In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.

https://publications.eai.eu/index.php/airo/article/download/3462/2413

Review of Image Classification Algorithms Based on Graph Convolutional Networks

Resource leaks, which are caused by acquired resources not being released, often result in performance degradation and system crashes. Resource leak detection relies on two essential components: identifying potential Resource Acquisition and Release (RAR) API pairs, and subsequently analyze code to uncover instances where the corresponding release API call is absent after an acquisition API call. Yet, existing techniques confine themselves to an incomplete pair pool, either pre-defined manually or mined from project-specific code corpus, thus limiting coverage across libraries/APIs and po- tentially overlooking latent resource leaks. In this work, we propose to represent resource-operation knowledge as abstract resource acquisition/release operation pairs (Abs-RAR pairs for short), and present a novel approach called MiROK to mine such Abs-RAR pairs to construct a better RAR pair pool. Given a large code corpus, MiROK first mines Abs-RAR pairs with rule-based pair expansion and learning-based pair identification strategies, and then instantiates these Abs-RAR pairs into concrete RAR pairs. We implement MiROK and apply it to mine RAR pairs from a large code corpus of 1,454,224 Java methods and 20,000 Maven libraries. We then perform an extensive evaluation to investigate the mining effectiveness of MiROK and the practical usage of its mined RAR pairs for supporting resource leak detection. Our results show that MiROK mines 1,313 new Abs-RAR pairs and instantiates them into 6,314 RAR pairs with a high precision (i.e., 93.3%). In addition, by feeding our mined RAR pairs, existing approaches detect more resource leak defects in both online code examples and open-source projects

Mining Resource-Operation Knowledge to Support Resource Leak Detection

Estimating fetal brain age based on sulci by magnetic resonance imaging (MRI) is clinically crucial in determining the normal development of fetal brains. Deep learning provides a possible way for fetal brain age estimation using MRI. Previous studies have mainly emphasized optimizing individual-wise correlation criteria, such as mean square error. However, they ignored the very important global and peer-wise criterion, which are essential for learning the structured relationships among regression samples. Moreover, the imbalanced label distribution introduces an adverse bias, which impairs the reliability and interpretation of correlation estimation and the model's fairness and generalizability. In this work, we propose a novel joint correlation learning with ranking similarity regularization (JoCoRank) algorithm for deep imbalanced regression of fetal brain age. Joint correlation learning concurrently captures individual, global, and peer-level valuable relationship information, and the customized optimization scheme for each criterion exhibits strong robustness against outliers and imbalanced regression. Ranking similarity regularization is designed to calibrate the biased feature representations by aligning the sorted list of neighbors in the label space with those in the feature space. A total of 1327 MRI images from 157 healthy fetuses between 22 and 34 weeks were collected at Wuhan Children's Hospital and utilized to evaluate the performance of JoCoRank in fetal brain age estimation. JoCoRank achieved promising results with an average mean absolute error of 0.693±0.064 weeks and R2 coefficient of 0.930±0.019. Our fetal brain age estimation algorithm would be useful for identifying abnormalities in fetal brain development and undertaking early intervention in clinical practice. 

JoCoRank: Joint correlation learning with ranking similarity regularization for imbalanced fetal brain age regression

While Application Programming Interfaces (APIs) allow easier reuse of existing functionality, developers might make mistakes in using these APIs (a.k.a. API misuses). If an API usage specification exists, then automatically detecting such misuses becomes feasible. Since manually encoding specifications is a tedious process, there has been a lot of research regarding pattern-based specification mining. However, while annotations are widely used in Java enterprise microservices frameworks, most of these pattern-based rule discovery techniques have not considered annotation-based API usage rules. In this industrial case study of MicroProfile, an open-source Java microservices framework developed by IBM and others, we investigate whether the idea of pattern-based discovery of rules can be applied to annotation-based API usages. We find that our pattern-based approach mines 23 candidate rules, among which 4 are fully valid specifications and 8 are partially valid specifications. Overall, our technique mines 12 valid rules, 10 of which are not even documented in the official MicroProfile documentation. To evaluate the usefulness of the mined rules, we scan MicroProfile client projects for violations. We find 100 violations of 5 rules in 16 projects. Our results suggest that the mined rules can be useful in detecting and preventing annotation-based API misuses. 

Mining Annotation Usage Rules: A Case Study with MicroProfile

Human motion capture (mocap) data is of crucial importance to the realistic character animation, and the missing optical marker problem caused by marker falling off or occlusions often limit its performance in real-world applications. Although great progress has been made in mocap data recovery, it is still a challenging task primarily due to the articulated complexity and long-term dependencies in movements. To tackle these concerns, this paper proposes an efficient mocap data recovery approach by using Relationship-aggregated Graph Network and Temporal Pattern Reasoning (RGN-TPR). The RGN is comprised of two tailored graph encoders, local graph encoder (LGE) and global graph encoder (GGE). By dividing the human skeletal structure into several parts, LGE encodes the high-level semantic node features and their semantic relationships in each local part, while the GGE aggregates the structural relationships between different parts for whole skeletal data representation. Further, TPR utilizes self-attention mechanism to exploit the intra-frame interactions, and employs temporal transformer to capture long-term dependencies, whereby the discriminative spatio-temporal features can be reasonably obtained for efficient motion recovery. Extensive experiments tested on public datasets qualitatively and quantitatively verify the superiorities of the proposed learning framework for mocap data recovery, and show its improved performance with the state-of-the-arts.

Efficient motion capture data recovery via relationship-aggregated graph network and temporal pattern reasoning.

Annotations have been widely used in Java programs to support additional compile-time, deployment-time, and runtime processing. Developers use annotations to delegate repetitive logics such as object initialization and request forwarding to compilers and runtime frameworks. Therefore, these annotations are important for the correct execution of programs. In practice, however, developers often find it hard to correctly use annotations and the misuse of annotations has led to real bugs in Java programs. In this paper, we conduct an empirical study on Stack Overflow questions to investigate the major development frameworks that are involved in questions about Java annotations and the main problems encountered by developers in the use of Java annotations. Based on the findings of the study, we propose DeepAnna, a deep learning based Java annotation recommendation and misuse detection approach. Based on a corpus of Java programs with intensive use of annotations, DeepAnna trains a deep learning based multi-label classification model by considering both the structural and textual contexts of source code. DeepAnna can recommend annotations at both class level and method level. Our evaluation with a large corpus of open-source Java projects shows that DeepAnna outperforms state-of-the-art text multi-label classification approaches in annotation recommendation and can effectively detect annotation misuses. Based on our analysis, we submit 85 bug-fixing pull requests for annotation misuses in open-source projects and 20 of them have been accepted and merged.

DeepAnna: Deep Learning based Java Annotation Recommendation and Misuse Detection

In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version (S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.

A Safe Semi-supervised Graph Convolution Network

Fetal magnetic resonance imaging (MRI) sequences are often used for fetal development judgment and disease diagnosis. In clinical practice, doctors usually rely on the naked eye to identify and make corresponding judgments based on the shape, depth and appearance time of the sulci and gyri between different gestational ages. This study uses deep learning to classify and predict fetal gestational age. Because ultrasound detection is accurate in the first trimester, and after this time, it is impossible to accurately judge and predict the corresponding gestational age, so this is a problem that we need to solve. We use MRI images to classify and predict the gestational age of the next three months. We select 13 gestational ages of the coronal sequence of 22 to 34 as the classification prediction data. For adequate training, we changed the cross-entropy loss function of the network model to Fnlloss and Flloss we proposed. The model after modifying the loss function has a better improvement in the accuracy of classification compared to the original cross-entropy loss used by the network model

Deep Learning with Modified Loss function to Predict Gestational Age of the Fetal Brain

Whether at home or abroad, local debts play an important role in easing the contradiction between supply and demand of funds, and promoting local economic development and other aspects. The measure of the appropriate local debts scale is the basis to control local debts risks, and it is an important issue in the study of local debts. Based on the analysis on the influence factors of local debts scale, we build a panel data model of moderate local debts scale, calculate 30 provinces and cities where local debts are issued by the moderate scale, and provide basic methods for the issuance scale of local debts. Index Terms - local debts, appropriate scale, panel data model Local government bonds(hereinafter called local debts) is mainly refer to a financing form of raising funds, which makes the local government uses credit tool in order to meet the needs of the development of the local economic and social public welfare under the shortage of regular revenues, in accordance with the relevant legal provisions. At present, 37 countries allow to issue local debts of 53 major countries in the world. China's local debts is a very tortuous course. The future of local debts will be according to the following pattern transformation, namely "central government on behalf→self-issuing→issuing autonomously". (1) Only moderately issuing local debts can governments control and reduce the local debts risks and the local financial crisis from the source, so that the local debts really plays a role to promote local economic development and social stability. Determining the appropriate scale of government bonds has become an important problem to research. To measure the debt scale is the foundation of moderately issuing. Research on government debt scale of foreign scholars: government debt issuance should control the scale(2-5). Influence factors of the government debt issuance scale(2-5) mainly include GDP, fiscal revenue, income distribution, deposit, urbanization rate, population flow, interest rate etc. Quantitative research methods of China's local debts issuance scale are: (1) KMV model. Scholars such as Wang Gang(6) put forward the concept of the default risk of municipal bonds on the basis of American municipal bonds, and build the credit risk model for municipal bonds by using KMV model. He calculates the debt scale of different default probability by taking Beijing and Shanghai as examples. (2) Theoretical inference. Scholars such as Wang Yu(7) conclude that: in the rational expectations equilibrium model, when the control of the government can not improve the cash flow in the future, investors will refuse to buy the bonds. Therefore, a small risk of insolvency will lead to a great risk of immobilization. (3) Regression model. Scholars such as Cheng Zhenhuang (8) taking

/pdf/the-study-on-estimating-the-scale-of-local-government-debt-4or6iv10kc.pdf

The Study on Estimating the Scale of Local Government Debt in China

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DeepAnna: Deep Learning based Java Annotation Recommendation and Misuse Detection

A Safe Semi-supervised Graph Convolution Network

Deep Learning with Modified Loss function to Predict Gestational Age of the Fetal Brain

The Study on Estimating the Scale of Local Government Debt in China