Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this field.For a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or modules.However,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification problem.At the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs.Finally,we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction.Our model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches.Finally,we evaluated our model on two publicly accessible datasets.The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal model.On the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal model.Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Method for Determining the Importance of Critical Emergency Indicators Based on Multi-granularity Uncertain Language

In view of the complexity of emergencies and the subjectivity of decision-makers,a method of determining key emergency indicators based on multi-granularity uncertainty language is proposed.Firstly,decision members use preferred uncertain language phrases to represent the importance of each key indicator and use transformation functions to carry out the consistent transformation of this multi-granularity uncertain language information.Secondly,the group evaluation vector is obtained by using the extended weighted average operator of uncertainty,and then the weight vector of each key index is obtained by using the decision theory of uncertain language.Finally,an example is given to verify the practicability and effectiveness of the proposed method.

Multi-granularity spatial-temporal access control model for web GIS

The multi-granularity spatial-temporal-related access control（MSTAC） model was proposed to meet the spatial access control requirements for the service-oriented spatial data infrastructure（SDI）. MSTAC extends the attribute constraints of role-based access control（RBAC）, which includes the user＇s location attribute, the role＇s time constraint, the layer vector constraint of a map class, the scale and time constraints of a geographic layer, the topological constraints of geographic features, the semantic attribute expression constraints of geographic features, and the field constraint of feature views. Through this model, authorized users would be limited to access different granularity spatial datasets, such as the map granularity, the graphic layer granularity, the feature object granularity and the feature view granularity. Finally, the MSTAC model is achieved in a web GIS, which shows the positive and negative authorizations to different services in different data granularities and time periods.

Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.

CRF:A Scheduling of Multi-Granularity Locks in Object-Oriented Database Systems

This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are proposed in the paper. The ideas of single queue scheduling(SQS) and dual queue scheduling (DQS) are proposed and the algorithm and the performance evaluation for these two scheduling are presented in some paper. This paper describes a new idea of thescheduling for MGL, compatible requests first (CRF). Combining the new idea with SQS and DQS,we propose two new scheduling algorithms called CRFS and CRFD. After describing the simulationmodel, this paper illustrates the comparisons of the performance among these four algorithms. Asshown in the experiments, DQS has better performance than SQS, CRFD is better than DQS, CRFSperforms better than SQS, and CRFS is the best one of these four scheduling algorithms.

Network Resource Provisioning for IP over Multi-Granular Optical Networks

In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength switching （WXC） layer and fiber switching （FXC） layer. This network is capable of both IP layer grooming and wavelength grooming in a hierarchical manner. Resource provisioning in the multi-granular network paradigm is called hierarchical grooming problem. An integer linear programming （ILP） model is proposed to formulate the problem. An iterative heuristic approach is developed for solving the problem in large networks. Case study shows that IP/MG-OXC network is much more extendible and can significantly save the overall network cost as compared with IP over wavelength division multiplexing network.

A Novel Multi-Granularity Flexible-Grid Switching Optical-Node Architecture

A novel multi-granularity flexible-grid switching optical-node architecture is proposed in this paper.In our system,the photonic lanterns are used as mode division multiplexing/demultiplexing(MD-Mux/MD-Demux)for selecting mode.The wavelength division multiplexer/demultiplexer(WDMux/WD-Demux)and the fiber bragg gratings(FBGs)are used to select wavelength channels with the various grid.The experimental results show that the transmission bandwidth covers the C+L band,the average transmission loss is-13.4 dB,and the average crosstalk is-30.5 dB.The optical-node architecture is suit for mode division multiplexing(MDM)optical communication system.

Positive unlabeled named entity recognition with multi-granularity linguistic information

The research on named entity recognition for label-few domain is becoming increasingly important.In this paper,a novel algorithm,positive unlabeled named entity recognition(PUNER)with multi-granularity language information,is proposed,which combines positive unlabeled(PU)learning and deep learning to obtain the multi-granularity language information from a few labeled in-stances and many unlabeled instances to recognize named entities.First,PUNER selects reliable negative instances from unlabeled datasets,uses positive instances and a corresponding number of negative instances to train the PU learning classifier,and iterates continuously to label all unlabeled instances.Second,a neural network-based architecture to implement the PU learning classifier is used,and comprehensive text semantics through multi-granular language information are obtained,which helps the classifier correctly recognize named entities.Performance tests of the PUNER are carried out on three multilingual NER datasets,which are CoNLL2003,CoNLL 2002 and SIGHAN Bakeoff 2006.Experimental results demonstrate the effectiveness of the proposed PUNER.

Multi-granularity sequence generation for hierarchical image classification

Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity labels.We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification task.Specifically,we introduce a transformer architecture to encode the image into visual representation sequences.Next,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional information.The proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label sequence.The decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention mechanism.In this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different granularities.Evaluations on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed method.Our project is available at https://github.com/liuxindazz/mgs.

面向区块链漏洞知识库的大模型增强知识图谱问答模型

大语言模型(LLM)在专业领域特别是区块链漏洞领域应用时存在局限性,如专业术语噪声干扰和细粒度信息过重导致理解不足。为此,构建一种面向区块链漏洞知识库的增强型知识图谱问答模型(LMBK_KG)。通过整合大模型和知识图谱来增强知识表示和理解能力,同时利用多粒度语义信息进行专业问题的过滤和精准匹配。研究方法包括使用集成的多粒度语义信息和知识图谱来过滤专业术语噪声,以及采用大模型生成的回答与专业知识图谱进行结构化匹配和验证,以提高模型的鲁棒性和安全性。实验结果表明,所提出的模型在区块链漏洞领域问答的准确率比单独使用大模型提高26%。

MULTI-GRANULARITY EVOLUTION ANALYSIS OF SOFTWARE USING COMPLEX NETWORK THEORY

Software systems are a typical kind of man-made complex systems. Understanding their evolutions can lead to better software engineering practices. In this paper, the authors use complex network theory as a tool to analyze the evolution of object-oriented （OO） software from a multi-granularity perspective. First, a multi-granularity software networks model is proposed to represent the topological structures of a multi-version software system from three levels of granularity. Then, some parameters widely used in complex network theory are applied to characterize the software networks. By tracing the parameters＇ values in consecutive software systems, we have a better understanding about software evolution. A case study is conducted on an open source OO project, Azureus, as an example to illustrate our approach, and some underlying evolution characteristics are uncovered. These results provide a different dimension to our understanding of software evolutions and also are very useful for the design and development of OO software systems.

Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity

Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.

基于CAP-Net的多粒度乳腺癌病理图像识别模型

在医学图像识别领域,图像的特征提取与图片的放大倍数有着紧密的联系,因此,多数乳腺癌图像识别模型都会在不同放大倍数下进行实验。但在实际应用中希望能够综合不同倍数的图像信息来全面评估疾病特征,提升患者治疗效果。针对上述问题以及医学图像中肿瘤分类的挑战,聚焦于关注肿瘤类别而不依赖于特定放大倍数,提出了基于卷积神经网络(Convolutional Neural Networks, CNN)和上下文感知注意力池化(Context-aware Attentional Pooling, CAP)的分类模型。首先通过CNN提取图像的卷积特征,然后结合CAP模块综合考虑4种级别的特征上下文信息(包括像素级、小区域、大区域和图片级)进行分类。使用DenseNet121、 MobileNetV2和Xception 3种CNN网络结合CAP在BreaKHis数据集上进行实验,将同一类别4种不同放大倍数的数据合并起来,对8类乳腺癌病理图像进行识别。该模型的准确率达到了96.87%,验证了其在医学图像分类中的有效性。

A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks

There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering(DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks.The results show that our method outperforms other four state-of-the-art approaches.

融合时频特征的多粒度时间序列对比学习方法

现有的时间序列对比学习方法存在增强样本构造方式过于依赖人工经验、泛化能力不足、正样本的定义方式不够通用、对比度量方式存在粗粒度表征等问题,使得整体的时序表示效果较差。为此,提出了一种融合时频特征的多粒度时间序列对比学习方法(Temporal-Spectral Deep Contrastive Network, TSDC)。该方法通过季节-趋势生成网络在时域内产生具有稳定变化的时序增强样本,通过多频带融合扰动操作在频域内产生非稳定变化的时序增强样本,两种增强样本通过实例级别的粗粒度对比以及维度级别的细粒度对比方式进行对比学习,使得模型在获得较好表征的同时能够较好地适应于下游不同类型的时序任务。在多个时间序列公开数据集上进行的分类、预测以及异常检测实验表明,由TSDC方法所得的表征用于下游任务的结果优于典型基线模型。

Multi-granularities capture interaction information for text matching accuracy enhancement

Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network(CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.

Optimizing Fiber Topologies for WDM Optical Networks Based on Multi-Granularity Optical Switching Technology

For the quality of service （QoS） and fairness considerations, the hop counts of various lightpaths in a wavelength division multiplexing （WDM） optical network should be short and compact. The development of multi-granularity optical switching technology has made it possible to construct various fiber topologies over a fixed physical topology. This paper describes a fiber topology design （FTD） problem, which minimizes the maximum number of required fibers in the physical links for a maximum lightpath hop count in the fiber topology. After the formular description for the FTD problem, a method was given to obtain the lower bound on the maximum number of required fibers. For large or moderate scale networks, three heuristic algorithms are given to efficiently solve the FTD problem. This study gives a new way to optimize the resource configuration performance in WDM optical networks at the topology level and proves its effectiveness via both analyses and numerical experiments.