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IEEE International Conference on Big Data

作者： Wendan Cheng Zhen Zhang Heng Qian Qiuyue Wang Guanqun Su Lingge Meng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shu Ju (Shandong) Intelligent Technology Co. Ltd. Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Qingniao IIoT Co. Ltd. Jinan China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial Data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial Data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies.

关键词： Adaptation models Analytical models Protocols Knowledge graphs Data collection Ontologies Data models Cognition Natural language processing Standards

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Generalization Boosted Adapter for Open-Vocabulary Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenhao Wang, Changwei Feng, Xuxiang Xu, Rongtao Huang, Longzhao Zhang, Zherui Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The 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 Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China The Aerospace Information Research Institute Chinese Academy of Sciences China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging due to their lack of pixel-level granularity and the limited data available for fine-tuning, leading to overfitting and poor generalization. To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation. GBA comprises two core components: (1) a Style Diversification Adapter (SDA) that decouples features into amplitude and phase components, operating solely on the amplitude to enrich the feature space representation while preserving semantic consistency;and (2) a Correlation Constraint Adapter (CCA) that employs cross-attention to establish tighter semantic associations between text categories and target regions, suppressing irrelevant low-frequency "noise" information and avoiding erroneous associations. Through the synergistic effect of the shallow SDA and the deep CCA, GBA effectively alleviates overfitting issues and enhances the semantic relevance of feature representations. As a simple, efficient, and plug-and-play component, GBA can be flexibly integrated into various CLIP-based methods, demonstrating broad applicability and achieving state-of-the-art performance on multiple open-vocabulary segmentation benchmarks. © 2024, CC BY.

关键词： Semantics

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 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 Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

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Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong 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

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm.

关键词： Patents Vocabulary Technological innovation Accuracy Semantics Predictive models Writing Prediction algorithms Data mining Robotics and automation

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Android Malware Detection Method Based on Graph Convolutional Networks

Android Malware Detection Method Based on Graph Convolutiona...

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Artificial Intelligence and power Systems (AIPS), International Conference on

作者： Qingling Xu Shumian Yang Lijuan Xu Dawei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (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

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Facing the serious security threats posed by Android malware amidst the rapid development of mobile internet, there is an urgent need to develop more accurate and efficient detection technologies. This paper introduces an innovative Android malware detection solution based on graph neural networks(GCN). The solution centers around function call graphs as core features, incorporating sensitive API identification and node importance analysis to streamline redundant information and reinforce key feature representation. Leveraging graph neural network models, complex call graphs are efficiently encoded into low-dimensional features, revealing the deep structure of malicious code. This method not only significantly improves detection accuracy to 99% but also drastically reduces detection time while ensuring performance, demonstrating its strong advantage in Android malware detection and classification tasks.

关键词： Training Accuracy Costs Graph convolutional networks Biological system modeling Feature extraction Malware power systems Complexity theory Security

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A Novel Collaborative Edge Caching via User Preference Awareness and Adaptive Clustering in MENs

A Novel Collaborative Edge Caching via User Preference Aware...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Pengfei Gu Huiling Shi Hao Hao Mingfa Li Wei Zhang 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

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

In this paper, we propose a collaborative caching placement scheme leveraging user preference awareness and adaptive clustering (UPAAC), which jointly considers the heterogeneity of user preferences and user activity to reduce user access latency and improve the quality of service. Specifically, we first utilize Bidirectional Long Short-Term Memory (Bi-LSTM) to capture dynamic user behavior to predict user preferences, and then density-based spatial clustering of applications with noise (DBSCAN) is used to cluster users based on the predicted user preferences for more efficient cache placement. Furthermore, the cache placement optimal problem is defined as the latency reduction maximization problem under the constraint of the cache capacity and latency threshold, and a genetic algorithm (GA) is used to optimize the problem and design the cache placement strategy. The simulation outcomes indicate that the suggested approach outperforms the conventional caching methods by significantly decreasing content access latency and enhancing the cache hit ratio.

关键词： Noise Collaboration Quality of service Prediction algorithms Smart systems Probability distribution Timing History Long short term memory Genetic algorithms

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enCBS: Delay Guarantee Mechanism based on Credit-based Shaping for Programmable Switch

enCBS: Delay Guarantee Mechanism based on Credit-based Shapi...

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Xinhang Wang Lizhuang Tan Wei Zhang 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

The Time-Sensitive Networking (TSN) aims to achieve delay guarantee through traffic shaping, and the programmable switch provides a data plane implementation solution for the flexible and deployable TSN network. However, the built-in quality of service guarantee mechanism of the current programmable switch cannot satisfy the deterministic guarantee of queue control and end-to-end delay of TSN network. Credit-based Shaping (CBS) is a common traffic shaping scheme for static TSN network, and it is helpless in the face of topology changes and dynamic entry/exit of flows. In this paper, we design the enhance CBS (enCBS), a CBS-based delay guarantee mechanism on the programmable switch, using the advantages of Software-Defined Network (SDN) architecture to optimize flow management and parameter configuration. In addition, we improved the traditional CBS and strengthened the packet interval constraints. The experimental results prove that, compared with the existing quality of service control mechanism of programmable switch, the delay guarantee mechanism based on CBS shaping has better delay guarantee performance.

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TIBS: A Deep-Learning Model for Network Intrusion Detection for SDN Environments

TIBS: A Deep-Learning Model for Network Intrusion Detection ...

引用

International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yilin Zhang Xiaoming Wu Huomin Dong Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Ministry of Education 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

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Software-Defined Networking (SDN) is a modern network strategy that replaces traditional network structures. Its management efficiency and convenience are significantly higher than traditional networks. However, it should be pointed out that the centralized control mechanism adopted in SDN may increase the risk of single points of failure, making it more susceptible to different types of network attacks, such as Distributed Denial of service (DDoS), DoS, PortScan and Brute Force etc. The identification and defense of these attacks are complex and require effective preventive measures. Network intrusion detection technology increasingly plays an irreplaceable role in network security. Therefore, this paper proposes an intrusion detection method for SDN environments. The system covers multiple stages, each with unique functions and roles. First, it is proposes an imbalanced data oversampling method based on improved Auxiliary Classifier Generative Adversarial Networks (ACGAN) to solve the problem of data imbalance. At the same time, an intrusion detection model TIBS based on Transformer and Inception-BiGRU-SA network is proposed. First, the Encoder part of Transformer is used to capture the connection globally and perform preliminary feature extraction on the input data. Secondly, the improved Inception module is used to extract multi-scale features. Extract, use the self-attention (SA) to weight the extracted spatial features of different scales, use bidirectional gated recurrent unit(BiGRU) to improve the model's ability to extract temporal features, and use the Softmax activation function for final classification. The experiment uses the CIC-IDS-2017 dataset and CIC-DDoS-2019 dataset. The experimental results show that the proposed method has high accuracy and robustness in intrusion detection tasks, and can better identify various types of attack behaviors than traditional methods. And provides more accurate prediction results. Therefore, this method has potential

关键词： Accuracy Computational modeling Network intrusion detection Network security Feature extraction Transformers Data models

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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

引用

Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text; The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy; Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance.

关键词： Patents Accuracy Fuses Splicing Feature extraction Transformers Search problems Vectors Data models Data mining

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Malicious Traffic Classification for IoT based on Graph Attention Network and Long Short-Term Memory Network

Malicious Traffic Classification for IoT based on Graph Atte...

引用

Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Lei Zhang Lizhuang Tan Huiling Shi Hongyang Sun Wei Zhang 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 Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

With the rapid development of digital services and the internet of Things (IoT), the number and diversity of cyber attacks have reached unprecedented levels, making network traffic classification a crucial means of maintaining network security. However, in the face of increasingly complex traffic attacks, traditional analysis of individual traffic flows is no longer sufficient for effective detection. Therefore, to reveal the potential correlations between network traffic flows, we propose a novel IoT malicious traffic classification model called EGAT-LSTM. This model combines an improved Graph Attention Network (GAT) with Long Short-Term Memory (LSTM) network to effectively capture the spatial topology and temporal features of network traffic, ultimately achieving efficient classification. Moreover, we optimize the aggregation function of network node features within the attention mechanism to effectively address the issue of essential information retention during the feature learning process. The experimental results on two publicly available IoT datasets demonstrate that our method achieves improvements in both binary and multi-class classification tasks, exhibiting significant advantages over some current approaches.

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