2443391
Improve retrieval accuracy for difficult queries using negative feedback
How to improve search accuracy for difficult topics is an under-addressed, yet important research question. In this paper, we consider a scenario when the search results are so poor that none of the top-ranked documents is relevant to a user's query, and propose to exploit negative feedback to improve retrieval accuracy for such difficult queries. Specifically, we propose to learn from a certain number of top-ranked non-relevant documents to rerank the rest unseen documents. We propose several approaches to penalizing the documents that are similar to the known non-relevant documents in the language modeling framework. To evaluate the proposed methods, we adapt standard TREC collections to construct a test collection containing only difficult queries. Experiment results show that the proposed approaches are effective for improving retrieval accuracy of difficult queries.
5471705
Probabilistic Models for Expert Finding
A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
9299231
Language Models for Expert Finding--UIUC TREC 2006 Enterprise Track Experiments
In this paper, we report our experiments in the TREC 2006 Enterprise Track. Our focus is to study a language model for expert finding. We extend an existing language model for expert retrieval in three aspects. First, we model the document-expert association using a mixure model instead of name matching heuristics as in the existing work; such a mixture model allows us to put different weights on email matching and name matching. Second, we propose to model the prior of an expert based on the counts of email matches in the supporting documents instead of using uniform prior as in the previous work. Finally, we perform topic expansion and generalize the model from computing the likelihood to computing the cross entropy. Our experiments show that the first two extensions are more effective than the third extension, though when optimized, they all seem to be effective.
13897908
Detail preserving shape deformation in image editing
Shape deformation is a common practice in digital image editing, but can unrealistically stretch or compress texture detail. We propose an image editing system that decouples feature position from pixel color generation, by resynthesizing texture from the source image to preserve its detail and orientation around a new feature curve location. We introduce a new distortion to patch-based texture synthesis that aligns texture features with image features. A dense correspondence field between source and target images generated by the control curves then guides texture synthesis.
16418680
Term feedback for information retrieval with language models
In this paper we study term-based feedback for information retrieval in the language modeling approach. With term feedback a user directly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.
5670917
Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments
In this paper, we report our experiments in the TREC 2003 Genomics Track and the Robust Track. A common theme that we explored is the robustness of a basic language modeling retrieval approach. We examine several aspects of robustness, including robustness in handling different types of queries, different types of documents, and optimizing performance for difficult topics. Our basic retrieval method is the KL-divergence retrieval model with the two-stage smoothing method plus a mixture model feedback method. In the Genomics IR track, we propose a new method for modeling semi-structured queries using language models, which is shown to be more robust and effective than the regular query model in handling gene queries. In the Robust track, we experimented with two heuristic approaches to improve the robustness in using language models for pseudo feedback.
11027312
An Implementation of Time and Frequency Synchronization for Carrier Interferometry of OFDM in an Underwater Acoustic Channel
Considering the features of Underwater Acoustic channel and the sensitivity versus synchronization for CI/OFDM system, A time and frequency synchronization method based on Constant Amplitude Zero Auto-Correlation(CAZAC) sequence is presented. The time offset estimation and carrier frequency offset estimation can be achieved simultaneously. The fractional part and integral part of carrier offset estimation can be respectively got by the correlation result of time offset estimation and cyclic correlation principle. The efficacy of the synchronization algorithms is demonstrated experimentally.
1454940
Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization
There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.
8868065
Controlling Uncertainty in Personal Positioning at Minimal Measurement Cost
One interesting scenario in personal positioning involves an energy-conscious mobile user who tries to obtain estimates about his positions with sufficiently high confidence while consuming as little battery energy as possible. Besides obtaining estimates directly from a position measuring device, the user can rely on extrapolative calculations based on a user movement model and a known initial estimate. Because each measuring probe usually incurs a substantially higher cost than the extrapolative calculation, the objective is to minimize the overall cost of the measurement probes. 
 
Assuming that the user moves at a normally-distributed velocity, we consider two scenarios which differ in the probing devices used. In the first scenario, only one probing device is used. In this case, the aim is to minimize the total number of probes required. In the second scenario, two types of positioning devices are given, where one type of devices offers a higher positioning precision, but also at a greater probing cost. In this case, the aim is to choose an optimal combination of probes from the two types of devices. 
 
For both scenarios, we present algorithms for determining the minimum-cost probing sequences. The algorithms are computationally efficient in reducing the searching space of all possible probing sequences. Our approach is based on Kalman Filtering theory which allows to integrate estimates obtained from the measurements and the extrapolative calculations. The variances in the estimates can provably stay below the specified level throughout the journey. 
 
To the best of our knowledge, these results appear to be the first that uses a mathematically rigorous approach to minimize the probing cost while guaranteeing the quality of estimates in personal positioning.
8593487
Behavioral Consistency Extraction for Face Verification
According to the preferred embodiment of the present invention pull-up/pull-down circuits are provided that use transistors with different threshold voltages to assure power-up to the correct predetermined state. These circuits have the ability to hold a node up or down while drawing very little DC current. In one embodiment a pull-up/pull-down circuit is provided that powers up to a first state with the pull-up node high and the pull-down node low, and that can be toggled from one state to another. A second embodiment provides a pull-up or pull-down circuit that powers up to the desired state and can be disabled by pulling the pull-up node low or pulling the pull-down node high. The circuits remain disabled until the power to the circuit is cycled.
7764302
Cognitive personal positioning based on activity map and adaptive particle filter
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers.