431999
Q2C@UST: our winning solution to query classification in KDDCUP 2005
In this paper, we describe our ensemble-search based approach, Q2C@UST (http://webprojectl.cs.ust.hk/q2c/), for the query classification task for the KDDCUP 2005. There are two aspects to the key difficulties of this problem: one is that the meaning of the queries and the semantics of the predefined categories are hard to determine. The other is that there are no training data for this classification problem. We apply a two-phase framework to tackle the above difficulties. Phase I corresponds to the training phase of machine learning research and phase II corresponds to testing phase. In phase I, two kinds of classifiers are developed as the base classifiers. One is synonym-based and the other is statistics based. Phase II consists of two stages. In the first stage, the queries are enriched such that for each query, its related Web pages together with their category information are collected through the use of search engines. In the second stage, the enriched queries are classified through the base classifiers trained in phase I. Based on the classification results obtained by the base classifiers, two ensemble classifiers based on two different strategies are proposed. The experimental results on the validation dataset help confirm our conjectures on the performance of the Q2C@UST system. In addition, the evaluation results given by the KDDCUP 2005 organizer confirm the effectiveness of our proposed approaches. The best F1 value of our two solutions is 9.6% higher than the best of all other participants' solutions. The average F1 value of our two submitted solutions is 94.4% higher than the average F1 value from all other submitted solutions.
456221
Integrating hidden Markov models and spectral analysis for sensory time series clustering
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to data mining, including uneven sequence lengths, multi-dimensionality and high levels of noise. We adopt a principled approach, by first transforming all the data into an equal-length vector form while keeping as much temporal information as we can, and then applying dimensionality and noise reduction techniques such as spectral clustering to the transformed data. Experimental evaluation on synthetic and real data shows that our proposed approach outperforms standard model-based clustering algorithms for time series data.
9667404
Power-efficient access-point selection for indoor location estimation
An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of access points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet (clustering and decision tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy.
1805342
Adaptive Temporal Radio Maps for Indoor Location Estimation
In this paper, we present a novel method to adapt the temporal radio maps for indoor location estimation by offsetting the variational environmental factors using data mining techniques and reference points. Environmental variations, which cause the signals to change from time to time even at the same location, present a challenging task for indoor location estimation in the IEEE 802.11b infrastructure. In such a dynamic environment, the radio maps obtained in one time period may not be applicable in other time periods. To solve this problem, we apply a regression analysis to learn the temporal predictive relationship between the signal-strength values received by sparsely located reference points and that received by the mobile device. This temporal prediction model can then be used for online localization based on the newly observed signal-strength values at the client side and the reference points. We show that this technique can effectively accommodate the variations of signal-strength values over different time periods without the need to rebuild the radio maps repeatedly. We also show that the location of mobile device can be accurately determined using this technique with lower density in the distribution of the reference points
16826835
High-Level Goal Recognition in a Wireless LAN
Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated plan-recognition model that combines low-level sensory readings with high-level goal inference. A two-level architecture is proposed to infer a user's goals in a complex indoor environment using an RF-based wireless network. The novelty of our work derives from our ability to infer a user's goals from sequences of signal trajectory, and the ability for us to make a trade-off between model accuracy and inference efficiency. The model relies on a dynamic Bayesian network to infer a user's actions from raw signals, and an N-gram model to infer the users' goals from actions. We present a method for constructing the model from the past data and demonstrate the effectiveness of our proposed solution through empirical studies using some real data that we have collected.
43330192
Impact of bursty error rates on the performance of wireless local area network (WLAN)
Abstract With an increasing popularity of DCF based wireless LAN, the modeling of 802.11 distributed coordination function (DCF) has attracted lots of research attention. Existing analysis of 802.11 DCF has been focused on the determination of the throughput and the packet delay under saturated traffic and ideal channel conditions. Although some recent papers address the saturated performance under a simple uniform error model, they can hardly capture the impact of bursty characteristics of wireless fading on the performance of 802.11 DCF. This paper presents exact formulae for the throughput and the delay in DCF for various traffic conditions when either saturated or unsaturated traffic load is present. A two-state Markov channel model is incorporated to present the bursty characteristics of channel errors. With our analysis, the impact of bursty channel error on unsuccessful transmission probability and the DCF performance can be determined. The results of our analytical framework reveal that the four-way handshaking scheme does not improve throughput substantially for light traffic load. However, for heavy traffic load, the four-way handshaking scheme is advantageous as compared to the basic access scheme. Also, extensive simulation is done to substantiate the accuracy of our analytical model.
7749218
Impact of channel conditions on the throughput optimization in 802.11 DCF
In this paper, we evaluate the impact of transmission error rate on the contention and the system throughput in IEEE 802.11 DCF. We identify the trade-off between the desire to reduce the ratio of overhead in the data packet by adopting larger packet size and the need to reduce the packet error rate by using smaller packet length. Meanwhile, we provide a novel approach to model the different channel conditions incurred by different nodes. This work has applications in performance optimization by establishing trade-offs between transmission error rate and transmission power, modulation, coding scheme, packet size. Our results substantiate the intuition that in a contention-based system like 802.11 DCF, the channel variation of any single node in the system not only impacts the throughput of itself, but also affects the throughput of other nodes and the system throughput. Our work shows that to achieve maximum optimization of the throughput, the channel conditions of all the nodes in the system have to be taken into account. Otherwise the optimization is suboptimal. Copyright © 2005 John Wiley & Sons, Ltd.
1277960
Modeling biological systems using Dynetica-a simulator of dynamic networks
UNLABELLED
We present Dynetica, a user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations.


AVAILABILITY AND SUPPLEMENTARY INFORMATION
Dynetica 1.0, example models, and the user's guide are available at http://www.its.caltech.edu/~you/Dynetica/Dynetica_page.htm