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arXiv 2024年

作者： Lyu, Shen-Huan Wu, Jin-Hui Zheng, Qin-Cheng Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. Zhou and Feng [50] further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments confirm the theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

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A recommendation algorithm for point of interest using time-based collaborative filtering

A recommendation algorithm for point of interest using time-...

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作者： Zeng, Jun He, Xin Li, Feng Wu, Yingbo School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Location-based social networks (LBSNs) make it possible for people to share their visited places by uploading the check-in information. To improve the efficiency of recommendation algorithm, researchers introduce check-in data into point of interest (POI) recommendation to help users find new and interesting place. However, some researches ignore the signification of time factor for POI recommendation in LBSNs. In this paper, we propose a time-based collaborative filtering algorithm according to the similarity between users which combines the global similarity during a long period and local similarity within a short time interval. The experimental results show that the method we proposed can get more accurate recommendation. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

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A Penalty-Dual Learning Framework for Multi-User QoS-Constrained Beamforming

A Penalty-Dual Learning Framework for Multi-User QoS-Constra...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yang Li Ya-Feng Liu Fan Xu Qingjiang Shi Tsung-Hui Chang Shenzhen Research Institute of Big Data China LSEC ICMSEC AMSS Chinese Academy of Sciences China Peng Cheng Laboratory China School of Software Engineering Tongji University China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

This paper investigates a novel deep learning framework for general nonconvex quality-of-service (QoS)-constrained beamforming design problems in multi-user systems. While existing deep learning-based methods have shown great success for beamforming design with simple constraints, it is still challenging to tackle more complicated QoS constraints, where the beam-formers and the wireless channels are commonly coupled. To fill this gap, this paper proposes an augmented Lagrangian based penalty-dual training algorithm, which alternatingly trains two individual neural networks to infer the beamformers and the corresponding Lagrange multipliers, respectively. This framework is then applied for learning the energy-efficient beamformers, with judiciously designed neural network architectures based on the solution structures of the problem. Simulations demonstrate the superiority of the proposed approach over both state-of-the-art deep learning and optimization-based approaches.

关键词： Wireless communication Deep learning Training Learning systems Array signal processing Conferences Neural networks Signal processing algorithms Quality of service Energy efficiency

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词： Training Metalearning Visualization Semantic segmentation Semantics Prototypes Training data

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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Class-Aware Multi-window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features 23rd

Class-Aware Multi-window Adversarial Lung Nodule Synthesis C...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Wang, Qiuli Zhang, Xingpeng Chen, Wei Wang, Kun Zhang, Xiaohong School of Big Data and Software Engineering Chongqing University Chongqing400030 China

ISBN: (纸本)9783030597245

Nodule CT image synthesis is effective as a data augmentation method for deep learning tasks about lung nodules. To advance the realistic malignant/benign lung nodule synthesis, the conditional Generative Adversarial Networks have been widely adopted. In this paper, we argue about an issue in the existing technique for class-aware nodule synthesis: the class-aware controllability of semantic features. To address this issue, we propose a adversarial lung nodule synthesis framework based on conditional Generative Adversarial Networks and class-aware multi-window semantic feature learning. By learning semantic features from multi-window CT images, our framework can generate realistic nodule CT images, and has better controllability of class-aware nodule features. Our framework provides a new perspective for nodule CT image synthesis that has never been noticed before. We train our framework on the public dataset LIDC-IDRI. Our framework improves the malignancy prediction F1 score by more than 3% and shows promising results as a solution for lung nodule augmentation. The source code can be found at https://***/qiuliwang/CA-MW-Adversarial-Synthesis. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Collaborative Segmentation Model for Colonoscopy Ulcers based on Fuzzy Labeling*

Collaborative Segmentation Model for Colonoscopy Ulcers base...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yanning Lin Jie Chen Ziqian Ding Linxu Chen Le Li Zhongsheng Zhu Zhaoxia Wang Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Children’s Hospital Shenzhen China Gastroenterology Shenzhen Children’s Hospital China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Inflammatory Bowel Disease (IBD) is a global chronic intestinal inflammatory disease, and its incidence rate increases year by year with the progress of economic globalization. Currently, the diagnosis of IBD in children mainly relies on endoscopic examination, but scoring endoscopic images is a challenging issue, especially in distinguishing different types of ulcers. To address this issue, this article designs a mobile application to accelerate data annotation processing and may provide reference for other unlabeled datasets. In the context of image segmentation, blurring labels has become an important issue. Deep learning methods are widely used in medical image segmentation, but their accuracy depends on high-quality annotated data. However, there are low-quality noise areas in the annotated data, and obtaining accurate and high-quality annotations becomes more time-consuming with limited annotation budgets. This article proposes a collaborative training framework to improve learning of noisy pixels. This framework determines the label confidence of an image by calculating the similarity between image pixels and surrounding pixels. Then, two parallel deep networks were constructed for semantic prediction, which aimed to guide each other on pixels that may have noise. By applying consistency in dual network prediction, the semantic information of uncertain pixels is corrected as much as possible. Experimental results have shown that this framework is slightly superior to models trained with pixel level precise labels, thus more effectively utilizing existing annotated data in the case of fuzzy labels.

关键词： Training Image segmentation Accuracy Annotations Biological system modeling Semantics Noise Collaboration Colonoscopy Medical diagnostic imaging

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IIoT Mobile Business data Placement Strategy Based on Bayesian Optimization Algorithm 24

IIoT Mobile Business Data Placement Strategy Based on Bayesi...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shen, Zhijie Liu, Bowen Xu, Xiaolong Qi, Lianyong Dai, Fei Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350319934

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies. © 2022 IEEE.

关键词： Edge computing

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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