arXiv subjects

Huy Nguyen

Publications and source records attributed to Huy Nguyen.

102 records · Page 6Linked to original sources

arXiv2013

On Budgeted Influence Maximization in Social Networks

Given a budget and arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1 - 1/sqrt(e)). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.

cs.SI↗

arXiv2012

On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation

We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: * A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. * A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. * A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems.

cs.DS↗

arXiv2012

Binary is Good: A Binary Inference Framework for Primary User Separation in Cognitive Radio Networks

Primary users (PU) separation concerns with the issues of distinguishing and characterizing primary users in cognitive radio (CR) networks. We argue the need for PU separation in the context of collaborative spectrum sensing and monitor selection. In this paper, we model the observations of monitors as boolean OR mixtures of underlying binary latency sources for PUs, and devise a novel binary inference algorithm for PU separation. Simulation results show that without prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy. An interesting implication of the proposed algorithm is the ability to effectively represent n independent binary sources via (correlated) binary vectors of logarithmic length.

cs.NI↗

arXiv2010

Binary Independent Component Analysis with OR Mixtures

Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals. The classical Independent Components Analysis (ICA) framework usually assumes linear combinations of independent sources over the field of realvalued numbers R. In this paper, we investigate binary ICA for OR mixtures (bICA), which can find applications in many domains including medical diagnosis, multi-cluster assignment, Internet tomography and network resource management. We prove that bICA is uniquely identifiable under the disjunctive generation model, and propose a deterministic iterative algorithm to determine the distribution of the latent random variables and the mixing matrix. The inverse problem concerning inferring the values of latent variables are also considered along with noisy measurements. We conduct an extensive simulation study to verify the effectiveness of the propose algorithm and present examples of real-world applications where bICA can be applied.

cs.IT↗

arXiv2010

Binary Inference for Primary User Separation in Cognitive Radio Networks

Spectrum sensing receives much attention recently in the cognitive radio (CR) network research, i.e., secondary users (SUs) constantly monitor channel condition to detect the presence of the primary users (PUs). In this paper, we go beyond spectrum sensing and introduce the PU separation problem, which concerns with the issues of distinguishing and characterizing PUs in the context of collaborative spectrum sensing and monitor selection. The observations of monitors are modeled as boolean OR mixtures of underlying binary sources for PUs. We first justify the use of the binary OR mixture model as opposed to the traditional linear mixture model through simulation studies. Then we devise a novel binary inference algorithm for PU separation. Not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.

cs.NI↗

arXiv2010

Network Traffic Anomalies Detection and Identification with Flow Monitoring

Network management and security is currently one of the most vibrant research areas, among which, research on detecting and identifying anomalies has attracted a lot of interest. Researchers are still struggling to find an effective and lightweight method for anomaly detection purpose. In this paper, we propose a simple, robust method that detects network anomalous traffic data based on flow monitoring. Our method works based on monitoring the four predefined metrics that capture the flow statistics of the network. In order to prove the power of the new method, we did build an application that detects network anomalies using our method. And the result of the experiments proves that by using the four simple metrics from the flow data, we do not only effectively detect but can also identify the network traffic anomalies.

cs.NI↗

arXiv2010

Context Awareness Framework Based on Contextual Graph

Nowadays computing becomes increasingly mobile and pervasive. One of the important steps in pervasive computing is context-awareness. Context-aware pervasive systems rely on information about the context and user preferences to adapt their behavior. However, context-aware applications do not always behave as user's desire, and can cause users to feel dissatisfied with unexpected actions. To solve these problems, context-aware systems must provide mechanisms to adapt automatically when the context changes significantly. The interesting characteristic of context is its own behaviors which depend on various aspects of the surrounding contexts. This paper uses contextual graphs to solve the problem "the mutual relationships among the contexts". We describe the most relevant work in this area, as well as ongoing research on developing context-aware system for ubiquitous computing based on contextual graph. The usage of contextual graph in context-awareness is expected to make it effective for developers to develop various applications with the need of context reasoning.

cs.NI↗

arXiv2010

Network Anomaly Detection: Flow-based or Packet-based Approach?

One of the most critical tasks for network administrator is to ensure system uptime and availability. For the network security, anomaly detection systems, along with firewalls and intrusion prevention systems are the must-have tools. So far in the field of network anomaly detection, people are working on two different approaches. One is flow-based; usually rely on network elements to make so-called flow information available for analysis. The second approach is packet-based; which directly analyzes the data packet information for the detection of anomalies. This paper describes the main differences between the two approaches through an in-depth analysis. We try to answer the question of when and why an approach is better than the other. The answer is critical for network administrators to make their choices in deploying a defending system, securing the network and ensuring business continuity.

cs.NI↗

arXiv2010

Application of Data Mining to Network Intrusion Detection: Classifier Selection Model

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.

cs.NI↗

arXiv2010

How to Maximize User Satisfaction Degree in Multi-service IP Networks

Bandwidth allocation is a fundamental problem in communication networks. With current network moving towards the Future Internet model, the problem is further intensified as network traffic demanding far from exceeds network bandwidth capability. Maintaining a certain user satisfaction degree therefore becomes a challenge research topic. In this paper, we deal with the problem by proposing BASMIN, a novel bandwidth allocation scheme that aims to maximize network user's happiness. We also defined a new metric for evaluating network user satisfaction degree: network worth. A three-step evaluation process is then conducted to compare BASMIN efficiency with other three popular bandwidth allocation schemes. Throughout the tests, we experienced BASMIN's advantages over the others; we even found out that one of the most widely used bandwidth allocation schemes, in fact, is not effective at all.

cs.NI↗

arXiv2010

Context Ontology Implementation for Smart Home

Context awareness is one of the important fields in ubiquitous computing. Smart Home, a specific instance of ubiquitous computing, provides every family with opportunities to enjoy the power of hi-tech home living. Discovering that relationship among user, activity and context data in home environment is semantic, therefore, we apply ontology to model these relationships and then reason them as the semantic information. In this paper, we present the realization of smart home's context-aware system based on ontology. We discuss the current challenges in realizing the ontology context base. These challenges can be listed as collecting context information from heterogeneous sources, such as devices, agents, sensors into ontology, ontology management, ontology querying, and the issue related to environment database explosion.

cs.NI↗

arXiv2010

Development of a Context Aware Virtual Smart Home Simulator

Context awareness is the most important research area in ubiquitous computing. In particular, for smart home, context awareness attempts to bring the best services to the home habitants. However, the implementation in the real environment is not easy and takes a long time from building the scratch. Thus, to support the implementation in the real smart home, it is necessary to demonstrate that thing can be done in the simulator in which context information can be created by virtual sensors instead of physical sensors. In this paper, we propose ISS, an Interactive Smart home Simulator system aiming at controlling and simulating the behavior of an intelligent house. The developed system aims to provide architects, designers a simulation and useful tool for understanding the interaction between environment, people and the impact of embedded and pervasive technology on in daily life. In this research, the smart house is considered as an environment made up of independent and distributed devices interacting to support user's goals and tasks. Therefore, by using ISS, the developer can realize the relationship among virtual home space, surrounded environment, use and home appliances.

cs.NI↗