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Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Journal of Information Security 2017年第4期8卷 339-361页

作者： Asmah Muallem Sachin Shetty Jan Wei Pan Juan Zhao Biswajit Biswal Department of Electrical and Computer Engineering Tennessee State University Nashville TN USA Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA R&D Engineering Department Roadstar.AI. Mountain View CA USA Department of Electrical and Computer Engineering Tennessee State University Nashville TN USA Department of Mathematics and Computer Science South Carolina State University Orangeburg SC USA

This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.

关键词： Hoeffding Trees Distributed Ensembles Anomaly Detection Machine Learning Spark

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Beyond bilinear: Generalized multimodal factorized high-order pooling for visual question answering

arXiv

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arXiv 2017年

作者： Yu, Zhou Yu, Jun Xiang, Chenchao Fan, Jianping Tao, Dacheng Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China Department of Computer Science University of North Carolina Charlotte United States UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question;2) multi-modal feature fusion that is able to capture the complex interactions between multi-modal features;3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a 'co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multi-modal feature fusion, a generalized Multi-modal Factorized High-order pooling approach (MFH) is developed to achieve more effective fusion of multi-modal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the KL (Kullback-Leibler) divergence is used as the loss function to achieve precise characterization of the complex correlations between multiple diverse answers with same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A deep neural network architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance. With an ensemble of our MFH models, we achieve the state-of-the-art performance on the large-scale VQA datasets and win the runner-up in VQA Challenge 2017. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Energy efficiency comparison of hypervisors 7

Energy efficiency comparison of hypervisors

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Jiang, Congfeng Ou, Dongyang Wang, Yumei You, Xindong Zhang, Jilin Wan, Jian Luo, Bing Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Zhejiang China Mobile and Internet Systems Laboratory Department of Computer Science Wayne State University DetroitMI United States

ISBN: (纸本)9781509051175

Current cloud data centers are fully virtualized for service consolidations and power/energy reduction. Although virtualization could reduce real time power and overall energy consumption, the energy characteristics of hypervisors hosting different workloads are not well profiled and understood. In this paper, we investigate the power and energy characteristics of mainstream hypervisors and container engine, i.e., VMware ESXi, Microsoft Hyper-V, KVM, XenServer and Docker, on five different platforms (two mainstream 2U rack servers, one emerging ARM64 server, one desktop server, and one laptop) with hundreds of hours' power measures. We use both computing intensive and mixed web server-database workloads to explore the power and energy characteristics of different hypervisors. Extensive experiment results of four workload levels (very light, light, fair, and very heavy workload) demonstrate that hypervisors expose different power and energy characteristics. We find that: (1) Hypervisors expose different power and energy consumption on the same hardware running same workloads. (2) Although mainstream hypervisors have different energy efficiencies aligned with different workload types and workload levels, there is no single hypervisor that outperforms all other hypervisors on all platforms in terms of power or energy consumptions. (3) Although container virtualization is considered as light-weight virtualization in terms of implementation and maintenance, it is not essentially more power efficient than traditional virtualization technology. (4) ARM64 server does have lower power consumption, but they finish computing jobs with longer execution time and sometimes consume more energy. And ARM64 servers has medium energy consumption per database operations for mixed workloads. The results presented in this paper provide useful insights to system designers, as well as data center operators for power-aware workload placement and virtual machine scheduling. © 2016 IEEE.

关键词： Energy efficiency

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Building a hybrid DEVS and GRASS model using a composable cellular automaton

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International Journal of modeling, simulation, and Scientific Computing 2016年第1期7卷 62-92页

作者： Gary R.Mayer Hessam S.Sarjoughian Department of Computer Science Southern Illinois University Edwardsville 61 Circle Drive EdwardsvilleIllinois 62026USA Arizona Center for Integrative Modeling&Simulation School of Computing Informaticsand Decision Systems Engineering Arizona State University699 South Mill Avenue TempeArizona 85281USA

modeling and simulation is pervasive throughout many different *** computing technology has provided more capability,the systems being modeled and simulated have grown larger and more *** times,these large systems are managed as interacting *** it is necessary for the simulation to allow disparate subsystems to maintain their independence,then a hybrid model of the subsystems should be ***,to ease the burden of verification and validation of simulation results,a proven system theoretical modeling specification should be ***,many communities have already adopted nonsystem theoretical software solutions and established a group of domain experts familiar with these *** paper provides two things:a formal approach to building a hybrid model,and a discussion of how to incorporate a nonsystem theoretical software implementation into a proven *** first is done through the implementation of a Knowledge Interchange Broker(KIB)as an Interaction Model(IM).The second is accomplished by exemplifying the use of the IM in an agent-environment hybrid *** the hybrid model,the agent is implemented in the Discrete-event System(DEVS)specification and the environment is implemented in the Geographical Resources Analysis Support System(GRASS)using a Composable Cellular Automaton(CCA)*** concept has been successfully applied to both example models and an interdisciplinary research project where the interactions between human activities and landscape processes are studied.

关键词： Multi-formalism modeling hybrid simulation composability agent cellular automata

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Mixed optimization for constrained resource allocation, an application to a local bus service 16

Mixed optimization for constrained resource allocation, an a...

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simulation Winter Conference

作者： Felisa J. Vázquez-Abad Larry Fenn Department of Computer Science CUNY Institute for Computer Simulation Stochastic Modeling and Optimization New York NY USA

ISBN: (纸本)9781509044849

The present paper follows up on Vázquez-Abad (2013), where we applied the ghost simulation model to a public transportation problem. The ghost simulation model replaces faster point processes (passenger arrivals) with a “fluid” model while retaining a discrete event simulation for the rest of the processes (bus dynamics). This is not an approximation, but an exact conditional expectation when the fast process is Poisson. It can be interpreted as a Filtered Monte Carlo method for fast simulation. In the current paper we develop the required theory to implement a mixed optimization procedure to find the optimal fleet size under a stationary probability constraint. It is a hybrid optimization because for each fleet size, the optimal headway is real-valued, while the fleet size is integer-valued. We exploit the structure of the problem to implement a stopped target tracking method combined with stochastic binary search.

关键词： Optimization Stochastic processes Computational modeling Resource management Search problems Investment Public transportation

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Explore K-Anycast Information Dissemination in Mobile Opportunistic Networks

Explore K-Anycast Information Dissemination in Mobile Opport...

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IEEE Wireless Communications and Networking Conference

作者： Peng Liu Jia Xu Biao Xu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University

ISBN: (纸本)9781467398152

In Mobile Opportunistic Networks, there are special cases of multicast in which destinations (except the total number) are not predetermined. E.g., a person tries to find another three players to play poker without knowing them. Achieving efficient data routing in this scenario (target any k destinations among m of them) can be very challenge due to (1) no predetermined destinations, (2) extra delay cost by the destinations collision, and (3) difficulty of the receiver quantity control. We define this as a K-Anycast problem, the goal is to route copies of the message to any k destinations with the shortest average delay. In this paper, we first propose a matching method to solve the problem in a centralized way. We then bring forward a layered hierarchical structure where nodes are organized according to their degree of activities. Based on the structure, two routing algorithms are proposed where K-Cast initializes exactly k copies of the message without replication in the middle, K-Epidemic performs epidemic routing only in a controlled range. Both algorithms will first forward copies upwards along the structure and then downwards to the destinations. Experiments on real data trace show that the proposed algorithms achieve much better delay, delivery ratio and lower forwarding numbers.

关键词： Mobile Opportunistic Network Anycast Data routing Destination collision

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Free-surface flow simulations for moored and floating offshore platforms 7

Free-surface flow simulations for moored and floating offsho...

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7th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2016

作者： Veldman, Arthur E.P. Luppes, Roel Van Der Plas, Peter Van Der Heiden, Henri Düz, Bülent Seubers, Henk Helder, Joop Bunnik, Tim Huijsmans, René Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands Department of Ship Hydrodynamis Delft University of Technology Delft Netherlands Maritime Reseach Institute MARIN Wageningen Netherlands Multiscale Modeling and Simulation University of Twente Enschede Netherlands

ISBN: (纸本)9786188284401

During the development of the ComFLOW simulation method many challenges have to be tackled concerning the flow modelling and the numerical solution algorithm. Examples here of are wave propagation, absorbing boundary conditions, fluid-solid body interaction, turbulence modeling and numerical efficiency. Some of these challenges will be discussed in the paper, in particular the design of absorbing boundary conditions and the numerical coupling for fluid-solid body interaction. As a demonstration of the progress made, a number of simulation results for engineering applications from the offshore industry will be presented: a wave-making oscillating buoy, a free-fall life boat dropping into wavy water, and wave impact against a semi-submersible offshore platform. For those applications, MARIN has carried out several validation experiments.

关键词： Computational fluid dynamics

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Photo Aesthetic Quality Assessment via Label Distribution Learning

Photo Aesthetic Quality Assessment via Label Distribution Le...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Xiaowei Zhang Fei Gao Di Huang Min Tan Jun Yu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781509018987

Automatic prediction of photo aesthetic quality is useful for many practical purposes. Current computational approaches typically solved this problem by assigning a categorical label (good or bad) to a photo. However, due to the subjectivity and complexity of humans aesthetic judgments, only a categorical label is insufficient to represent humans perceived aesthetic quality of a photo. This paper focuses on an interesting problem: is it possible to predict the crowed opinions about the aesthetic quality of a photo? The crowed opinion here is expressed by the distribution of scores given by a number of subjects. For each given photo, a deep convolutional neural network (DCNN) is utilized to calculate its feature representation. Afterwards, the crowed opinion prediction problem is formulated as one of label distribution learning (LDL). Experiments show that the proposed method is highly effective and outperforms state-of-the-art algorithms.

关键词： label distribution support regressor photo quality assessment convolutional neural network deep learning label distribution learning

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Syntactic model for Russian: Deep learning models with dependency parsing scheme

Syntactic model for Russian: Deep learning models with depen...

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2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

作者： Sboev, Alexandr Rybka, Roman Gudovskikh, Dmitry Moloshnikov, Ivan Selivanov, Anton Ivanov, Igor Department of Information Technology and Mathematical Modeling NRC Kurchatov Institute Moscow Russia Moscow Russia Department of Computer and Engineering Simulation National Research Nuclear University MEPhI Moscow Russia R and D Department Voskhod Plekhanov Russian University Moscow Russia

ISBN: (纸本)9781509055104

This work presents results of effectiveness analyses of using deep neural networks models for syntactic parsing of SynTagRus dataset. A set of modular neural network topologies based on composition of Stack long short-term memory layers, multilayer perceptron has been compared with widely used algorithms of classification on base of Gradient boosting trees and Support vector machine. Results allow to select set of state-of-art neural networks model and estimate their perspective. © 2016 IEEE.

关键词： Deep neural networks

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Clustering the Stations of Bicycle Sharing System

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Journal of Donghua University(English Edition) 2016年第6期33卷 968-972页

作者： SHI Xiao-ying YU Zhen-hai XU Hai-tao HUANG Bin-bin Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hang zhou 310018 China

Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past *** the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics *** to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out ***,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of *** experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.

关键词： urban mining human mobility analysis visual analytics bicycle sharing system

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