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2024 International Symposium on Digital Home, ISDH 2024

作者： Qiu, Jie Chen, Zuoyun Chen, Jianmei Lan, Wei Ding, Xiaojun Computer Science and Engineering College Yulin Normal University Center for Applied Mathematics of Guangxi Guangxi Yulin537000 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Center for Applied Mathematics of Guangxi Guangxi Nanning537000 China

ISBN: (纸本)9798331509873

Many biological data exhibit characteristics such as high dimensionality, large scale, high complexity, and high noise. In many cases, it is necessary to eliminate noise and identify truly useful features for better data analysis and modeling. In labeled data, algorithms such as correlation analysis or embedded selection methods can be used to select features most relevant to the labels. However, these algorithms are not effective for unlabeled data. Currently, common methods involve selecting features with higher variance or, based on feature correlation, identifying central features from a group of related features. This paper proposes a new method for feature selection using self-representation of data. The idea is that features in high-dimensional biological big data exhibit some redundancy, and redundant features can be represented by other features. Therefore, it is only necessary to find a subset of features, from which the entire dataset can be generated. This subset may represent more essential and important features. Using this subset for subsequent work can resist certain noise and reduce complexity, thereby improving algorithm performance. The paper presents a framework that can use linear models and non-linear neural network models to select feature subsets. Experiments compared the proposed method with popular unsupervised feature selection methods, using KMeans, KNN, and SVM as classification methods to classify the original data and the data after feature selection, and analyzed the results. The experiments ultimately demonstrated that the proposed method in this paper outperforms other popular unsupervised feature selection methods on multiple biological datasets. © 2024 IEEE.

关键词： Feature Selection

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Compositional metric learning for multi-label classification

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Frontiers of computer Science 2021年第5期15卷 1-12页

作者： Yan-Ping SUN Min-Ling ZHANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationChina Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100China

Multi-label classification aims to assign a set of proper labels for each instance,where distance metric learning can help improve the generalization ability of instance-based multi-label classification *** multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature *** this paper,a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label *** one hand,compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on com-ponent *** the other hand,compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label *** to the compositional nature of employed distance metric,the resulting problem admits quadratic programming formulation with linear optimization complexity *** number of training *** also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional *** experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.

关键词： machine learning multi-label learning metric learning compositional metric positive semidefinite matrix decomposition

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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C2RG: Parameter-efficient Adaptation of 3D Vision and Language Foundation Model for Coronary CTA Report Generation

C2RG: Parameter-efficient Adaptation of 3D Vision and Langua...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ye, Zhiyu Sun, Yue Shi, Wei Yang, Bang Wu, Shibin Wang, Hancong Xu, Cheng Zheng, Hairong Wang, Yining Zhang, Tong Shenzhen Institute of Advanced Technology Pengcheng Laboratory Paul C.Lauterbur Research Center for Biomedical Imaging Department of Network Intelligence Shenzhen China University of Chinese Academy of Sciences Beijing China Chinese Academy of Medical Sciences Peking Union Medical College Peking Union Medical College Hospital Department of Radiology Beijing China Sun Yat-sen University School of Computer Science Guangzhou China Pengcheng Laboratory Department of Network Intelligence Shenzhen China Peking University Pengcheng Laboratory Adsplab School of Electronic and Computer Engineering Department of Network Intelligence Shenzhen China Tsinghua University Pengcheng Laboratory Shenzhen International Graduate School Department of Network Intelligence Shenzhen China Shenzhen Institute of Advanced Technology Paul C.Lauterbur Research Center for Biomedical Imaging Shenzhen China

ISBN: (纸本)9798350386226

Medical report generation (MRG) is a challenging yet highly demanding task in the application of multi-modal artificial intelligence in medicine. Typically, training an MRG model requires tens of thousands of labelled radiology images and reports datasets, which could be impractical for most clinical research groups. In this study, we present C2RG, a novel 3D vision and language foundation model tailored for Coronary Computed Tomography Angiography (CTA) Report Generation. Inspired by BLIP-2's architecture, our method integrates a self-supervised pre-trained 3D cardiac vision model (ViT-B) and a general-purpose bilingual foundation model (ChatGLM-6B), with a lightweight querying Transformer (Q-Former). We also introduce a parallel high-resolution feature extractor module and a coronary calcification evaluation loss to simultaneously encode fine-grained 3D features and constrain the accuracy of report generation. We compared our model with six state-of-the-art MRG methods on a clinical dataset with 118 subjects, comprising 453 paired 3D CTA images and radiology reports. Experimental results with extensive ablations show the efficacy of our C2RG. Codes will be open-sourced after the conference. © 2024 IEEE.

关键词： computerized tomography

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Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

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China Communications 2024年第11期21卷 257-266页

作者： Mu Yunping Fan Dian Wang Gongpu Xu Yongjun Kuang Lei Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China China Academy of Information and Communications Technology Beijing 100191China School of Communication and Information Engineering Chongqing University of Posts and TelemcommunicationsChongqing 400065China Shanghai Key Laboratory of Multidimensional Information Processing Engineering Center of SHMEC for Space Information and GNSSSchool of Information Science and TechnologyEast China Normal UniversityShanghai 200241China

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication *** letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system ***,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna ***,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is ***,simulation results are provided to corroborate our proposed studies.

关键词： activation distance ambient backscatter communications beamforming channel capacity power allocation sensitivity

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PROMIPL: A Probabilistic Generative Model for Multi-Instance Partial-Label Learning

PROMIPL: A Probabilistic Generative Model for Multi-Instance...

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IEEE International Conference on Data Mining (ICDM)

作者： Yin-Fang Yang Wei Tang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network Information Integration (Southeast University) Ministry of Education China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Multi-instance partial-label learning (MIPL) tackles scenarios where each training sample is represented as a multiinstance bag associated with a candidate label set. This set contains one true label and several false positives. Existing MIPL algorithms have predominantly focused on mapping multiinstance bags to candidate label sets for disambiguation. However, these algorithms may not be adequately generalizable in intricate real-world situations due to their reliance on heuristic methods for identifying true labels. In this paper, we propose PROMIPL, i.e., a PRObabilistic generative model for Multi-instance partiallabel learning, to address these challenges. PROMIPL is the first attempt to explore the probabilistic generative model to infer latent ground-truth labeling information from the data generation process in multi-instance partial-label learning. Besides, the discovered underlying structures also provide improved explanations of the classification predictions. To circumvent the computationally intensive process of training the generative model, we formulate a unified variational lower bound within the stochastic gradient variational Bayesian framework for the model parameters. Experimental results from benchmark and realworld datasets show that our proposed PROMIPL is competitive or superior to the state-of-the-art methods.

关键词： Training Lower bound Computational modeling Heuristic algorithms Stochastic processes Probabilistic logic Data models Bayes methods Labeling Data mining

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Offensive Text Style Transfer based on Unsupervised Learning

Offensive Text Style Transfer based on Unsupervised Learning

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2022 International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2022

作者： Zhao, Hanlin Yang, Peng School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781510660175

As the current mainstream social networking method, online social networking brings convenience to people, but also generates negative problems such as language violence. Therefore, offensive text style transfer research has become an arduous task. Research has shown that the semantic loss is caused by the discreteness and timing of the text data, resulting in a low degree of content retention after style transferring, and the lack of relevant parallel corpus data and specific style tag keywords, therefore, the accuracy of text style transfer is limited. Aiming at the existing problems, this paper proposes an Offensive Text Style Transfer based on the Unsupervised Learning (OTST-UL) model. First, the input text is encoded through the bidirectional encoding attention mechanism to retain the core content of the text. Then the generator-discriminator is applied for adversarial training, and a reconstruction loss algorithm is constructed to ensure the accuracy of the offensive language style transformation and the integrity of the text content. Experimental results show that the OTST-UL model outperforms existing text style transfer models on offensive language datasets. © 2022 SPIE.

关键词： Unsupervised learning

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Binary decomposition: a problem transformation perspective for open-set semi-supervised learning 24

Binary decomposition: a problem transformation perspective f...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jun-Yi Hang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Semi-supervised learning (SSL) is a classical machine learning paradigm dealing with labeled and unlabeled data. However, it often suffers performance degradation in real-world open-set scenarios, where unlabeled data contains outliers from novel categories that do not appear in labeled data. Existing studies commonly tackle this challenging open-set SSL problem with detect-and-filter strategy, which attempts to purify unlabeled data by detecting and filtering outliers. In this paper, we propose a novel binary decomposition strategy, which refrains from error-prone procedure of outlier detection by directly transforming the original open-set SSL problem into a number of standard binary SSL problems. Accordingly, a concise yet effective approach named BDMatch is presented. BDMatch confronts two attendant issues brought by binary decomposition, i.e. class-imbalance and representation-compromise, with adaptive logit adjustment and label-specific feature learning respectively. Comprehensive experiments on diversified benchmarks clearly validate the superiority of BDMatch as well as the effectiveness of our binary decomposition strategy.

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Cyber-Physical Systems Design in An Uncertain Environment with Time Uncertainty Concern 29

Cyber-Physical Systems Design in An Uncertain Environment wi...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Chen, Si Huang, Lida Xiao, Xiongren Liu, Yan Xie, Guoqi Li, Renfa Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Changsha China Research Institute of Hunan University in Chongqing Center for Convergence of Automobile and Cyberspace Chongqing China

ISBN: (纸本)9798350330717

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm. © 2023 IEEE.

关键词： Cyber-physical systems (CPSs) design multiple processors system on chip (MPSoC) partitioning time uncertainty uncertain environment

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