Earthquake disasters have had an important impact on people's lives and economy. As the national department for comprehensive response to various disasters, the Emergency Management Department has done a lot of wo...
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In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZS...
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With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quaranti...
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In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasin...
In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasingly important. In this work, we focus on community recommendation scenario. A distinctive feature of game community recommendation is that each member can only belong to one gang for a certain duration, which we refer to as uniqueness of communities. The problem caused by uniqueness is that for users to be recommended, there are no positive samples available for training. The challenge caused by uniqueness is that there are no positive samples available for training when users are recommended. Therefore, the collaborative filtering information between the user and the community is very sparse. Meanwhile, existing methods fail to fully model communities and users based on their features and profiles. To address these problems, we propose Hypergraph Attribute Attention Network (HATT) framework. In order to fully exploit user profiles and similarity between users, we discretize user features into entity nodes and model the heterogeneous relationships between users and communities by hyperedge. We propose a hypergraph attention-based message passing mechanism to capture the high-order relationships and obtain embedding with more semantics. At last, we design contrastive learning paradigms to enhance the model’s representation ability and apply a multi task training strategy to train the model. Extensive experiments on two real world game datasets are conducted and the results demonstrate the superiority of our method in community recommendation.
Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign a...
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Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.
Conventional synthetic aperture radar (SAR) imaging methods based on the Nyquist sampling theorem encounters the dilemma of balancing imaging quality and resource consumption. In order to solve the problem, we propose...
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The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no...
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A precise and durable health management strategy for battery power systems is crucial to maintaining the reliable operation of Unmanned Aerial Vehicles(UAVs).This paper introduces an adaptive Decay Regularized Stochas...
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One of the important research directions in information extraction is event extraction(EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for...
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To enhance the compatibility in the version control of Java Third-party Libraries (TPLs), Maven adopts Semantic Versioning (SemVer) to standardize the underlying meaning of versions, but users could still confront abn...
ISBN:
(纸本)9781450394758
To enhance the compatibility in the version control of Java Third-party Libraries (TPLs), Maven adopts Semantic Versioning (SemVer) to standardize the underlying meaning of versions, but users could still confront abnormal execution and crash after upgrades even if compilation and linkage succeed. It is caused by semantic breaking (SemB) issues, such that APIs directly used by users have identical signatures but inconsistent semantics across upgrades. To strengthen compliance with SemVer rules, developers and users should be alerted of such issues. Unfortunately, it is challenging to detect them statically, because semantic changes in the internal methods of APIs are difficult to capture. Dynamic testing can confirmingly uncover some, but it is limited by inadequate coverage. To detect SemB issues over compatible upgrades (Patch and Minor) by SemVer rules, we conduct an empirical study on 180 SemB issues to understand the root causes, inspired by which, we propose Sembid (Semantic Breaking Issue Detector) to statically detect such issues of TPLs for developers and users. Since APIs are directly used by users, Sembid detects and reports SemB issues based on APIs. For a pair of APIs, Sembid walks through the call chains originating from the API to locate breaking changes by measuring semantic diff. Then, Sembid checks if the breaking changes can affect API’s output along call chains. The evaluation showed Sembid achieved recall and precision and outperformed other API checkers on SemB API detection. We also revealed Sembid detected over 3 times more SemB APIs with better coverage than unit tests, the commonly used solution. Furthermore, we carried out an empirical study on 1,629,589 APIs from 546 version pairs of top Java libraries and found there were 2 ∼ 4 times more SemB APIs than those with signature-based issues. Due to various version release strategies, of Patch version pairs and of Minor version pairs had at least one API affected by any breaking.
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