We present componentwise condition numbers for the problems of MoorePenrose generalized matrix inversion and linear least squares. Also, the condition numbers for these condition numbers are given.
Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts un...Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware installation.Thus,it is imperative to detect such threats.However,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs.In this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web attacks.Specifically,we firstly use auto-encoder to represent URLs.Then,the represented URLs will be input into a proposed composite neural network for detection.In order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.展开更多
Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic,which is especially relevant in Network Intrusion Detection.In this paper,as enlightene...Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic,which is especially relevant in Network Intrusion Detection.In this paper,as enlightened by the theory of Deep Learning Neural Networks,Hierarchy Distributed-Agents Model for Network Risk Evaluation,a newly developed model,is proposed.The architecture taken on by the distributed-agents model are given,as well as the approach of analyzing network intrusion detection using Deep Learning,the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented,and the hierarchical evaluative framework for Network Risk Evaluation of the proposed model is built.Furthermore,to examine the proposed model,a series of experiments were conducted in terms of NSLKDD datasets.The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60%on NSL-KDD datasets.As the results acquired from the experiment indicate,the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network.展开更多
Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amou...Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for statistical evaluation. Although many efficient algorithms have been introduced, exhaustive search methods are still infeasible and feasible approximation methods are yet implausible.Additionally, the fast and continual growth of biological networks makes the problem more challenging. As a consequence, parallel algorithms have been developed and distributed computing has been tested in the cloud computing environment as well. In this paper, we survey current algorithms for network motif detection and existing software tools. Then, we show that some methods have been utilized for parallel network motif search algorithms with static or dynamic load balancing techniques. With the advent of cloud computing services, network motif search has been implemented with MapReduce in Hadoop Distributed File System（HDFS）, and with Storm, but without statistical testing. In this paper, we survey network motif search algorithms in general, including existing parallel methods as well as cloud computing based search, and show the promising potentials for the cloud computing based motif search methods.展开更多
A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- va...A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.展开更多
Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often infl...Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often influenced by music,and can be independent body language expressions.Dance choreography requires motion content to follow a general dance genre,whereas dance performances under musical influence are infused with diverse impromptu motion styles.Considering the high expressiveness and variations in space and time,providing accessible and effective user control for tuning dance motion styles remains an open problem.Methods In this study,we present a hierarchical framework that decouples the dance synthesis task into independent modules.We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences.This novel framework allows the individual modules to be trained separately.Because of the decoupling,dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments,and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network.Each module is replaceable at runtime,which adds flexibility to the synthesis of dance sequences.Results Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.展开更多
The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration o...The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration orders of elements of c.e. sets by means of Turing machines on natural numbers are investigated. In this paper, we consider the enumeration orders of elements of c.e. sets on rational numbers. We present enumeration order reducibility and enumeration order equivalence on rational numbers and propose some lemmas and theorems on these concepts. Also, we show that the theories here hold for Rc and we could repeat the same theories in this domain, in a same way.展开更多
The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput tec...The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput technologies and the production of large amounts of data enable the discovery of essential proteins at the system level by analyzing Protein-Protein Interaction （PPI） networks, and replacing biological or chemical experiments. Furthermore, additional gene-level annotation information, such as Gene Ontology （GO） terms, helps to detect essential proteins with higher accuracy. Various centrality algorithms have been used to determine essential proteins in a PPI network, and, recently motif centrality GO, which is based on network motifs and GO terms, works best in detecting essential proteins in a Baker＇s yeast Saccharomyces cerevisiae PPI network, compared to other centrality algorithms. However, each centrality algorithm contributes to the detection of essential proteins with different properties, which makes the integration of them a logical next step. In this paper, we construct a new feature space, named CENT-ING-GO consisting of various centrality measures and GO terms, and provide a computational approach to predict essential proteins with various machine learning techniques. The experimental results show that CENT-ING-GO feature space improves performance over the INT-GO feature space in previous work by Acencio and Lemke in 2009. We also demonstrate that pruning a PPI with informative GO terms can improve the prediction performance further.展开更多
In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware li...In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring （QoM） of the network. In this paper, a particle swarm optimization （PSO）-based solution is proposed to achieve the optimal channel selection. A 2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO （MC-PSO） algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.展开更多
The nearly analytic discrete method(NADM)is a perturbation method originally proposed by Yang et al.(2003)[26]for acoustic and elastic waves in elastic media.This method is based on a truncated Taylor series expansion...The nearly analytic discrete method(NADM)is a perturbation method originally proposed by Yang et al.(2003)[26]for acoustic and elastic waves in elastic media.This method is based on a truncated Taylor series expansion and interpolation approximations and it can suppress effectively numerical dispersions caused by the discretizating the wave equations when too-coarse grids are used.In the present work,we apply the NADM to simulating acoustic and elastic wave propagations in 2D porous media.Our method enables wave propagation to be simulated in 2D porous isotropic and anisotropic media.Numerical experiments show that the error of the NADM for the porous case is less than those of the conventional finite-difference method(FDM)and the so-called Lax-Wendroff correction(LWC)schemes.The three-component seismic wave fields in the 2D porous isotropic medium are simulated and compared with those obtained by using the LWC method and exact solutions.Several characteristics of wave propagating in porous anisotropic media,computed by the NADM,are also reported in this study.Promising numerical results illustrate that the NADM provides a useful tool for large-scale porous problems and it can suppress effectively numerical dispersions.展开更多
Many competing approaches exist in evaluating sensor network solutions differing by levels of ease of use, cost, control, and realism. Existing work concentrates on simulating network protocols or emulating processing...Many competing approaches exist in evaluating sensor network solutions differing by levels of ease of use, cost, control, and realism. Existing work concentrates on simulating network protocols or emulating processing units at the machine cycle level. However, little has been done to emulate the sensors and the physical environments that they monitor. The main contribution of this work is the design of WiserEmulator, an emulation framework for structural health monitoring, which gracefully balances the trade-offs between realism, controllability, and cost. WiserEmulator consists of two main components -- a testbed of wireless sensor nodes and a software emulation environment. To emulate the excitation and response of piezo-electric transducers, as well as the wave propagation inside concrete structures, the COMSOL Multi-Physics software was utilized. Digitized sensing output from COMSOL was played back via a multi-channel Digital-to-Analog Converter （DAC） connected to the wireless sensor testbed. In addition to the emulation of concrete structures, WiSeREmulator also allows users to choose pre- stored data collected from field experiments and synthesized data. A user-friendly Graphical User Interface （GUI） was developed that facilitates intuitive configurations of experimental settings, control of the on-set and progression of the experiments, and real-time visualization of experimental results. We have implemented WiSeREmulator in MATLAB. This work advances the state of the art in providing low cost solutions to evaluating Cyber Physical Systems such as wireless structural health monitoring networks.展开更多
In this paper, we investigate the condition numbers for the generalized matrix inversion and the rank deficient linear least squares problem： minx ｜｜Ax- b｜｜2, where A is an m-by-n （m ≥ n） rank deficient matrix...In this paper, we investigate the condition numbers for the generalized matrix inversion and the rank deficient linear least squares problem： minx ｜｜Ax- b｜｜2, where A is an m-by-n （m ≥ n） rank deficient matrix. We first derive an explicit expression for the condition number in the weighted Frobenius norm ｜｜ [AT,βb] ｜｜F of the data A and b, where T is a positive diagonal matrix and β is a positive scalar. We then discuss the sensitivity of the standard 2-norm condition numbers for the generalized matrix inversion and rank deficient least squares and establish relations between the condition numbers and their condition numbers called level-2 condition numbers.展开更多
基金the NSF of China under grant 10471027 and Shanghai Education Commission.
文摘We present componentwise condition numbers for the problems of MoorePenrose generalized matrix inversion and linear least squares. Also, the condition numbers for these condition numbers are given.
基金This work is supported in part by the National Natural Science Foundation of China(61871140,61872100,61572153,U1636215,61572492,61672020)the National Key research and Development Plan(Grant No.2018YFB0803504)Open Fund of Beijing Key Laboratory of IOT Information Security Technology(J6V0011104).
文摘Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware installation.Thus,it is imperative to detect such threats.However,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs.In this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web attacks.Specifically,we firstly use auto-encoder to represent URLs.Then,the represented URLs will be input into a proposed composite neural network for detection.In order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.
基金This work is supported by the National Key Research and Development Program of China under Grant 2016YFB0800600the Natural Science Foundation of China under Grant(No.61872254 and No.U1736212)+2 种基金the Fundamental Research Funds for the central Universities(No.YJ201727,No.A0920502051815-98)Academic and Technical Leaders’Training Support Fund of Sichuan Province(2016)the research projects of the Humanity and Social Science Youth Foundation of Ministry of Education(13YJCZH021).We want to convey our grateful appreciation to the corresponding author of this paper,Gang Liang,who has offered advice with huge values in all stages when writing this essay to us.
文摘Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic,which is especially relevant in Network Intrusion Detection.In this paper,as enlightened by the theory of Deep Learning Neural Networks,Hierarchy Distributed-Agents Model for Network Risk Evaluation,a newly developed model,is proposed.The architecture taken on by the distributed-agents model are given,as well as the approach of analyzing network intrusion detection using Deep Learning,the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented,and the hierarchical evaluative framework for Network Risk Evaluation of the proposed model is built.Furthermore,to examine the proposed model,a series of experiments were conducted in terms of NSLKDD datasets.The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60%on NSL-KDD datasets.As the results acquired from the experiment indicate,the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network.
文摘Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for statistical evaluation. Although many efficient algorithms have been introduced, exhaustive search methods are still infeasible and feasible approximation methods are yet implausible.Additionally, the fast and continual growth of biological networks makes the problem more challenging. As a consequence, parallel algorithms have been developed and distributed computing has been tested in the cloud computing environment as well. In this paper, we survey current algorithms for network motif detection and existing software tools. Then, we show that some methods have been utilized for parallel network motif search algorithms with static or dynamic load balancing techniques. With the advent of cloud computing services, network motif search has been implemented with MapReduce in Hadoop Distributed File System（HDFS）, and with Storm, but without statistical testing. In this paper, we survey network motif search algorithms in general, including existing parallel methods as well as cloud computing based search, and show the promising potentials for the cloud computing based motif search methods.
文摘A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.
基金Supported by Startup Fund 20019495,McMaster University。
文摘Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often influenced by music,and can be independent body language expressions.Dance choreography requires motion content to follow a general dance genre,whereas dance performances under musical influence are infused with diverse impromptu motion styles.Considering the high expressiveness and variations in space and time,providing accessible and effective user control for tuning dance motion styles remains an open problem.Methods In this study,we present a hierarchical framework that decouples the dance synthesis task into independent modules.We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences.This novel framework allows the individual modules to be trained separately.Because of the decoupling,dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments,and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network.Each module is replaceable at runtime,which adds flexibility to the synthesis of dance sequences.Results Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.
文摘The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration orders of elements of c.e. sets by means of Turing machines on natural numbers are investigated. In this paper, we consider the enumeration orders of elements of c.e. sets on rational numbers. We present enumeration order reducibility and enumeration order equivalence on rational numbers and propose some lemmas and theorems on these concepts. Also, we show that the theories here hold for Rc and we could repeat the same theories in this domain, in a same way.
文摘The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput technologies and the production of large amounts of data enable the discovery of essential proteins at the system level by analyzing Protein-Protein Interaction （PPI） networks, and replacing biological or chemical experiments. Furthermore, additional gene-level annotation information, such as Gene Ontology （GO） terms, helps to detect essential proteins with higher accuracy. Various centrality algorithms have been used to determine essential proteins in a PPI network, and, recently motif centrality GO, which is based on network motifs and GO terms, works best in detecting essential proteins in a Baker＇s yeast Saccharomyces cerevisiae PPI network, compared to other centrality algorithms. However, each centrality algorithm contributes to the detection of essential proteins with different properties, which makes the integration of them a logical next step. In this paper, we construct a new feature space, named CENT-ING-GO consisting of various centrality measures and GO terms, and provide a computational approach to predict essential proteins with various machine learning techniques. The experimental results show that CENT-ING-GO feature space improves performance over the INT-GO feature space in previous work by Acencio and Lemke in 2009. We also demonstrate that pruning a PPI with informative GO terms can improve the prediction performance further.
基金supported by the National Natural Science Foundation of China under Grant Nos. 61100211 and 61003307the Central High School Basic Research Foundation of China under Grant No. 2011HGZL0010the Postdoctoral Science Foundation of China under Grant Nos. 20110490084 and 2012T50569
文摘In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring （QoM） of the network. In this paper, a particle swarm optimization （PSO）-based solution is proposed to achieve the optimal channel selection. A 2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO （MC-PSO） algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.
基金the National Natural Sciences Foundation of China(Grant 40574014)and the MCME of China。
文摘The nearly analytic discrete method(NADM)is a perturbation method originally proposed by Yang et al.(2003)[26]for acoustic and elastic waves in elastic media.This method is based on a truncated Taylor series expansion and interpolation approximations and it can suppress effectively numerical dispersions caused by the discretizating the wave equations when too-coarse grids are used.In the present work,we apply the NADM to simulating acoustic and elastic wave propagations in 2D porous media.Our method enables wave propagation to be simulated in 2D porous isotropic and anisotropic media.Numerical experiments show that the error of the NADM for the porous case is less than those of the conventional finite-difference method(FDM)and the so-called Lax-Wendroff correction(LWC)schemes.The three-component seismic wave fields in the 2D porous isotropic medium are simulated and compared with those obtained by using the LWC method and exact solutions.Several characteristics of wave propagating in porous anisotropic media,computed by the NADM,are also reported in this study.Promising numerical results illustrate that the NADM provides a useful tool for large-scale porous problems and it can suppress effectively numerical dispersions.
文摘Many competing approaches exist in evaluating sensor network solutions differing by levels of ease of use, cost, control, and realism. Existing work concentrates on simulating network protocols or emulating processing units at the machine cycle level. However, little has been done to emulate the sensors and the physical environments that they monitor. The main contribution of this work is the design of WiserEmulator, an emulation framework for structural health monitoring, which gracefully balances the trade-offs between realism, controllability, and cost. WiserEmulator consists of two main components -- a testbed of wireless sensor nodes and a software emulation environment. To emulate the excitation and response of piezo-electric transducers, as well as the wave propagation inside concrete structures, the COMSOL Multi-Physics software was utilized. Digitized sensing output from COMSOL was played back via a multi-channel Digital-to-Analog Converter （DAC） connected to the wireless sensor testbed. In addition to the emulation of concrete structures, WiSeREmulator also allows users to choose pre- stored data collected from field experiments and synthesized data. A user-friendly Graphical User Interface （GUI） was developed that facilitates intuitive configurations of experimental settings, control of the on-set and progression of the experiments, and real-time visualization of experimental results. We have implemented WiSeREmulator in MATLAB. This work advances the state of the art in providing low cost solutions to evaluating Cyber Physical Systems such as wireless structural health monitoring networks.
基金The first author is supported by the National Natural Science Foundation of China Under grant 10471027 Shanghai Education＇Committee. The third author is partially supported by Natural ScienceEngineering Research Council of Canada and supported by Shanghai Key Laboratory of Contemporary Applied Mathematics of Fudan University during Sanzheng Qiao＇s visit.
文摘In this paper, we investigate the condition numbers for the generalized matrix inversion and the rank deficient linear least squares problem： minx ｜｜Ax- b｜｜2, where A is an m-by-n （m ≥ n） rank deficient matrix. We first derive an explicit expression for the condition number in the weighted Frobenius norm ｜｜ [AT,βb] ｜｜F of the data A and b, where T is a positive diagonal matrix and β is a positive scalar. We then discuss the sensitivity of the standard 2-norm condition numbers for the generalized matrix inversion and rank deficient least squares and establish relations between the condition numbers and their condition numbers called level-2 condition numbers.
