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IEEE Symposium on Computers and Communications (ISCC)

作者： Zhao Ming Chuan Sun Xiuhua Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users' complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

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Machine Learning-Based Generalized User Grouping in NOMA

Machine Learning-Based Generalized User Grouping in NOMA

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Weichao Chen Shengjie Zhao Rongqing Zhang Yi Chen Liuqing Yang School of Software Engineering Tongji University Shanghai China Shenzhen Research Institute of Big Data School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) Shenzhen China University of Minnesota MN USA

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Non-orthogonal multiple access (NOMA) provides high spectral efficiency and supports massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. We formulate a joint power control and GuG optimization problem, and then provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently. Simulation results show significant performance gains in terms of system sum rate.

关键词： NOMA Power control Uplink Silicon carbide Performance gain Machine learning Decoding

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CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine Supervision Network

CTF-Net: Retinal Vessel Segmentation via Deep Coarse-To-Fine...

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IEEE International Symposium on Biomedical Imaging

作者： Kun Wang Xiaohong Zhang Sheng Huang Qiuli Wang Feiyu Chen School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Retinal blood vessels structure plays an important role in the early diagnosis of diabetic retinopathy, which is a cause of blindness globally. However, the precise segmentation of retinal vessels is often extremely challenging due to the low contrast and noise of the capillaries. In this paper, we propose a novel model of deep coarse-to-fine supervision network (CTF-Net) to solve this problem. This model consists of two U-shaped architecture(coarse and fine segNet). The coarse segNet, which learns to predict probability retina map from input patchs, while the fine segNet refines the predicted map. To gain more paths to preserve the multi-scale and rich deep features information, we design an end-to-end training network instead of multi-stage learning framework to segment the retina vessel from coarse to fine. Furthermore, in order to improve feature representation and reduce the number of parameters of model, we introduce a novel feature augmentation module (FAM-residual block). Experiment results confirm that our method achieves the state-of-the-art performances on the popular datasets DRIVE, CHASE_DB1 and STARE.

关键词： Image segmentation Training Decoding Retinal vessels Biomedical imaging Standards

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A Novel Node-Level Rumor Propagation Model with Recommendation Mechanism

A Novel Node-Level Rumor Propagation Model with Recommendati...

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IEEE International Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Hong Peng Xiaofan Yang School of Big Data & Software Engineering Chongqing University Chongqing china

Nowadays, online social media have become an integral part of people daily life. To provide high-quality contents, a variety of recommender systems have been applied to social platforms. On the framework of epidemic theory, in this paper we present a novel node-level rumor propagation model that takes into account the influence of recommender systems. With the proposed model, we perform a series of numerical simulations to investigate the dynamics of rumor propagation. The result shows that with a recommendation mechanism rumors can fast spread through a social network. Also, we examine the effects of some factors in the proposed model. This paper provides theoretical insight into the influence of recommender systems on rumor propagation.

关键词： Numerical models Recommender systems Social network services Force Numerical simulation Network topology Topology

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DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

arXiv

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Research on The Framework of The GIS-document Workflow System

Research on The Framework of The GIS-document Workflow Syste...

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IEEE International Conference on software engineering and Service Sciences (ICSESS)

作者： Ying Du Pu Cheng School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Software Henan University Kaifeng China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Geographic information system (GIS)-document workflows are formed by combining workflow and GIS, and have the capabilities of geographic data processing, analysis, and visualization. They are widely used in areas with “integration of map and document.” An increasing number of complex business processes have great demands for business logic control and geographic data processing. Therefore, we established the framework of GIS - document workflow system in order to face the complex business processes and large geographic data processing processes.

关键词： Process control data visualization data processing Faces Geographic information systems Business software engineering

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Modeling data-Driven Liver Cancer Prediction with Medical Knowledge on Chinese Population

Modeling Data-Driven Liver Cancer Prediction with Medical Kn...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dandan Liu Jun Liao Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China

The following topics are dealt with: learning (artificial intelligence); mobile computing; feature extraction; Internet of Things; cloud computing; traffic engineering computing; data privacy; data analysis; Internet;... 详细信息

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

关键词： Cancer data models Liver Feature extraction Predictive models Training Support vector machines

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Misplaced subsequences repairing with application to multivariate industrial time series data

arXiv

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arXiv 2020年

作者： Ding, Xiaoou Wang, Hongzhi Su, Jiaxuan Wang, Chen Harbin Institute of Technology P.O.Box 750 HarbinHeilongjiang150001 China National Engineering Laboratory for Big Data Software EIRI Tsinghua University Beijing China

Both the volume and the collection velocity of time series generated by monitoring sensors are increasing in the Internet of Things (IoT). data management and analysis requires high quality and applicability of the IoT data. However, errors are prevalent in original time series data. Inconsistency in time series is a serious data quality problem existing widely in IoT. Such problem could be hardly solved by existing techniques. Motivated by this, we define an inconsistent subsequences problem in multivariate time series, and propose an integrity data repair approach to solve inconsistent problems. Our proposed repairing method consists of two parts: (1) we design effective anomaly detection method to discover latent inconsistent subsequences in the IoT time series;and (2) we develop repair algorithms to precisely locate the start and finish time of inconsistent intervals, and provide reliable repairing strategies. A thorough experiment on two real-life datasets verifies the superiority of our method compared to other practical approaches. Experimental results also show that our method captures and repairs inconsistency problems effectively in industrial time series in complex IIoT scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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Adversarial Learning with Cost-Sensitive Classes

arXiv

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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