18118919
EnjoyPhoto: a vertical image search engine for enjoying high-quality photos
In this paper, we propose building a vertical image search engine called EnjoyPhoto that leverages rich metadata from various photo forum web sites to meet users' requirements for enjoying high-quality photos, which is virtually impossible in traditional image search engines. To solve the ranking problem when aggregating multiple photo forums, we propose a novel rank fusion algorithm that uses duplicate photos to normalize rating scores. To further improve user experiences in enjoying photos, we design an in-place image browsing interface, and compare it with several other interfaces in a user study. With rich metadata and rating information, more attractive user interfaces are enabled, including slideshow authoring and photo recommendations. We conducted experiments and user studies on a 2.5-million image database to evaluate the proposed rank fusion algorithm, investigate the rationale behind building a vertical image search engine, and study user interfaces and preferences for the purpose of enjoying high-quality photos. The experimental results demonstrate the effectiveness of the proposed ranking algorithm. The results also show that the 2.5-million high-quality image database in EnjoyPhoto performs comparably with Google's 1- billion image database for queries related to location, nature, and daily life categories. Finally, our results show that the in-place browsing interface-called Force-Transfer view-is much more convenient for users than traditional interfaces.
32640232
AP-Based Borda Voting Method for Feature Extraction in TRECVID-2004
We present a novel fusion method — AP-based Borda voting method (APBB)— for rankings. Due to its adaptive weighting scheme, APBB outperforms many traditional methods. Comparative experiments on TRECVID 2004 data were carried out and showed the robustness and effectiveness of this method.
10396270
Temporal Shot Clustering Analysis for Video Concept Detection
The phenomenon that conceptually related shots appear together in videos is called temporal shot clustering. This phenomenon is a useful cue for video concept detection, which is one of basic steps in content-based video indexing and retrieval. We propose a method, called temporal shot clustering analysis, to improve results of video concept detection by exploiting the temporal shot clustering phenomenon. Two other methods are compared with temporal shot clustering analysis on the TRECVID 2003 dataset. Experiments showed that temporal shot clustering is of much benefit for video concept detection, and that temporal shot clustering method outperforms the other methods.
3172606
Gene name ambiguity of eukaryotic nomenclatures
MOTIVATION
With more and more scientific literature published online, the effective management and reuse of this knowledge has become problematic. Natural language processing (NLP) may be a potential solution by extracting, structuring and organizing biomedical information in online literature in a timely manner. One essential task is to recognize and identify genomic entities in text. 'Recognition' can be accomplished using pattern matching and machine learning. But for 'identification' these techniques are not adequate. In order to identify genomic entities, NLP needs a comprehensive resource that specifies and classifies genomic entities as they occur in text and that associates them with normalized terms and also unique identifiers so that the extracted entities are well defined. Online organism databases are an excellent resource to create such a lexical resource. However, gene name ambiguity is a serious problem because it affects the appropriate identification of gene entities. In this paper, we explore the extent of the problem and suggest ways to address it.


RESULTS
We obtained gene information from 21 organisms and quantified naming ambiguities within species, across species, with English words and with medical terms. When the case (of letters) was retained, official symbols displayed negligible intra-species ambiguity (0.02%) and modest ambiguities with general English words (0.57%) and medical terms (1.01%). In contrast, the across-species ambiguity was high (14.20%). The inclusion of gene synonyms increased intra-species ambiguity substantially and full names contributed greatly to gene-medical-term ambiguity. A comprehensive lexical resource that covers gene information for the 21 organisms was then created and used to identify gene names by using a straightforward string matching program to process 45,000 abstracts associated with the mouse model organism while ignoring case and gene names that were also English words. We found that 85.1% of correctly retrieved mouse genes were ambiguous with other gene names. When gene names that were also English words were included, 233% additional 'gene' instances were retrieved, most of which were false positives. We also found that authors prefer to use synonyms (74.7%) to official symbols (17.7%) or full names (7.6%) in their publications.


CONTACT
lifeng.chen@dbmi.columbia.edu
16022661
Monitoring access link capacity using TFRC probe
Accurate estimation of network characteristics based on end-to-end measurements is an important and challenging problem. With the increasing variety and sophistication of Internet access methods, the mobility of the users and the need for seamless handoff across them, monitoring access link capacity becomes critical for efficient multimedia delivery. In fact, this allows the multimedia server to properly adjust its sending rate to the rapidly changing access speed of the mobile client. This problem is challenging because most of the existing capacity monitoring schemes are active, like Pathrate (and therefore, introduce extra overhead on the bandwidth limited access link). Moreover, the access link is often asymmetric (e.g. ADSL, 1xRTT, etc.) thus preventing the use of round-trip monitoring schemes such as Pathchar and CapProbe. In this study, we propose and evaluate a passive, one-way link capacity monitoring tool called TFRC Probe. With TFRC Probe the source can monitor the forward direction capacity of both asymmetric and symmetric access links, and can rapidly and accurately adapt its transmissions rate accordingly. We validate TFRC Probe with both simulation and testbed experiments, and show that TFRC Probe is a very flexible tool that can accurately track frequent changes in access capacity.
9822806
Wavelet-based optical flow estimation
In this paper, a new algorithm for accurate optical flow estimation using discrete wavelet approximation is proposed. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately represent "things", to model optical flow vectors and image related functions. Each flow vector and image function are represented by linear combinations of wavelet basis functions. From such wavelet-based approximation, the leading coefficients of these basis functions carry the global information of the approximated "things". The proposed method can successfully convert the problem of minimizing a constraint function into that of solving a linear system of a quadratic and convex function of wavelet coefficients. Once all the corresponding coefficients are decided, the flow vectors can be determined accordingly. Experiments conducted on both synthetic and real image sequences show that our approach outperformed the existing methods in terms of accuracy.
7815226
Peak Load Forecasting Using the Self-organizing Map
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.
9226100
Mining Positive and Negative Association Rules from XML Query Patterns for Caching
Recently, several approaches that mine frequent XML query patterns and cache their results have been proposed to improve query response time. However, frequent XML query patterns mined by these approaches ignore the temporal sequence between user queries. In this paper, we take into account the temporal features of user queries to discover association rules, which indicate that when a user inquires some information from the XML document, she/he will probably inquire some other information subsequently. We cluster XML queries according to their semantics first and then mine association rules between the clusters. Moreover, not only positive but also negative association rules are discovered to design the appropriate cache replacement strategy. The experimental results showed that our approach considerably improved the caching performance by significantly reducing the query response time.