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检索条件"机构=Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing"

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Reconstruction Algorithm of Heterogeneous network data Flow in Smart Factory Based on SDN 14

Reconstruction Algorithm of Heterogeneous Network Data Flow ...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Peng, Jiajia Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

In smart factories, the wide application of a variety of intelligent manufacturing devices and advanced sensors leads to the increasing demand for data exchange between different devices. In order to realize the flexible forwarding of multi-source manufacturing data in heterogeneous networks and the optimal utilization of network resources, in this article, we combined smartphone manufacturing scenarios, referred to the data flow reconstruction algorithm in related documents, analyzed the high dynamics of the data flow and proposed a data flow reconstruction algorithm based on routing-protocol, according to the particularity of different data forwarding and processing procedures, redesigned and defined the data flow in the scene. Finally, the results of Mininet simulation experiments showed that our proposed algorithm reduces the average transmission delay. © 2022 IEEE

关键词： Heterogeneous networks

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A compound fault diagnosis method based on Multi-task Learning with Multi-gate Mixture-of-Experts 14

A compound fault diagnosis method based on Multi-task Learni...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Ren, Lele Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

At present, the research object of fault diagnosis focuses on single fault diagnosis. This method of diagnosis does not consider the relationship between mechanical equipment faults, and cannot fully identify the type of fault. In the actual industrial process, mechanical equipment is mostly complex system, and compound fault diagnosis is more in line with industrial needs. In order to solve the problem of compound fault diagnosis of mechanical equipment and improve the accuracy of fault identification, this paper establishes a compound fault diagnosis model for gearbox by means of multi-task learning, uses multi-gated networks to extract features from the task sharing layer, and finally classifies the fault through the task specific *** experimental results show that, the compound fault diagnosis method based on Multi-task Learning with Multi-gate Mixture-of-Experts (ML-MMoE) can accurately identify compound fault types under different working conditions. © 2022 IEEE

关键词： Failure analysis

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Moving Forward Together: A Multi-User Pattern Extraction Neural Model for Sequential Recommendations 23

Moving Forward Together: A Multi-User Pattern Extraction Neu...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Xiangpeng Du Xiaoge Li Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The sequential recommendation is a very important task in recommendation systems. The aim of it is to dynamic predict user's interests based on their historical behaviors. Despite recent progress, most of deep learning approaches focus on extracting the behavioral patterns of a single user. Pulling multiple users can increase the diversity of historical information. In this paper, we propose multi-user behavioral pattern extraction model (TMGN) via a combination of traditional behavioral pattern extraction and graph convolutional aggregation. Concretely, TMGN first learns the graph structure of users and items. Then it performs graph convolution aggregation to derive a representation of users to extract user interest features. We recall the users with the highest similarity by calculating the cosine similarity of each candidate user. Finally, we inject multiple users' sequence patterns and time information into the muti-head attention mechanism. Experiments on actual data (Movielens1m, Movielen20m and Amazon beauty) show that the proposed method outperforms current state-of-the-art sequential recommendation methods. TMGN achieves an average improvement of 5.67%, 5.75%, 7.32%, and 7.19% over the most robust baseline in Hit@5, Hit@10, NDCG@5, and NDCG@10, respectively. We provide extensive ablation studies to analyze the impact of each model component on TMGN.

关键词： Graph Convolutional network Self-attention Sequential Recommendation Sequential prediction Temporal Embedding

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Plagiarism detection of multi-threaded programs using frequent behavioral pattern mining 32

Plagiarism detection of multi-threaded programs using freque...

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32nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2020

作者： Wang, Qing Tian, Zhenzhou Gao, Cong Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China College of Information Sciences and Technology Pennsylvania State University PA United States

ISBN: (纸本)1891706500

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program's behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multithreaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth cope better with multi-threaded plagiarism detection than alternative approaches. © 2020 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Towards fine-grained compiler identification with neural modeling 32

Towards fine-grained compiler identification with neural mod...

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32nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2020

作者： Xie, Borun Tian, Zhenzhou Gao, Cong Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China College of Information Sciences and Technology Pennsylvania State University PA United States

ISBN: (纸本)1891706500

Different compilers and optimization levels can be used to compile the source code. Revealed in reverse from the produced binaries, these compiler details facilitate essential binary analysis tasks, such as malware forensics and binary code similarity analysis. Most existing approaches adopt a signature matching based or machine learning based strategy to identify the compiler details, showing limits in either the detection accuracy or granularity. In this work, we propose NeuralCI (Neural modeling-based Compiler Identification) to perform the identification of compiler family and optimization level on individual functions. The basic idea is to formulate sophisticated neural networks to process abstracted instruction sequences generated using a lightweight function abstraction strategy. To evaluate the performance of NeuralCI, a large dataset consisting of 413,119 unique functions collected from real-world projects is constructed. The experiments show that NeuralCI achieves over 99% and 90% accuracy in identifying the compiler family and optimization level respectively, which outperforms most state-of-the-art function level compiler identification methods. Also, we explore for the first time the possibility of conducting compiler identification on binary code snippets rather than complete functions, where NeuralCI still achieves 96% accuracy, indicating its ability of capturing subtle yet significant features. © 2020 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Binary codes

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A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit 14

A Load Balancing Strategy Of "Container Virtual Machine" Clo...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Xia, Hong Liu, MengDi Chen, YanPing Jin, XiaoMin Wang, ZhongMin Wang, FengWei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-tech Road South Shenzhen518057 China

ISBN: (纸本)9781665499781

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine;Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost. © 2022 IEEE

关键词： Containers

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Visual Question Answering Model Based on Relationship graphs and Co-Attention 23

Visual Question Answering Model Based on Relationship graphs...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Xueli Yang Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Visual Question Answering is a very challenging tasks in the fiield of AI. The existing model is not accurate enough to judge the relationship between problem words, image objects and the association between problem and image objects, meanwhile, the interpretability of the model needs to be improved. In response, this paper proposes a visual question answering model based on relationship graph and co-attention. Relational features can be obtained by constructing a relational graph using the relationships between the problem and the image, and between the objects of the image, and by dynamically updating the relational graph using a graph attention network guided by the problem. Then, the obtained features are fused using co- attention to obtain richer ***, these features are used to make answer prediction. The model is experimented on the VQA2.0 dataset, and the experimental results show that the model improved the accuracy of the visual question answering model to some extent compared with the existing models.

关键词： Co-Attention Mechanism Relationship Graph,Feature Fusion Visual Question Answering

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Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks

Anomaly detection frameworks for outlier and pattern anomaly...

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International Conference on networking and network Applications (NaNA)

作者： Cong Gao Yanping Chen Zhongmin Wang Hong Xia Ning Lv Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xian China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

As a major form of data, the collection and analysis of time series have been widely used in many fields. In practice, wireless sensor network is a popular mechanism for data collection and it is suitable for time series. However, due to the diversity deployment area for a wireless sensor network, the external environmental factors pose a great challenge on the quality of data collected by a sensor node. Besides, the accuracy and reliability of collected data are also affected by internal factors of a sensor node and the performance of the whole wireless sensor network. Common internal factors are resource limitation such as battery capacity, computing ability, and memory size. Moreover, a wireless sensor network deployed for production use is very likely to suffer from malicious attacks from both inside and outside. Therefore, anomaly is inevitable for time series collected by a wireless sensor network. In the paper, we make an extensive review about two types of anomaly detection methods in time series for wireless sensor networks: outlier detection and pattern anomaly detection. For outlier detection, six categories of detection methods are elaborated. For pattern anomaly detection, two main ideas are discussed. Finally, we present the main drawbacks of existing approaches to outlier detection and pattern anomaly detection for time series. The possible future research directions are also summarized.

关键词： Wireless sensor networks Time series analysis Production data collection Environmental factors Reliability Anomaly detection

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Dialogue Generation for Heterogeneous Graph Neural on networks Incorporating External Knowledge 23

Dialogue Generation for Heterogeneous Graph Neural on Networ...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Shuaiqian Guo Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In recent years, with the improvement of hardware computing power and the rapid development of deep learning theory, the field of dialogue generation has also entered the era of deep learning. However, because it is difficult to generate content-rich and recovery statements by learning semantics only from dialogue corpus, many models are prone to generate responses that are weakly scalable to the topic of the input dialogue or generic responses that lack knowledge information. To address the above issues, a Heterogeneous Graph Neural network Incorporating External Knowledge Dialogue Generation Model (HEDG) is proposed. First, we build up the external knowledge into a knowledge graph, and find the relevant external knowledge in the knowledge graph according to the entities in the input conversation according to certain rules. This external knowledge and the historical conversation context are then used as inputs to the heterogeneous graph encoder, and the conversation content and the corresponding external knowledge are represented by a Heterogeneous Graph Neural network (HGNN). Finally, a decoder with Transformer structure as the main body is used to generate a response that is relevant to the conversation context with some knowledge extension by taking the encoded heterogeneous graphical representation as input. The experimental results show that the model has higher relevance and diversity compared with existing comparison models, and can effectively integrate external knowledge.

关键词： Dialogue Generation External Knowledge Heterogeneous Graph Neural network Knowledge Graph

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CA-WGE: A two-view graph neural network-based knowledge graph completion approach combining common sense perception 23

CA-WGE: A two-view graph neural network-based knowledge grap...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Xiaoru Lian Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The knowledge graph completion algorithm can make the knowledge graph more complete and is currently a research hotspot in the field of artificial intelligence. The knowledge graph completion model is mainly defined in three aspects, the way of negative example generation, the design of scoring function and the design of loss function. The previous knowledge graph completion models only rely on factual view data to predict the missing links between entities and ignore the valuable common sense knowledge, and there is invalid negative sampling in knowledge graph embedding techniques; on the other hand, the existing graph neural network-based knowledge graph embedding models mainly consider capturing the graph structure around entities, and the relational representation is only used to update the entity embedding, which may miss the potentially useful information about the relational structure of potentially useful information. To address the above challenges, this paper proposes a two-view graph neural network-based knowledge graph completion model combined with common sense awareness. Common knowledge is first automatically extracted from fact triples with entity concepts to facilitate high-quality negative sampling, and then positive and weighted negative triples are fed into the two-view graph neural network-based knowledge graph embedding model to capture entity- and relationship-centric graph structures and learn vector representations of entities and relationships, and then the learned entity and relationship representations are fed into a weighted score function to return the final the final score. Extensive experimental and ablation studies on four datasets, FB15K, FB15K237, NELL995, and DBpedia-242, show that the model achieves better performance compared to the state-of-the-art models.

关键词： Common Sense Awareness Graph Neural network Knowledge Graph Completion Knowledge Graph Embedding Negative Sampling

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