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Residual diverse ensemble for long-tailed multi-label text classification

Science China(Information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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Computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Heterogeneous UAV Resource Scheduling for Dynamic Time Sensitive Target Detection and Interference

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IEEE Internet of Things Journal 2025年第12期12卷 20030-20041页

作者： Wan, Liangtian Wang, Jiashuai Sun, Lu Li, Kuixian Xiong, Xuanrui Lin, Yun Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian116620 China Dalian Maritime University Institute of Information Science Technology Department of Communication Engineering Dalian116026 China Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this paper, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy Unmanned Aerial Vehicles (UAVs) to interfere with the targets. First, we established a UAV swarm task scheduling optimization model after considering constraints such as target threat range, priority of reconnaissance and interference tasks, interference task time, and UAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a non-dominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve UAV Resource Scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Environment-Tolerant Trust Opportunity Routing Based on Reinforcement Learning for Internet of Underwater Things

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6348-6360页

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

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Computer Modeling in engineering & Sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： Key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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An Iterative Deep Neural network Pruning Method Based on Filter Redundancy Assessment 5

An Iterative Deep Neural Network Pruning Method Based on Fil...

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5th International Seminar on Artificial Intelligence, networking and Information technology, AINIT 2024

作者： Lai, Xiaochen Wang, Lukang Zhang, Liyong School of Software Dalian University of Technology Dalian China Key Laboratory For Ubiquitous Network and Service Software of Liaoning Province Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350385557

Storage and inference of deep neural network models are resource-intensive, limiting their deployment on edge devices. Structured pruning methods can reduce the resource requirements for model storage and inference by selectively removing low-contributing filters. Current methods do not focus on the functional relations between filters and cannot remove filters that are scored as high-contributing but have redundant feature extraction capabilities. To address this limitation, we propose a structured pruning method, Important filter Decreasing Iteratively (IDI), which removes low-contributing filters and functionally redundant filters in two pruning stages, respectively. The first stage of the IDI removes low contribution filters using a filter contribution evaluation criterion. In the second stage, the Important filter Decreasing (ID) operation removes redundant filters with high functional similarity. Multiple ID operations are performed iteratively to gradually reduce the parameter size of the model without drastically breaking its performance. Experimental results show that compared with existing filter-level pruning methods, the compact model obtained by IDI has higher accuracy. IDI efficiently realizes the compression of deep neural networks. © 2024 IEEE.

关键词： Deep neural networks

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Tracking-Removed GRU with Denoising Autoencoder for Multivariate Time Series Imputation 4

Tracking-Removed GRU with Denoising Autoencoder for Multivar...

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4th International Conference on Neural networks, Information and Communication engineering, NNICE 2024

作者： Lai, Xiaochen Yao, Yachen Mu, Jichong Zhang, Liyong Dalian University of Technology School of Software Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China Jilin University School of Software Changchun China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9798350394375

We propose the Tracking-Removed Gated Recurrent Unit (TRGRU) with Denoising Autoencoder (DAE) for handling missing values in the incomplete multivariate time series. The internal network of TRGRU is replicated into several subnetworks, each tasked with reconstructing a corresponding attribute of the multivariate time series utilizing information from other attributes, which removes the attribute's self-tracking during the reconstruction. The output of TRGRU is the reconstruction of its input, enabling the utilization of observed values at the current time step in estimating missing values. These network groups share weights, leading to a reduction in overall network parameters. Additionally, we design an imputation unit based on DAE, which leverages historical information and the hidden features of input data to impute missing values within the input data and generate completed data for the training of TRGRU, enabling the model to be trained using incomplete datasets. Experimental results across multiple datasets have corroborated the effectiveness of TRGRU-DAE. © 2024 IEEE.

关键词： Time series

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A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

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Frontiers of Computer Science 2023年第4期17卷 127-139页

作者： Jian AN Siyuan WU Xiaolin GUI Xin HE Xuejun ZHANG School of Computer Science and Technology Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong UniversityXi’an 710049China School of Software Henan UniversityKaifeng 475001China School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive *** the limitation of calculation power,edge computing is introduced to release unnecessary data *** edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data *** involved in one task are required to cooperate to process the task *** privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and *** crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is *** mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality ***,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this ***(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in ***,the final result is calculated without revealing the privacy of *** on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

关键词： crowdsensing edge computing devices blockchain quality assessment reinforcement learning

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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