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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Jiang, Xuhui Shen, Yinghan Wang, Yuanzhuo Shen, Huawei Xu, Chengjin Ma, Shengjie Data Intelligent System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology UCAS Gloucester United Kingdom Big Data Academy Zhongke Zhengzhou China International Digital Economy Academy Shenzhen China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9783031306747

Social relation reasoning in heterogeneous social networks (HSNs) should not only infer whether people are connected, but also why they know each other and what their relationship is. This task is challenging because the heterogeneous social network is non-trivial to leverage and the collected data is always noisy and incomplete, which leads that the existing rule-based and representation methods cannot efficiently and accurately reason social relations. To reason social relations in noisy and incomplete HSNs, we shift from simply inferring meta-path based social relations by rule-based methods to reasoning by social relation learning. Then, we present an inductive model named Heterogeneous Graph Variational AutoEncoders (HGVAE) towards robust social relation learning on noisy and incomplete HSN data. In HGVAE, the well-designed heterogeneous graph encoder, multi-signal decoder, and variational inference mechanism bring prominent robustness and significant performance improvement. Extensive experiments are conducted to compare our methods against a set of the most representative baseline methods. The efficiency analysis, robust analysis and ablation studies confirm the value of our proposed approach for modelling meta-path based social relations buried in various HSNs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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An API Field Design Method Based on Pre-trained Model

An API Field Design Method Based on Pre-trained Model

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International Conference on Software Technology and engineering (ICSTE)

作者： Hai Wang Yihua Lou Yi Yang Ranbin Xu Jingbo Yang Xingchuang Liao Beijing Engineering Research Center of Civil Aviation Big Data TravelSky Technology Limited Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Application programming interface (API) plays an important role in modern software development, a well designed API can provide tremendous convenience for developers. However, designing an excellent API is not a trivial task, each field in the API should avoid any ambiguity. Besides, in some special cases, for instance, a Chinese developer might not accurately translate the meaning of a Chinese API field into its English counterpart. Therefore, there exists a tremendous requirement in designing high quality API field. In this paper, a fine-tuning pre-trained model based method is proposed for API field design task. Firstly, we consider the design of API fields as a translation task, converting the fields from another language into English. Secondly, we fine-tune a pre-trained model for API field design task. In the end, we curate two datasets to illustrate our method and the experiment results demonstrate that our method has a better performance in designing API field.

关键词： Translation Design methodology Software Application programming interfaces Software development management

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Customer Churn Combination Prediction Model Based on Convolutional Neural Network and Gradient Boosting Decision Tree 22

Customer Churn Combination Prediction Model Based on Convolu...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Shiyang Li Guoen Xia Xianquan Zhang School of Computer Science and Engineering Guangxi Normal University China Guangxi Big Data Analysis of Taxation Research Center of Engineering Guangxi University of Finance and Economics China

ISBN: (纸本)9781450398336

In order to improve the hit ratio of lost customers in telecom industry, a combination prediction model of customer churn based on one-dimensional convolutional neural network(1DCNN) and gradient boosting decision tree(GBDT) is proposed. Firstly, customer data is fed into 1DCNN model, which uses one-dimensional convolution to automatically extract customer features and then predicts customer churn through full connection layer. If the prediction result of 1DCNN model is churn, the result is directly output. If the prediction result is non-churn, the customer data will be re-introduced into GBDT model for second forecast, and the new prediction result will be output. Experiments on two publicly available telecom customer data set show that the proposed combined model significantly improves the recall rate and F1 score of customer churn prediction.

关键词： gradient boosting decision tree combination model one-dimensional convolutional neural network customer churn prediction

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Intensity Confusion Matters: An Intensity-Distance Guided Loss For Bronchus Segmentation

Intensity Confusion Matters: An Intensity-Distance Guided Lo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haifan Gong Wenhao Huang Huan Zhang Yu Wang Xiang Wan Hong Shen Guanbin Li Haofeng Li Shenzhen Research Institute of Big Data China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China InferVision China School of Computer Science and Engineering Sun Yat-sen University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have paied less attention to the issue we term as Intensity Confusion, wherein the intensity values of certain background voxels approach those of the foreground voxels within bronchi. Conversely, the intensity values of some foreground voxels are nearly identical to those of background voxels. This proximity in intensity values introduces significant challenges to neural network methodologies. To address the issue, we introduce a novel Intensity-Distance Guided loss function, which assigns adaptive weights to different image voxels for mining hard samples that cause the intensity confusion. The proposed loss estimates the voxel-level hardness of samples, on the basis of the following intensity and distance priors. We regard a voxel as a hard sample if it is in: (1) the background and has an intensity value close to the bronchus region; (2) the bronchus region and is of higher intensity than most voxels inside the bronchus; (3) the background region and at a short distance from the bronchus. Extensive experiments not only show the superiority of our method compared with the state-of-the-art methods, but also verify that tackling the intensity confusion issue helps to significantly improve bronchus segmentation. Project page: https://***/lhaof/ICM.

关键词： Image segmentation Image resolution Accuracy Computed tomography Neural networks Focusing Benchmark testing

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Enabling Complete Atomicity for Cross-Chain Applications Through Layered State Commitments

Enabling Complete Atomicity for Cross-Chain Applications Thr...

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Reliable Distributed Systems

作者： Yuandi Cai Ru Cheng Yifan Zhou Shijie Zhang Jiang Xiao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Cross-chain Decentralized Applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dependent transactions execute correctly together, known as complete atomicity, remains a challenge. Existing works provide financial atomicity, protecting against monetary loss, but lack the ability to ensure correctness for complex tasks. In this paper, we introduce Avalon, a transaction execution framework for cross-chain dApps that guarantees complete atomicity for the first time. Avalon achieves this by introducing multiple state layers above the native one to cache state transitions, allowing for efficient management of these state transitions. Most notably, for concurrent cross-chain transactions, Avalon resolves not only intra-chain conflicts but also addresses potential inconsistencies between blockchains via a novel state synchronization protocol, enabling serializable cross-chain execution. We implement Avalon using smart contracts in Cosmos ecosystem and evaluate its commitment performance, demonstrating acceptable latency and gas consumption even under conflict cases.

关键词： Atomic layer deposition Smart contracts Ecosystems Decentralized applications Consensus protocol Synchronization Reliability Interoperability

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Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14752-14765页

作者： Sun, Ziyi Xue, Bing Zhang, Mengjie Schindler, Jan Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington 6140 New Zealand Manaaki Whenua-Landcare Research Wellington 6140 New Zealand

Instance segmentation techniques based on Convolutional Neural Networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. In varied rural landscapes, canopy imagery often includes a mix of tiny, small, and medium tree objects scattered across diverse terrains, from standalone trees to densely clustered forest stands. This variability poses significant challenges to traditional instance segmentation methods. To achieve this, we introduce a new method named YOLOv8-FF, which incorporates a feature fusion (FF) technique based on the YOLOv8 architecture. We first design a network architecture based on YOLOv8 that is optimized for the characteristics of our dataset, enabling effective segmentation of densely distributed tiny and small tree crowns. Moreover, YOLOv8-FF incorporates a feature fusion mechanism that includes both cross-scale and same-scale fusion methods, enhancing the model's ability to integrate information across different layers and scales, thereby improving segmentation performance. We incorporate Sparse Large Kernel Network (SLaK), whose large convolution kernel can effectively extract key features, helping the model capture richer and deeper global information in the image. Experimental results on the tree crown dataset demonstrate that YOLOv8-FF outperforms several recent peer competitors, making it a promising tool for accurate and efficient tree crown instance segmentation. © 2008-2012 IEEE.

关键词： Instance segmentation remote sensing tree crowns tree species YOLOv8

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Efficient Hardware Redo Logging for Secure Persistent Memory 23

Efficient Hardware Redo Logging for Secure Persistent Memory

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Zhan Yue, Jianhui Liao, Xiaofei Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China Michigan Technological University Computer Science Department HoughtonMI United States

ISBN: (纸本)9781665494571

Write-ahead log and data encryption technologies are employed to ensure both crash consistency and data security for persistent memory (PM). The encryption/decryption of both data and log requests increase the memory request latency, degrading the system performance. To tackle this issue, this paper proposes a novel log-aware memory encryption scheme to reduce encryption/decryption operations, without compromising data security. Specifically, redo log blocks are stored as ciphertext for their corresponding data blocks to avoid encryption/decryption of log data blocks when in-place update operations are applied to them. We further design a compact log record layout with fewer encryption metadata in log records to reduce logging traffic. Our simulation results show that the transaction throughput of the proposed design outperforms the baseline design and the state-of-the-art design by 87.3% and 55.3% on average, respectively. © 2021 IEEE.

关键词： Smart cities System performance Simulation Memory management Layout Memory architecture Metadata

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of Information Technology & Electronic engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

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12th CCF Conference on bigdata, bigdata 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

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World Journal of Pediatrics 2024年第10期20卷 1090-1101页

作者： Zhi-Xing Zhu Georgi ZGenchev Yan-Min Wang Wei Ji Yong-Yong Ren Guo-Li Tian Sira Sriswasdi Hui Lu Shanghai Engineering Research Center for Big Datain Pediatric Precision Medicine Center for BiomedicalInformaticsShanghai Children’s HospitalSchoolof MedicineShanghai Jiao Tong UniversityShanghaiChina Center of Excellence in Computational Molecular Biology Faculty of MedicineChulalongkorn UniversityBangkokThailand Newborn Screening Center Shanghai Children’s HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational MedicineShanghai JiaoTong UniversityShanghaiChina Center for Artifcial Intelligence in Medicine ResearchAfairsFaculty of MedicineChulalongkorn UniversityBangkokThailand State Key Laboratory of Microbial Metabolism JointInternational Research Laboratory of Metabolic&Developmental SciencesDepartment of Bioinformaticsand BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,***-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of *** We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives *** utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature *** selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model *** Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising *** best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the *** model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%*** The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric *** improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help r

关键词： Machine learning Methylmalonic acidemia Second-tier test Stacking

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