Kien A. Hua
Bio: Kien A. Hua is an academic researcher from University of Central Florida. The author has contributed to research in topics: Multicast & Mobile computing. The author has an hindex of 36, co-authored 307 publications receiving 7836 citations. Previous affiliations of Kien A. Hua include Iowa State University & Symantec.
Papers published on a yearly basis
••09 Jul 2003
TL;DR: A peer-to-peer technique called ZIGZAG for single-source media streaming is designed, which allows the media server to distribute content to many clients by organizing them into an appropriate tree rooted at the server that has a height logarithmic with the number of clients and a node degree bounded by a constant.
Abstract: A peer-to-peer technique called ZIGZAG for single-source media streaming is designed . ZIGZAG allows the media server to distribute content to many clients by organizing them into an appropriate tree rooted at the server. This application-layer multicast tree has a height logarithmic with the number of clients and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottleneck. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics and unpredictable client behaviors. ZIGZAG handles such situations gracefully requiring a constant amortized control overhead. Especially, failure recovery can be done regionally with little impact on the existing clients and mostly no burden on the server.
••01 Sep 1998
TL;DR: This paper is able to tiate the service latency and improve the efficiency of mtiticast at the same time, and indicates convincingly that Patching offers .wbstanti~y better perforrnace.
••01 Oct 1997
TL;DR: This study investigates a novel multicast technique, called Skyscraper Broadcasting (SB), for video-on-demand applications, and is able to achieve the low latency of PB while using only 20% of the buffer space required by PPB.
Abstract: We investigate a novel multicast technique, called Skyscraper Broadcasting (SB), for video-on-demand applications. We discuss the data fragmentation technique, the broadcasting strategy, and the client design. We also show the correctness of our technique, and derive mathematical equations to analyze its storage requirement. To assess its performance, we compare it to the latest designs known as Pyramid Broadcasting (PB) and Permutation-Based Pyramid Broadcasting (PPB). Our study indicates that PB offers excellent access latency. However, it requires very large storage space and disk bandwidth at the receiving end. PPB is able to address these problems. However, this is accomplished at the expense of a larger access latency and more complex synchronization. With SB, we are able to achieve the low latency of PB while using only 20% of the buffer space required by PPB.
TL;DR: In Zigzag, the multicast tree has a height logarithmic with the number of clients, and a node degree bounded by a constant, so that the end-to-end delay is kept small.
Abstract: Given that the Internet does not widely support Internet protocol multicast while content-distribution-network technologies are costly, the concept of peer-to-peer could be a promising start for enabling large-scale streaming systems In our so-called Zigzag approach, we propose a method for clustering peers into a hierarchy called the administrative organization for easy management, and a method for building the multicast tree atop this hierarchy for efficient content transmission In Zigzag, the multicast tree has a height logarithmic with the number of clients, and a node degree bounded by a constant This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottlenecks Consequently, the end-to-end delay is kept small Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics Zigzag handles such situations gracefully, requiring a constant amortized worst-case control overhead Especially, failure recovery is done regionally with impact on, at most, a constant number of existing clients and with mostly no burden on the server
••18 Mar 2005
TL;DR: A genetic algorithm is used to select a subset of input features for decision tree classifiers, with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection.
Abstract: Machine Learning techniques such as Genetic Algorithms and Decision Trees have been applied to the field of intrusion detection for more than a decade. Machine Learning techniques can learn normal and anomalous patterns from training data and generate classifiers that then are used to detect attacks on computer systems. In general, the input data to classifiers is in a high dimension feature space, but not all of features are relevant to the classes to be classified. In this paper, we use a genetic algorithm to select a subset of input features for decision tree classifiers, with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. We used the KDDCUP 99 data set to train and test the decision tree classifiers. The experiments show that the resulting decision trees can have better performance than those built with all available features.
01 Jan 2002
01 Jan 2001
TL;DR: This chapter discusses Decision-Theoretic Foundations, Game Theory, Rationality, and Intelligence, and the Decision-Analytic Approach to Games, which aims to clarify the role of rationality in decision-making.
Abstract: Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index
15 Oct 2004
24 Oct 2007
TL;DR: In this article, the authors analyzed YouTube, the world's largest UGC VoD system, and provided an in-depth study of the popularity life cycle of videos, intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content.
Abstract: User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications.