceptron is established. The ability from convolutional neural network and multi-layer perception are combined. Firstly, select the satellite cloud picture and use the convolutional neural network to work at data feature clustering, which is proposed as similar-day clustering. Sequentially the original data is divided into subsets. Secondly, the pretrained multi-layer perceptron models according to different weather types are selected. The historical metrological data are used as the model input to train the multi-layer perceptron. Thus, multi-layer perceptron can learn the nonlinear mapping relationship and coupling connection between meteorological factors and photovoltaic power. Finally, a data set of a photovoltaic power plant in northern China is used for the case study. The case study shows that compared with LSTM and multi-layer perceptron, the MAPE of the proposed prediction model is improved by 19.6% and 50.7% respectively, which fully demonstrates the superiority and accuracy of the model.