7754676
FDUQA on TREC2003 QA task
It is the fourth time that we take part in the QA track. Our system, FDUQA, is based on our previous system (Wu et al, 2002). FDUQA includes an offline part and an online part. We make great efforts on the online part while leaving the offline part unchanged. We have tried many natural language processing techniques, and incorporated many sources of world knowledge, including Web. A novel Query formulation technique has also been put forward. In addition, we’ve tried another attempt on answer extraction in this year’s task. In the second section, we will describe the architecture of our QA system; and give a detailed description on the Query formulation for Web search in the third section; while in the fourth section, we will introduce our new attempt on answer extraction; and we will present our performance in the last section.
15814097
Language Independent Text Categorization
Text categorization is defined as the task of assigning pre-defined category labels to new documents. This paper proposes a language-independent text categorization model based on machine learning, and describes the feature extraction, classifier and evaluation method in detail. This model has been implemented on two news corpus of Chinese and Japanese and satisfactory categorization effectiveness has been achieved.
921552
Dynamic Web log session identification with statistical language models
We present a novel session identification method based on statistical language modeling. Unlike standard time-out methods, which use fixed time thresholds for session identification, we use an information theoretic approach that yields more robust results for identifying session boundaries. We evaluate our new approach by learning interesting association rules from the segmented session files. We then compare the performance of our approach to three standard session identification method--the standard timeout method, the reference length method, and the maximal forward reference method--and find that our statistical language modeling approach generally yields superior results. However, as with every method, the performance of our technique varies with changing parameter settings. Therefore, we also analyze the influence of the two key factors in our language-modeling-based approach: the choice of smoothing technique and the language model order. We find that all standard smoothing techniques, save one, perform well, and that performance is robust to language model order.
668000
Applying language modeling to session identification from database trace logs
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark.
15628775
An FPGA based Co-Design Architecture for MIMO Lattice Decoders
─ MIMO systems have attracted great attentions because of their huge capacity. The hardware implementation of MIMO decoder becomes a challenging task as the complexity of the MIMO systems increases. This paper presents hardware/software co-design architecture targeted on a single FPGA for two typical lattice decoding algorithms. Two levels of parallelisms are analyzed for an efficient implementation with the preprocessing part on embedded MicroBlaze soft processor and the decoder part on customized hardware. The system prototypes of the AV and VB decoders show that they support up to 34.2 Mbps and 3.15 Mbps data rate at 20dB SNR respectively on XUP Virtex-II pro developing board with an xc2vp30 FPGA, which are 19 and 16 times faster than their respective implementations on a DSP. The performance in terms of resource utilization and bit error rate are also compared between these two algorithms.
42013296
On the computational hardness based on linear FPT-reductions
The notion of linear fpt-reductions has been recently introduced to derive strong computational lower bounds for well-known NP-hard problems. In this paper, we formally investigate the notion of W[t]-hardness under the linear fpt-reduction, and study the structural properties of the corresponding complexity classes. Additional complexity lower bounds on important computational problems are established. Some observations on structural properties of the standard parameterized hierarchy, the W -hierarchy, are also presented.
3172126
Snapshot density queries on location sensors
Density queries are of practical importance in many mobility related applications. In this paper, we employ the location sensors, which are placed in a geographical area and can only detect the amount of objects moving in vicinity, to estimate the dense regions of objects. Our approach partitions the region of interest into subregions, and deploys in each subregion both location sensors and a processing node that issues and answers density queries. Three algorithms are proposed to process density queries on those processing nodes. Their accuracy and efficiency are empirically evaluated.