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International Conference on Communication Software and networks, ICCSN

作者： Zefang Bai Guijuan Wang Meijie Ma School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that $c \lambda_{3}^{h}\left(Q_{n}^{3}\right)=4 n h-$ $4 e_{h}\left(Q_{n}^{3}\right)-h$ for $1 \leq h \leq \frac{1}{2} \cdot 3^{\left\lceil\frac{h}{2}\right\rceil}, n \geq 3$. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3 -ary n-cubes for $g \in\left\{4,3^{c}+1\right\}, 1 \leq c \leq\left\lceil\frac{n}{2}\right\rceil-1, n \geq 3$.

关键词： Upper bound Multiprocessor interconnection Software Reliability

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Few-Shot Hyperspectral Image Classification with Spectral-Spatial Feature Fusion Based on Fuzzy Broad Learning System

Few-Shot Hyperspectral Image Classification with Spectral-Sp...

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IEEE International Conference on Image Processing

作者： Xiaopei Hu Guixin Zhao Aimei Dong Guohua Lv Yi Zhai Ying Guo Xiangjun Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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

In the few-shot hyperspectral image (HSI) classification, most current models don't fully utilize the advantage of spectral-spatial feature fusion, resulting in low classification accuracy. Therefore, we propose a few-shot HSI classification model with spectral-spatial feature fusion based on fuzzy broad learning system (FBLS) (FSFBLS). Firstly, we use a Gaussian filter to suppress noise while smoothing spectral features based on spatial information to achieve the first fusion of spectral-spatial features. Secondly, we use FBLS with fuzzy rules to fully model the complex mapping relationship between spectral-spatial features and HSI labels to complete HSI classification. The fuzzy processing can extract rich discriminative features to enhance the recognition of different categories. Finally, the guided filter corrects the misclassified samples of FBLS based on the guided image to achieve the second fusion of spectral-spatial features. Extensive experimental results on three public datasets demonstrate that FSFBLS achieves state-of-the-art classification performance compared to nine popular models.

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & Engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Joint Extraction of Entities and Relationships from Cyber Threat Intelligence based on Task-specific Fourier network

Joint Extraction of Entities and Relationships from Cyber Th...

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International Joint Conference on Neural networks (IJCNN)

作者： Haiqing Lv Xiaohui Han Hui Cui Peipei Wang Wenbo Zuo Yang Zhou 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 Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The increasing complexity of cyber threats and the emergence of new attack technologies have brought huge challenges to attack incident analysis and source tracing. Using cyber threat intelligence to build a Cyber security Knowledge Graph (CKG) provides a new technical solution for attack attribution. Constructing a CKG requires numerous entity and relationship triples extracted from unstructured cyber threat intelligence texts. However, existing entity and relationship joint extraction methods in cyber threat intelligence face two problems. Firstly, they share the same word embeddings for both subtasks, ignoring the fine-grained semantic differences between the subtasks. Secondly, they rarely consider the interaction between the features of the two subtasks, which is vital for capturing the semantic dependencies between the tasks. To address these issues, we propose a joint entity and relationship extraction model specifically designed for network security concepts. We utilize two lightweight Fourier networks with independent weights to build a feature extraction module for encoding fine-grained features for entity recognition and relationship extraction tasks. Furthermore, we use a subtask feature interaction strategy assisted by a gated attention mechanism to enhance feature interaction between entity recognition and relationship extraction tasks. Use fine-grained entity recognition task information to guide relationship extraction to capture semantic dependencies between tasks. Experimental results on a cyber threat intelligence dataset demonstrate that our model outperforms existing baselines.

关键词： Attention mechanisms Face recognition Semantics Neural networks Knowledge graphs Logic gates network security

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Ajsage: A Intrusion Detection Scheme Based on Jump-Knowledge Connection to Graphsage

SSRN

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SSRN 2024年

作者： Xu, Lijuan Zhao, ZiCheng Zhao, Dawei Li, Xin Lu, XiYu Yan, DingYu Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250014 China National Computer Network Emergency Response Technology Processing Coordination Center Jinan250014 China

In the field of network security, attackers often utilize Advanced Persistent Threats (APT) to conduct host-based intrusions for prolonged information gathering, penetration and to cause serious damages. Recent studies have used provenance data containing rich contextual information to achieve effective detection of host-based APT. Extracting system entities (e.g., processes, files) and operations between entities in provenance data to construct a directed acyclic graph (DAG) is the key to realize attack detection by provenance graph. Previous studies extracted the features of the whole provenance graph, which did not fully capture the relationship between the nodes in the graph, and the extracted features were not accurate enough. Moreover, the original node feature information may be lost in the process of aggregation. Therefore, abnormal nodes are recognized in the detection process, leading to low detection performance and a high false alarm rate. Facing the challenge, we introduce AJSAGE, a framework based on graph neural networks. A novel anomaly detection method by adding attention mechanism and Jump-Knowledge Connection to GraphSAGE. It enables the integration of node information across hierarchical levels, improves the detection of complex attack patterns, and enhances the accuracy and generalization of the model in node feature representation. It is able to identify features and nodes that are closely related to the anomaly detection task in a more focused manner. We evaluate the performance of AJSAGE on three publicly available datasets, and the results demonstrate that it significantly outperforms multiple state-of-the-art methods for host intrusion detection. © 2024, The Authors. All rights reserved.

关键词： Intrusion detection

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Large Model-Based Data Augmentation for Imbalanced Text Classification

Large Model-Based Data Augmentation for Imbalanced Text Clas...

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Artificial Intelligence, networking and Information technology (AINIT), International Seminar on

作者： Dawei Zhang Rongxin Mi Peiyao Zhou Dawei Jin Manman Zhang Tianhang Song School of Computer Science Jiangsu University of Science and Technology Zhenjiang Jiangsu China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

This study focuses on the application of large models to deal with imbalanced data problems in text classification. In view of the central position of text in web data and the negative impact of class imbalance on classifier performance, researchers have explored the method of using large models to generate high-quality minority class samples to enhance model performance. This paper reviews the technical progress of machine learning, deep learning, and large language models and their applications in text classification tasks. Although large models perform well in complex tasks due to their excellent language understanding ability, traditional machine learning and deep learning methods are popular in text classification scenarios that require fast response due to their simple structure and higher computational efficiency. This study proposes a data augmentation technique inspired by SMOTE, which uses a large language model combined with a simple prompt engineering strategy to generate high-quality minority samples. The experimental results show that the proposed method significantly improves the macro average precision, recall and F1 score on multiple text classification models, and effectively alleviates the challenge of class imbalance.

关键词： Deep learning Seminars Computational modeling Large language models Text categorization Data augmentation Data models

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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PTGFI: A Prompt-Based Two-Stage Generative Framework for Function Name Inference

PTGFI: A Prompt-Based Two-Stage Generative Framework for Fun...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo 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 Quan Cheng Laboratory Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability computer crime Assembly Cybernetics

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Performance Analysis for Intrusion Target Detection in Wireless Sensor networks

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Chinese Journal of Electronics 2023年第4期20卷 725-729页

作者： FAN Gaojuan WANG Ruchuan HUANG Haiping SUN Lijuan XIAO Fu College of Computer Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Information Engineering Henan University Kaifeng China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education of Jiangsu Province Nanjing China

Target detection and field surveillance are among the most prominent applications of wireless sensor networks. The quality of detection achieved by a sensor network can be quantified by evaluating the probability of detecting a mobile target crossing a sensing field. Detection probability of sensor nodes has been studied in sensor networks for many purposes such as quality of service and decision making. However, the sensing capabilities of sensors are affected by environmental factors in real deployment. This paper investigates the problem of detecting probability in a log-normal shadow fading environment. It presents an analytic method to evaluate the detection probability by at least $k$ sensors under practical considerations. Furthermore, we also shows that shadow fading makes significant influence in detection probability compared to unit disk sensing model through extensive simulation experiments.

关键词： Industries Wireless communication Fading channels Wireless sensor networks Surveillance Object detection Quality of service

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A Multiple-Image Authentication Method Based on Phase-Only Hologram and Logistic Map

SSRN

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SSRN 2024年

作者： Liansheng, Sui Zishan, Jiang Haiwei, Lu Zhaolin, Xiao Tianyu, Zeng School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control Shijiazhuang050024 China

An interesting security method for multiple-image authentication scheme is proposed based on computer-generated holograms and logistic map. First, each original image is encoded as the complex-valued hologram under the point light source model. The resulting hologram is then converted to a phase-only hologram using the Floyd-Steinberg dithering algorithm. Second, each phase-only hologram is randomly sampled by the aid of a binary mask. Through the catenation of all selected pixels, a phase-only pixel sequence is formed. Finally, a non-periodic and non-converging sequence generated with logistic map is used to scramble this sequence. After only preserving the phase data of the scrambled sequence, the real-valued ciphertext carrying the information of all original images is obtained. In the process of authentication, although no valid information can be discerned from noisy reconstructed images at a small sampling rate, the verification of original images can be efficiently accomplished using the nonlinear correlation maps. Besides binary masks, the parameters of logistic map are served as secret keys. Due to their high sensitivity, the security of the proposed method is greatly enhanced. The proposed authentication mechanism has been demonstrated to be effective and robust through experiments. To our best knowledge, it is first time to implement multiple-image authentication using phase-only holograms, which can provide a new perspective for optical information security. © 2024, The Authors. All rights reserved.

关键词： Holograms

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