Reviewerly/exHarmony huggingface dataset:
TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method.
NLP-based Generation of Ontological System Descriptions for Composition of Smart Home Devices

With the current rapid development of Internet of Things (IoT) technology and the widespread popularity of smart home devices, wireless technology has made it possible for IoT devices to integrate with each other in a complex system. Previous works have proposed utilizing ontological descriptions, called Holons, of IoT devices and subsystems to reason about the construction of systems. The holonic description, defined by an ontology, includes parameters, services, and properties of the IoT device. However, these previous works assume that Holon descriptions of IoT devices are already provided e.g., by vendors. This assumption requires device vendors and system engineers to manually create descriptions, which is time-consuming and error-prone given the increasing number of IoT devices that are offered in the market. This paper introduces a method for the automatic generation of Holon descriptions of IoT devices. This method uses the brand and model of a device and utilizes knowledge extraction in natural language processing to automatically generate the ontological description of the IoT device. The experimental results show that the proposed method can generate descriptions with a precision of 96.72% and a recall of 87.53% in a practically acceptable time.
Pattern-protecting Dynamic Searchable Symmetric Encryption Based on Differential privacy

Because it allows users to browse encrypted documents on an untrusted cloud server, searchable symmetric encryption has gotten a great deal of interest. However, based on the leakage of access and search patterns, the cloud server can infer users’ private data. Despite the fact that researchers have presented a number of strategies for protecting access or search patterns, all of them have a substantial computational and communication overhead. To that aim, this paper utilizes differential privacy technology to provide an efficient pattern-protecting dynamic searchable symmetric encryption scheme (DF-DSSE). Specifically, differential privacy’s false positives and false negatives are used to obfuscate the documents associated with each keyword, protecting access patterns. In particular, to reduce the computational and communication overhead associated by obfuscating query results, a tag symmetric encryption primitive is provided to encrypt indexes and query tokens. Furthermore, since the DF-DSSE scheme stores the token tags using the Bid Compress compression structure and accesses the documents corresponding to each keyword independently, an adversary cannot obtain the number of keywords or the frequency with which they are accessed, achieving the goal of protecting search patterns. In comparison to previous schemes, the DF-DSSE scheme enhances update and query efficiency and security, according to the security analysis and simulation experiment results.
Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Web Services

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale Web services. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve good performance, which conflicts with frequent pattern changes in Web services entities. Moreover, the training overhead of vast MTS in large-scale Web services is unacceptable. To address these issues, we design OmniTransfer, a model-agnostic framework that combines improved hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large Web content service provider show that OmniTransfer significantly reduces the model initialization time by 59.72% and the training cost by 85.01%, while maintaining high accuracy in detecting anomalies.
Research on Multi-vision Feature Matching Technology

In this paper, a multi-vision system is used to study camera calibration, target feature extraction and feature matching algorithms. First, the multi-vision system used is calibrated, and the basic composition is binocular cameras and monocular cameras, and the number can be expanded according to needs. Then, the camera is calibrated, and the internal and external parameters of each camera and the position relationship between the image coordinate system and the world coordinate system are obtained. In terms of target recognition, the feature point extraction and matching method based on SIFT algorithm is used to identify the target. In order to eliminate false matching and make the recognition result more accurate, the RANSAC algorithm is also used to eliminate the initial mismatched feature points of the SIFT algorithm, retain the accurate matching feature points, and realize the accurate identification of the target.
Research on High throughput Consensus Algorithm of Energy Blockchain based on Recurrent Neural Network Node Integrity Prediction

With the continuous improvement of the digitalization level in the field of energy and electric power, the combination of blockchain technology and business in the field of energy and electric power is getting deeper and deeper. This paper designs a blockchain consensus algorithm combined with artificial intelligence, aiming at the efficiency, security and other problems existing in the existing blockchain consensus algorithm. First, on the basis of ensuring the reliability of the blockchain system, combined with the integrity performance of the consensus node of the blockchain, combined with the recurrent neural network(RNN) to predict the credibility value of the node, screened out the reliable consensus node to participate in the consensus of the energy blockchain, and punished the malicious node. This algorithm can effectively enhance the consensus efficiency of the energy blockchain and improve the level of data privacy protection. We will further support the construction of new power systems.
A Measurement Method for Two-dimensional Characterization Unit Based on Morphological Completion Analysis

Constructing three-dimensional (3D) structures from a two-dimensional (2D) reference images is the principal way of obtaining the 3D structure of a porous medium. The performance of a 3D reconstruction and time consumption are greatly depended on the size of 2D reference image. The morphological and statistical characteristics of a 3D reconstruction will be improved while the size of a 2D reference image is increasing. At the same time, the involved data will be dramatically increased, and the computer memory is also confronted with a great challenge. In this work, a measurement method for 2D characterization unit is proposed based on morphological completion analysis. The measurement method can quantify a minimum size 2D reference image that can effectively represent the morphological character of a 3D structure. The morphological and statistical characteristics of a 3D reconstruction can not only be effectively represented, but also the involved data and computer memory are minimized when a 2D characterization unit is used. The core material was selected to verify the validity of the proposed measurement method, and the autocorrelation function and linear-path function were adopted as quantitative means. Detailed experiments show that the proposed measurement method is effective and reliable.
Attention Feature Selection Based on Prior Knowledge Injection

High-dimensional data generally appear in various analysis tasks, but high-dimensional data often causes computational waste and noise interference. Feature selection can make the model achieve better performance by preserving the optimal feature subset in the data to solve the problem of data dimensionality reduction. It is a popular method to select feature subset by combining autoencoder structure reconstruction. However, most autoencoder methods only take reconstruction as the objective function, which will lead to a decrease in the ability of feature discrimination for data with obvious clustering structure. Moreover, only decoder is used as an auxiliary structure, and the performance of the decode cannot be further released, resulting in the sub-optimal feature subset obtained. In this paper, we obtain the internal clustering matrix of the data in advance through manifold learning and spectral analysis, which we call prior knowledge. By constraining the distance between the decoder output and the prior knowledge, the model can force learning the clustering information of the data and improve the ability to identify the feature. Secondly, an attention module is designed to highlight the feature information of key locations. We prove the effectiveness of our method by experiments on several standard datasets.
Multi-model image matching based on feature map matching method

In the field of remote sensing, multimodal data matching is a problem with great challenges, and the matching between SAR and visible light images is tremendously difficult, because there is obvious speckle noise in SAR images, and the imaging methods of SAR images and optical images are very different. At present, although lots of excellent results have been fulfilled in multimodal image matching, it is difficult to meet the rapid and even real-time processing requirements of massive remote sensing data such as disaster emergency and military applications due to the complexity of the matching task. In view of the above problems, this paper proposes a feature map matching method based on the deep convolution neural network model of the pseudo-Siam architecture, which can quickly accomplish the multimodal image matching.
Video Bitrate Allocation Algorithm Based on Regions of Interest

This paper proposes a video bitrate allocation algorithm based on regions of interest (ROIs), taking into account the higher quality requirements of the ROIs as compared to the background. The algorithm is built upon the FFmpeg and X.264 frameworks and utilizes the Yolov7 detection model to extract ROIs from video frames and generate corresponding masks. The backend encoder prioritizes the bitrate allocation to the ROIs based on the generated masks, while sacrificing the bitrate allocation of the background to ensure overall bitrate stability. Experimental results, based on the comparison of QP values, file sizes, and PSNR values, demonstrate the feasibility of the algorithm, which successfully improves the bitrate allocation of ROIs to the designated value.
Long-term single-target pedestrian tracking combined with reliability discrimination and re-detection

Long-term pedestrian tracking is susceptible to reduced accuracy or even failure due to challenging factors such as occlusion and out-of-field events. To address this problem, we propose a novel long-term pedestrian tracking algorithm that combines reliability discrimination and re-detection techniques. The proposed algorithm assesses the reliability of tracking results by analyzing the response peak of correlation filtering and average peak correlation energy, which facilitates adaptive updating of the model. Additionally, we use the YOLO algorithm to detect the location of candidate pedestrian targets and utilize their response peak to determine whether to update the tracking location. In comparison to an advanced correlation filtering algorithm using the pedestrian sequences in the OTB-100 dataset, our algorithm achieves accuracy and success rates of 0.974 and 0.709, respectively. Moreover, it exhibits strong robustness and real-time performance in complex environments such as target occlusion and out-of-field challenges.
Abdominal Multi-Organ Segmentation Based on nnUNet

In recent years, medical image segmentation has been a critical task in the field of medical image analysis. Accurate segmentation of medical images is essential for many clinical applications such as diagnosis, treatment planning, and monitoring. In this study, we use the nnUNet model, a state-of-the-art deep learning architecture, to achieve very good results on abdominal multi-organ segmentation. Our dataset is selected from AMOS, which is a large-scale and diverse benchmark collected from abdominal multi-organ segmentation in real clinical scenarios, containing a total of 160 training data and 40 testing data. The nnUNet model demonstrated high accuracy, achieving excellent segmentation results on the dataset, covering 15 abdominal organs segmentation tasks including the spleen, right kidney, left kidney, gallbladder, etc.
Research on. NET Intelligent Platform Based on Three Layer Structure

The. NET three-tier architecture is a commonly used software development architecture that divides applications into three logical layers: the presentation layer, the business logic layer, and the data access layer. The presentation layer is responsible for interacting with users and receiving user input; The business logic layer processes business rules, validates data, and executes application logic; The data access layer is responsible for interacting with the database, including querying, updating, and deleting data. Using the. NET three tier architecture can reduce code coupling and improve application scalability and maintainability. Developers need to design the interfaces and communication methods between various layers based on the requirements of the application. They can use ASP.NET MVC or Web Forms to build Web applications, and use C # language to write business logic and database operation code.
Pancreas Segmentation Based on Multi-scale Convolution and Attention Module

The pancreas is characterized by its small size and variable shape. The current neural network model is not high in segmentation accuracy of pancreatic target region image, and there is still much room for improvement. U-Net is a kind of network with excellent performance in medical image segmentation. However, the traditional U-Net network is not strong enough to extract feature information of small regions. To improve the ability of the traditional U-Net network to extract feature information of local small regions, and to make the pancreas organ segmentation boundary clearer, this paper designs a multi-scale convolution block (MCB), Channel Attention module and Attention Gates structure are added into the U-Net network. The network model emphasizes the information extraction of a small region, so it can improve the segmentation effect. Dice coefficient, MIoU coefficient, and Precision coefficient are selected as the evaluation indexes of the training effect and conducted a series of experiments on the NIH pancreas segmentation dataset. The experimental data show that the model in this paper has improved by 1-2 percentage points in each positive index coefficient compared with the latest model, and the model converged faster, which confirms the superiority of the model.
Graph Contrastive Learning with Wasserstein Distance for Recommendation

Contrastive learning has been applied to graph collaborative filtering in recent years as a support task to boost efficiency. The majority of these approaches ignore global differences in favor of local variations among subgraphs. Additionally, different data augmentation strategies need to be developed for various datasets. Further, similarity metrics based on vector inner products might obscure sample-to-sample structural variations. To achieve this, we propose a method that adaptively samples subgraphs first, then uses a Wasserstein-based subgraph similarity measure to capture subgraph overall differences. To be more precise, we sample subgraph nodes from close to far, create subgraph edges using a similarity measure, and then build a contrastive loss based on Wasserstein distance to capture the distinction between pairs. Through extensive experimentation, we show the proposed method to be effective.
A Multi-Label Classification Algorithm Based on Self-paced Learning and LDA

To address the problem that existing multi-label learning algorithms treat all samples equally during model training and ignore inter-sample variability, this paper proposes a multi-label classification algorithm based on self-paced learning and LDA (SPLDA for short). SPLDA first constructs an adaptive self-paced function based on the label prediction loss values in multi-label data, and introduces a self-paced learning framework. Secondly, the linear discriminant analysis regularization term in the low-dimensional space of the samples is constructed by combining the principles of minimizing the within-class distance and maximizing the between-class distance in this framework. Next, a multi-label classification model is trained using alternating optimization methods. Finally, the experimental results on 5 real datasets and 3 evaluation metrics show that SPLDA outperforms the existing multi-label classification algorithms.
CPK-Adapter: Infusing Medical Knowledge into K-Adapter with Continuous Prompt

Recently, pre-trained language models (PLMs) have been significantly improved for downstream tasks by infusing knowledge. In the field of medical research, with the continuous updating and increasing of data, PLM often requires continuous learning of knowledge. Most existing methods jointly store new knowledge and historical knowledge in an entangled way by updating the overall parameters of PLM. However, when new knowledge infuses into PLM continuously, the historical knowledge is replaced by new knowledge. In this article, we propose CPK-Adapter, a method combines prompt learning and continual learning to balance the abilities of PLM to learn new knowledge and to memorize historical knowledge. We evaluated CPK-Adapter on a suit of BERT models, including BERT, BioBERT, ClinicalBERT, and BlueBERT. The experiments demonstrate that CPK-Adapter outperforms the comparative PLMs in medical tasks, and CPK-Adapter is significantly better than K-Adapter. Furthermore, the performance of CPK-Adapter approaches or exceeds DiseaseBERT which stores task knowledge by updating the overall parameters of PLM.
Video Anomaly Detection Based on Multiple Instance 3D Channel Attention

Video anomaly detection is a hot topic in the field of computer vision and has broad application prospects. To address the issues of feature enhancement and temporal continuity, this paper proposes a framework named multiple instance 3D channel attention. In particular, this framework includes two networks, i.e., the pseudo label generation network and the anomaly detection network. Based on smoothness and sparsity constraints, pseudo labels for video clips are generated in the pseudo label generation network. Moreover, the 3D channel attention block is designed to enhance features. On the ShanghaiTech dataset, experimental results demonstrated that the proposed method obtained better performance than baseline method and other methods.
Machine Vision Based Transformer Insulation Pad Identification Detection

Addressing the issues of low efficiency, low accuracy, and high cost in traditional transformer insulation pad recognition and detection, A machine vision based contour size detection system for transformer insulation pads was studied and designed. The image of the pad is captured by an industrial camera, the image is analyzed and processed by the vision software OpenCV, the shape is recognized by an improved edge detection algorithm, the minimum outer contour is fitted by the Douglas-Peucker algorithm, and the conversion of the spatial coordinate system is completed by fast camera calibration and pixel conversion, and finally the outer contour recognition and the length and width of different models of transformer insulation pads are realized. The results are displayed through the MFC user interface. The experimental results show that the pad identification and detection system is stable and can guarantee a high accuracy rate and solve various problems in traditional measurement.
Research on cotton seedling identification algorithm based on improved YOLOv7

In this paper, we propose an improved YOLOV7-based method for detecting cotton seedlings that addresses the challenging issues of overlap and small targets. To enhance the algorithm's precision in detecting small targets, we introduce the Normalized Wasserstein Distance as a new similarity measure for the enclosing frame. Furthermore, we incorporate the MobileNetViTv3 block into the Backbone of YOLOv7 to capture global information and aid in accurate localization in high-density scenes. Our algorithm achieves a detection accuracy of 97.6% for 384×384 sample images on the TAMU2015 dataset, surpassing the previous accuracy of 95.7%. Additionally, our algorithm satisfies the real-time detection requirements. Compared to mainstream algorithms, our improved method demonstrates superior performance.
Drill Pipe Counting Method in Coal Mine Based on Improved Object Tracking

With the advancement of mining intelligence, the application of artificial intelligence in this field is increasing. However, how to accurately count the number of drill pipes and improve safety production is still a challenge. To address this issue, one algorithm is proposed that involves analyzing the drilling pattern and using visual object tracking to count drill pipes. The You Only Look Once (YOLO) is improved by using rotated object detection with the circular smoothing label to obtain the position and angle information of the drill in the first frame. A dynamic template update strategy is adopted to improve the accuracy of Transformer Tracking (TransT) in long-term object tracking. Finally, the drill pipe is counted by using the peak points of the trajectory and worker actions with PoseConv3D. Compared with the baseline, the proposed algorithm achieves an average accuracy of 95.62% and a counting efficiency of 42.5 frames per second (FPS), making it more robust and accurate in the field of drill pipe counting.