1351954
Cost-Sensitive Learning of SVM for Ranking
In this paper, we propose a new method for learning to rank. ‘Ranking SVM' is a method for performing the task. It formulizes the problem as that of binary classification on instance pairs and performs the classification by means of Support Vector Machines (SVM). In Ranking SVM, the losses for incorrect classifications of instance pairs between different rank pairs are defined as the same. We note that in many applications such as information retrieval the negative effects of making errors between higher ranks and lower ranks are larger than making errors among lower ranks. Therefore, it is natural to bring in the idea of cost-sensitive learning to learning to rank, or more precisely, to set up different losses for misclassification of instance pairs between different rank pairs. Given a cost-sensitive loss function we can construct a Ranking SVM model on the basis of the loss function. Simulation results show that our method works better than Ranking SVM in practical settings of ranking. Experimental results also indicate that our method can outperform existing methods including Ranking SVM on real information retrieval tasks such as document search and definition search.
15513083
Adapting ranking SVM to document retrieval
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.
2595430
A new approach to intranet search based on information extraction
This paper is concerned with 'intranet search'. By intranet search, we mean searching for information on an intranet within an organization. We have found that search needs on an intranet can be categorized into types, through an analysis of survey results and an analysis of search log data. The types include searching for definitions, persons, experts, and homepages. Traditional information retrieval only focuses on search of relevant documents, but not on search of special types of information. We propose a new approach to intranet search in which we search for information in each of the special types, in addition to the traditional relevance search. Information extraction technologies can play key roles in such kind of 'search by type' approach, because we must first extract from the documents the necessary information in each type. We have developed an intranet search system called 'Information Desk'. In the system, we try to address the most important types of search first - finding term definitions, homepages of groups or topics, employees' personal information and experts on topics. For each type of search, we use information extraction technologies to extract, fuse, and summarize information in advance. The system is in operation on the intranet of Microsoft and receives accesses from about 500 employees per month. Feedbacks from users and system logs show that users consider the approach useful and the system can really help people to find information. This paper describes the architecture, features, component technologies, and evaluation results of the system.
12535990
Fast integer-pel and fractional-pel motion estimation for H.264/AVC
Abstract This paper gives out a fast motion estimation algorithm for H.264/AVC, which has almost the same quality performance as that of the Full Search scheme and also provides a strategy to balance the quality performance and the search speed. Integer-pel search is the most time consuming module for motion estimation. In this paper a hybrid Unsymmetrical-cross Multi-hexagon-grid Search (UMHexagonS) algorithm is introduced, which well solves the false motion vector estimation problem because of the local-minimum. As results, it saves more than 90% on search time whereas the averaging PSNR loss is less than 0.056 dB for all tested sequences with different motion extent compared with the Fast Full Search scheme. Fractional-pel fast search is not negligible when the integer motion estimation has been extensively speeded up. By utilizing the property of uni-modal error surface inside the fractional-pel search window, a novel Center Biased Fractional-pel Search (CBFPS) algorithm is proposed in this paper, which can save 30–50% computation compared with the Full Fractional-pel Search scheme. Early termination is another problem discussed in this paper, to construct a complete motion estimation method, giving a uniform method which can cover a wide range of applications, terminating the program at early and right stage is certainly required. A model based on the Normative SAD Difference (NSD) is given to assist the termination decision-making, and a tradeoff between the search speed and the reconstructed quality can be achieved by changing a modulation factor.
39336188
Local Strongly Arc-Connectivity in Regular Bipartite Digraphs
We show that for any vertex x of a d-regular bipartite digraph there are a vertex y, in the other class of the bipartition, and d (x, y)-paths and d (y, x)-paths such that all 2d of them are pairwise arc-disjoint. This result generalizes a theorem of Hamidoune and Las Vergnas for graphs.
33593406
CrimeLink Explorer: Using Domain Knowledge to Facilitate Automated Crime Association Analysis
Link (association) analysis has been used in law enforcement and intelligence domains to extract and search associations between people from large datasets. Nonetheless, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges and enable crime investigators to conduct automated, effective, and efficient link analysis, we proposed three techniques which include: the concept space approach, a shortest-path algorithm, and a heuristic approach that captures domain knowledge for determining importance of associations. We implemented a system called CrimeLink Explorer based on the proposed techniques. Results from our user study involving ten crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently. Additionally, subjects concluded that association paths found based on the heuristic approach were more accurate than those found based on the concept space approach.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
6317190
WORM vs. WORM: preliminary study of an active counter-attack mechanism
Self-propagating computer worms have been terrorizing the Internet for the last several years. With the increasing density, inter-connectivity and bandwidth of the Internet combined with security measures that inadequately scale, worms will continue to plague the Internet community. Existing anti-virus and intrusion detection systems are clearly inadequate to defend against many recent fast-spreading worms. In this paper we explore an active counter-attack method - anti-worms. We propose a method that transforms a malicious worm into an anti-worm which disinfects its original. The method is evaluated using the CodeRed, Blaster and Slammer worms. We show through simulation the effectiveness of an anti-worm with several propagation schemes and its impact on the overall network. We also discuss important limitations of the proposed method.