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International Conference on Computer and Communications (ICCC)

作者： Zhengyu Ju Zhao Li Ye Li Shiwei Wu Canjun Wang Shandong Academy of Sciences Qilu University of Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China

Over the past few years, "Pre-training + Fine-tuning" has been highly successful in Natural Language Processing (NLP) tasks, but it has limitations in few-shot tasks and zero-shot tasks. Recently, "Prompt-based Tuning + Pretrained Language models (PLMs)" has achieved great success in few-shot stage and zero-shot stage, including few-shot text classification. Prompt-based tuning wraps suggestive characters and [MASK] tokens into a raw input sequence. Additionally, Prompt learning uses a verbalizer to map the semantic information of the [MASK] position to the final classification prediction. The verbalizer contains manual verbalizer and automatic verbalizer, etc. However, manual verbalizers are too dependent on human experience and external knowledge, while automatic verbalizers still face significant challenges. In this work, we introduce a novel approach for constructing a verbalizer termed as “virtual class representation”. This method entails assigning a fixed-length sequence of digits to represent each label in the dataset. We train a small-scale randomly initialized embedding mechanism to achieve preliminary semantic information for each class when virtual class representation is introduced into the embedding mechanism. The semantic information of each category is then further extracted by a convolution operation, and the final model regression value is obtained by calculating the similarity between the last hidden state of PLM and the semantic vector of the class. Our extensive experiments demonstrate the effectiveness of the proposed method on few-shot text classification tasks.

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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Graph Learning-Based Multi-User Multi-Task Offloading in Wireless computing power networks

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IEEE Internet of Things Journal 2025年

作者： Dai, Yueyue Rao, Xiaoyang Gu, Bruce Qu, Youyang Yang, Huiran Lu, Yunlong Huazhong University of Science and Technology Research Center of6G Mobile Communications School of Cyber Science and Engineering Wuhan430074 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Australia University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences Beijing China and the School of Cyber Security Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

To enhance service quality, wireless computing power networks (WCPNs) need to realize flexible scheduling and allocation of computation resources across heterogeneous computing servers. Due to large user scales and diverse computation tasks, it is difficult for the current WCPN to serve multiple users and handle multiple tasks concurrently. Graph learning is a promising approach that can learn the representations of nodes through graph structures, enabling the exploration of dependencies among multiple users and tasks, and thereby facilitating computation task offloading. In this paper, we propose a graph learning-based multi-user multi-task offloading scheme for WCPN. First, we propose a wireless computing power network with multi-user and multi-task in which users need to make full use of distributed computing resources through task offloading to ensure efficient task execution. We formulate a system energy consumption minimization problem to jointly optimize computation resources, transmission power, and task offloading. To address the problem, we utilize graph learning to transform the joint optimization problem into a graph regression problem and leverage line graph to explore the solution. Numerical results demonstrate that our proposed scheme can improve computation efficiency, enhance optimization performance, and maintain transferability compared with the benchmarks. © 2014 IEEE.

关键词： computing power

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International Conference on Computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Map2Schedule: An End-to-End Link Scheduling Method for Urban...

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IEEE International Conference on Communications (ICC)

作者： Lihao Zhang Haijian Sun Jin Sun Ramviyas Parasuraman Yinghui Ye Rose Qingyang Hu School of Electrical and Computer Engineering University of Georgia Athens GA USA School of Computing University of Georgia Athens GA USA Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China Electrical and Computer Engineering Department at Utah State University Logan UT USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

关键词： Wireless communication Accuracy Processor scheduling Computational modeling System performance Urban areas Vehicular ad hoc networks

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Active power Control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control power control Rotors power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation

FEST: Feature Enhancement Swin Transformer for Remote Sensin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The global context is crucial for the precise segmentation of remote sensing images. However, the large volumes and high spatial resolutions of remote sensing images make efficient analysis of the entire scene challenging for most convolutional neural network (CNN)-based methods. To address this issue, we propose to design an innovative framework for semantic segmentation of remote sensing images called Feature Enhancement Swin Transformer (FEST). Firstly, we utilize the Swin Transformer as the encoder and incorporates a Global information Enhancement Model (GIEM) within each Swin Transformer block to reduce information loss and enable encoding of more accurate spatial information. Secondly, we introduce an enhanced decoding structure called Enhanced Feature Fusion Module (EFFM) with added enhanced channel and spatial attention modules to retain localized information while obtaining extensive contextual information. Finally, for loss calculation, we utilize the dice and cross-entropy loss to jointly supervise the model, aiming to achieve a competitive performance. We comprehensively evaluated FEST on the ISPRS-Vaihingen and Potsdam datasets. The results indicate that our approach has achieved significant improvements in semantic segmentation tasks compared to existing methods.

关键词： Deep learning Federated learning Semantic segmentation Semantics Transformers information retrieval Encoding

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing information retrieval Data mining Task analysis

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FedLRDP: Federated Learning Framework with Local Random Differential Privacy

FedLRDP: Federated Learning Framework with Local Random Diff...

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International Joint Conference on Neural networks (IJCNN)

作者： Runtian Zhou Anming Dong Jiguo Yu Qingyan Ding Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.

关键词： Training Differential privacy Privacy Accuracy Federated learning Noise Data models

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Data-Driven Active power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems power smoothing Wind farms Linear programming power grids

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