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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Peng Che Zhenlian Peng Buqing Cao Jianxun Liu Tieping Chen Runqing Fan School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Physical Education Hunan University of Science and Technology Xiangtan China

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %.

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Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders

Robust and Unsupervised KPI Anomaly Detection Based on Highl...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shili Yan Bing Tang Qing Yang Yijia He Xiaoyuan Zhang Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China Beijing Jiaotong University Beijing China

To ensure the stability and reliability of service quality, large Internet companies need to closely monitor various KPIs (key Performance Indicators, such as network throughput, CPU usages) and trigger timely troubleshooting or mitigation when any anomaly occurs. However, the diversity and complexity of anomalies bring great challenges to this work, especially when there is no manual label and low delay is required. In this paper, we propose HS-VAE (Highly Sensitive Variational Auto-Encoders), an unsupervised, robust algorithm based on CVAE (Conditional Variational Auto-Encoders) with high sensitivity to anomalies for KPIs, which contains mainly 3 parts: a simple but important data filter before training, improved conditional VAE with two dropout layers and adjusted anomaly detection method based on reconstruction probability. Our experiments using real-world data show that, HS-VAE's best F1-score ranges from 0.91 to 0.98. In addition, HS- VAE is excellent in sensitivity to anomaly and works well even with low latency requirements.

关键词： Training Sensitivity key performance indicator Training data Manuals Throughput Reliability

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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IEEE International Conference on Web services (ICWS)

作者： Yi Liu Jianxun Liu Xiangping Zhang Haize Hu Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Informationn Hunan University of Humanities Science and Technology Loudi China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today’s integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task.

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A Web service Classification Method Based on Graph Neural Network Knowledge Distillation

A Web Service Classification Method Based on Graph Neural Ne...

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

作者： Hao Huang Buqing Cao Shanpeng Liu Dong Zhou Mingdong Tang Feng Xiao School of Computer Science and Engineering & Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Xiangtan China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China

With the increasing number of Web services, service discovery becomes a significant challenge, and service classification methods based on Graph Neural Network (GNN) are considered to be an efficient way for service discovery. However, these methods rarely exploit the structure-based and feature-based prior knowledge of services. To address this problem, this paper proposes a Web service classification method based on graph neural network knowledge distillation. In this method, firstly, it employs Word2Vec to extract the semantic feature vector from service descriptions, and then builds the service relationship network based on the shared tag annotation relationship of service. Secondly, a graph neural network knowledge distillation framework for Web service classification consisting of a teacher model and a student model is designed. The teacher model extracts the knowledge of GNN model, and the student model obtains the structure-based and feature-based prior knowledge of the service relationship network from teacher model by neighbor feature aggregation and feature transformation. Finally, GNN models (GCN, GAT, SAGE, APPNP, SGC, and GCNII) as teacher models for accuracy comparison are used in the experiment. From the experiment result, the classification accuracy of the student model is 11.64%~23.10% higher than that of the teacher model.

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Deep Learning-based Image Text Processing Research*

Deep Learning-based Image Text Processing Research*

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Huixuan Xiong Kai Jin Jingnian Liu Jiahong Cai Lijun Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning Image forensics Recurrent neural networks Computational modeling Text categorization Feature extraction Data models

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Fast RFID Polling Protocols

Fast RFID Polling Protocols

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International Conference on Parallel Processing (ICPP)

作者： Jia Liu Bin Xiao Xuan Liu Lijun Chen State Key Laboratory for Novel software Technology Nanjing University China Department of Computing The Hong Kong Polytechnic University China College - of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9781509028245

Polling is a widely used anti-collision protocol that interrogates RFID tags in a request-response way. In conventional polling, the reader needs to broadcast 96-bit tag IDs to separate each tag from others, leading to long interrogation delay. This paper takes the first step to design fast polling protocols by shortening the polling vector. We first propose an efficient Hash Polling Protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than log(n) bits (n is the number of tags). We then enhance HPP (EHPP) to make it not only more efficient but also more steady with respect to the number of tags. To avoid redundant transmissions in both HPP and EHPP, we finally propose a Tree-based Polling Protocol (TPP) that reserves the invariant portion of the polling vector while updates only the discrepancy by constructing and broadcasting a polling tree. Theoretical analysis shows that the average length of the polling vector in TPP levels off at only 3.44, 28 times less than 96-bit tag IDs. We also apply our protocols to collect tag information and simulation results demonstrate that our best protocol TPP outperforms the state-of-the-art information collection protocol.

关键词： Protocols Indexes RFID tags Broadcasting Wireless communication Delays

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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Let's work together: Fast tag identification by interference elimination for multiple RFID readers

Let's work together: Fast tag identification by interference...

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International Conference on Network Protocols

作者： Xuan Liu Bin Xiao Feng Zhu Shigeng Zhang College of Computer Science and Electronic Engineering Hunan University P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory for Novel Software Technology Nanjing University P.R. China School of Information Science and Engineering Central South University P. R. China

ISBN: (纸本)9781509032822

Fast tag identification is a fundamental challenging problem in multi-reader RFID systems. The challenge is how to effectively handle Reader-Tag (RT) collisions and Reader-Reader (RR) collisions among adjacent readers. These collisions are caused by interfering signals simultaneously transmitted by the readers, and may disallow adjacent readers to work together. Prior works tackle this problem by scheduling adjacent readers to work in different time slots. The readers that are selected to simultaneously work, however, are usually only a small proportion of the total readers. This greatly restricts the identification throughput. Our insightful investigation on the current tag identification protocol reveals that RT collisions are caused by asynchronous actions of readers. i.e., a reader transmits signal while its adjacent readers receive. We thus develop Slot Splitting, a technique that can completely eliminate RT collisions by synchronizing actions of readers. We also propose a reader selection algorithm that minimizes RR collisions by selecting a reader set with maximum reading efficiency. The combination of these new techniques inspires Federal, a Fast and efficient tag identification protocol with interference-elimination-based reader scheduling. To our knowledge, Federal is the first identification protocol that completely eliminates RT collisions and minimizes RR collisions in both regularly and randomly deployed multi-reader systems. We validate the feasibility of Slot Splitting with experiments on the USRP platform and evaluate the performance of Federal through extensive simulations. The results show that Federal can increase tag identification throughput by up to 218% compared with the state-of-the-art work.

关键词： Protocols Throughput Radiofrequency identification Interference Radiation detectors Conferences Monitoring

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EHKP-Res: An explainable dynamic security solution for medical healthcare

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Digital Communications and Networks 2024年

作者： Xiaoyan Chen Jiahong Cai Weidong Xiao Yingzi Huo Jin Wang Wei Liang School of Software Engineering Xiamen University of Technology Xiamen 361024 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China

An increasing number of medical institutions and patients are adopting the practice of storing their data on medical cloud servers, which greatly facilitates the sharing of medical data. However, when many nodes are connected to the medical big data architecture, the overall system's security can be compromised. To address this, an explainable dynamic security protection scheme for medical healthcare, named EHKP-Res, has been proposed. This scheme aggregates data relevance features and integrates them with a deep residual network to predict the behavior of medical staff, addressing the issue of sudden changes in their behavioral credibility. To tackle the black-box problem, a model post-interpretation scheme using Bayesian networks to generate perturbed datasets has been proposed. It calculates sample weights and performs nonlinear fitting, deriving model interpretation through the eigenvalues of the instances. Experimental results show that the proposed Hidden KP-ABE method reduces time overhead by 14.95% compared to other access control methods and achieves a prediction accuracy of 98.34% for doctor trust metrics, effectively preventing malicious behavior by doctors.

关键词： Attribute-Based Encryption Behavior Prediction Deep Residual Network (ResNet) Explainable Artificial Intelligence Local Interpretable Model-agnostic Explanations (LIME)

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MSEConv: A Unified Warping Framework for Video Frame Interpolation

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Xiangling Ding Pu Huang Dengyong Zhang Wei Liang Feng Li Gaobo Yang Xin Liao Yue Li Hunan University of Science and Technology Xiangtan China Changsha University of Science and Technology Changsha China Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China Hunan University Changsha China University of South China Hengyang China

Within the context of video frame interpolation, complex motion modeling is the task of capturing, in a video sequence, where the moving objects are located in the interpolated frame, and how to maintain the temporal consistency of motion. Existing video frame interpolation methods typically assign either a fixed size of the motion kernel or a refined optical flow to model complex motions. However, they have the limitation of data redundancy and inaccuracy representation of motion. This paper introduces a unified warping framework, named multi-scale expandable deformable convolution (MSEConv), for simultaneously performing complex motion modeling and frame interpolation. In the proposed framework, a deep fully convolutional neural network with global attention is proposed to estimate multiple small-scale kernel weights with different expansion degrees and adaptive weight allocation for each pixel synthesis. Moreover, most of the kernel-based interpolation methods can be treated as the special case of the proposed MSEConv, thus, MSEConv can be easily transferred to other kernel-based frame interpolation methods for performance improvement. To further improve the robustness of motion occlusions, an operation of mask occlusion is introduced. As a consequence, our proposed MSEConv shows strong performance on par or even better than the state-of-the-art kernel-based frame interpolation works on public datasets. Our source code and visual comparable results are available at https://***/Pumpkin123709/MSEConv.

关键词： Video Frame Interpolation Unified Framework Complex motion Modeling Mask Occlusion

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