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Stigmergic Collaboration: A Sign-Based Perspective of Stigmergy

Stigmergic Collaboration: A Sign-Based Perspective of Stigme...

2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

作者： Chuanjun Ren Hongbing Huang Shiyao Jin National Laboratory for Parallel & Distributed Processing College of Computer Science National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781424472796

Insects build architecturally complex nests and search for remote food by collaboration work despite their limited sensors, minimal individual intelligence and the lack of a central control system. Insets' collaborations emerge as a response of the individual insects to Stigmergy. A sign-based model of Stigmergy to discuss collaboration is proposed in this paper where we picked up "sign" as a key notion to understand it. Therefore, sign is the link of all the components in a Stigmergic complex adaptive system. Based on this understanding, we propose a definition that reveals the nature of signs and exploit the significations and relationships carried by the notion of sign. Then, a sign-based model of Stigmergy is consequently reached, which captures the essentials of Stigmergy. A basic architecture of Stigmergy as well as its constituents are presented and discussed. At last, some applications of the model are discussed.

关键词： Stigmergy Sign Emergence Collaboration CAS

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Properties and applications of octonion fractional Fourier transform for 3-D octonion signals

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Digital Signal processing 2025年 165卷

作者： Xi Yang Qiang Feng Nan Jiang Mohammad Younus Bhat Didar Urynbassarova School of Mathematics and Computer Science Yanan University Yanan Shanxi 716000 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan Shanxi 716000 China Department of Mathematical Sciences Islamic University of Science and Technology Kashmir 192122 India National Engineering Academy of the Republic of Kazakhstan Almaty 050000 Kazakhstan

In the field of signal processing, especially when dealing with complex signals and multidimensional data analysis requirements, traditional transformation methods often struggle to meet practical application needs. Although the Fourier transform and its derivative methods (such as the fractional Fourier transform) play an important role in signal processing, these methods have obvious limitations in handling high-dimensional non-stationary signals, particularly in capturing the intrinsic structure and time-frequency characteristics of multidimensional signals. To address this issue, this paper proposes a new signal processing tool: the octonion fractional Fourier transform (OFRFT). By systematically studying the differential properties of OFRFT and establishing its convolution theorem, the application of this method in multi-dimensional linear time-invariant systems, including series, parallel, and feedback connections, is thoroughly discussed. Simulation experiments based on octonion signals further verify the practical application value of OFRFT in signal processing. The results demonstrate that OFRFT not only provides a novel theoretical tool for analyzing the time-frequency characteristics of complex signals but also shows significant advantages in non-stationary signal processing, laying an important foundation for theoretical research and engineering applications in related fields.

关键词： Fourier series

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Surrogate Supervision-based Deep Weakly-supervised Anomaly Detection

Surrogate Supervision-based Deep Weakly-supervised Anomaly D...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhiyue Wu Hongzuo Xu Yijie Wang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665424288

Many anomaly detection applications can provide partially observed anomalies, but only limited work is for this setting. Additionally, a number of anomaly detectors focus on learning a particular model of normal/abnormal class. However, the intra-class model might be too complicated to be accurately learned. It is still a non-trivial task to handle data with anomalies/inliers in skewed and heterogeneous distributions. To address these problems, this paper proposes an anomaly detection method to leverage Partially Labeled anomalies via Surrogate supervision-based Deviation learning (denominated PLSD). The original supervision (i.e., known anomalies and a set of explored inliers) is transferred to semantic-rich surrogate supervision signals (i.e., anomaly-inlier and inlier-inlier class) via vector concatenation. Then different relationships and interactions between anomalies and inliers are directly and efficiently learned thanks to the neural network’s connection property. Anomaly scoring is processed via the trained network and the high-efficacy inliers. Extensive experiments show that PLSD significantly prevails state-of-the-art semi/weakly-supervised anomaly detectors.

关键词： Learning systems Conferences Detectors Gaussian distribution Benchmark testing Task analysis Anomaly detection

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection

Word Embedding-based Context-sensitive Network Flow Payload ...

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International Conference on Applied Machine Learning (ICAML)

作者： Yizhou Li Yijie Wang Li Cheng Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421263

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due to its various possible characters and complex semantic context, and thus identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which applies the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method.

关键词： Art Semantics Clustering algorithms Intrusion detection Machine learning Feature extraction Task analysis

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Fden: Mining Effective Information of Features in Detecting Network Anomalies

Fden: Mining Effective Information of Features in Detecting ...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Li Yijie Wang Mingyu Liu Kele Xu Zhongyang Wang Li Cheng Yizhou Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En pk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En pk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En pk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the Network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： Knowledge engineering Conferences Telecommunication traffic Signal processing Feature extraction Numerical models Security

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A Scalable and Flexible Monitoring System Framework for Supercomputers

A Scalable and Flexible Monitoring System Framework for Supe...

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2017 International Conference on computer, Electronics and Communication Engineering(CECE2017)

作者： Tong XIAO Kai LU College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Mankind's demand for more powerful computing capabilities is never met, which has led to the continuous improvement of supercomputers' performance. A more powerful supercomputer tends to have a larger system scale, which brings serious challenges to the system management, within which how to monitor the system's state is a critical problem. To address this problem, a scalable and flexible monitoring system framework for supercomputers is brought forward in this paper which can monitor supercomputers with tens of thousands of nodes effectively and efficiently. In this paper, we firstly give an overview of the framework and then focus on the Super computer System Description Language(SCSDL) which is key to the framework. In the end, we explain some techniques about implementing the framework, and the client GUIs of a job monitoring system and an error monitoring system for Tianhe-2 based on this framework are given, from which we can see that the framework is well scalable and flexible to monitor Tianhe-2 which has 16,000 nodes effectively and efficiently.

关键词： Monitoring system Framework Supercomputer Scalable Flexible

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Implementation of encrypted data for outsourced database

Implementation of encrypted data for outsourced database

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International Conference on Computational Intelligence and Natural Computing, CINC

作者： Zheng-Fei Wang Ai-Guo Tang National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Computer Hunan Business College Changsha China

ISBN: (纸本)9781424477050

Encryption technology has become an important mechanism of securing data stored in the outsourced database. However, it is a difficulty to query efficiently the encrypted data and many researchers take it into consideration. To solve the problem, an encrypted schema, based on the Postgresql DBMS, is proposed. Through the security dictionary and the extended SQL, the approach implements the encrypted storage and efficiently query over the encrypted data in the outsourced databases. Results of experiments validate the efficiency and feasibility of our approach.

关键词： Indexes Dictionaries Random access memory Catalogs Encryption

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An Irregular cutting based distributed mapping algorithm for DVE systems

An Irregular cutting based distributed mapping algorithm for...

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Wei Zhang Hangjun Zhou Yuxing Peng Sikun Li Min Xi National Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China Department of Computer Science Xi'an Jiaotong University Xi'an China

distributed Virtual Environment (DVE) systems have become more and more important both in academic communities and the industries. To guarantee the load constrain, the physical world integrity and the virtual world integrity, the virtual environment should be divided into several parts and mapped to multiple servers, which are located in geographically different places. Many centralized algorithms based on regular cutting method are proposed to solve this problem. However, the dynamic property of clients and distributed property of the system make the existing algorithms unable to undertake it. In this paper, we propose a metric to measure the cutting quality and design a distributed mapping algorithm based on an irregular cutting. Simulation results show that our algorithm greatly decreases the costs of DVE systems for migration of clients and effectively improves the satisfaction degree of users.

关键词： Defense industry Virtual environment computer industry computer science Algorithm design and analysis Delay Partitioning algorithms Industrial training Computational intelligence Application software

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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SCQR - A P2P Query Routing Algorithm Based on Semantic Cluster

SCQR - A P2P Query Routing Algorithm Based on Semantic Clust...

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International Symposium on Intelligent Ubiquitous Computing and Education

作者： Shiyao Jin Gui-Ming Zhu School of Computer Science National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

The low efficiency and uncertainty routing is correct is a problem for resource location of an unstructured p2p network. It is hard to achieve high query hit with small cost and low latency for such kind of network. In this paper, we present a p2p query routing algorithm which is based on semantic cluster (SCQR). SCQR makes nodes clustered according to their semantic, and each cluster elects a super-node as cluster computing node which is responsible for computing cluster semantic and establishing links with all neighbor cluster computing nodes. Query is routed among cluster computing nodes. SCQR achieves high query hit with small routing latency and query cost in both analytical theoretical and experimental studies.

关键词： Query processing Clustering algorithms Peer to peer computing Routing Delay Costs computer networks Frequency Floods Intelligent networks

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