Automatic Extraction of Keywords and Co-occurrence Keyword Sets

Internet search has become an essential part of almost everyone’s daily life and work. To make wise personal and business decisions in a timely fashion, one must access the most relevant information efficiently. Because the amount of information on the Internet is enormous, it is important that a search engine ranks the information appropriately when it presents search results to users. Latent Semantic Indexing (LSI) addresses relevance ranking based on how significant a search word is in each document. Some innovative approaches of computing higher dimensional LSI (HD-LSI) were explored in this project. In traditional LSI, the term frequency-inverse document frequency (TFIDF) is calculated based on how significant a single word is in a document. The goal of this project is to generalize LSI to higher dimensions regarding the traditional LSI as the one-dimensional special case. A benefit of the project is to enable a search engine to rank documents based on the special meaning of multi-word phrases, such as “wall street,” which is captured by a two-dimensional LSI method. Another benefit of the project is the reusable Java software components that compute HD-LSI and store the indexes into a relational database, from which many types of applications can access the HD-LSI data. The software components may be reused for studying the proximity of semantics among documents in high dimensional space in future research. Besides the software engineering aspect, this project contributes to computer science by studying the different approaches to HD-LSI computation. In particular, the dimensional trends in each case were analyzed

Open source environment to define constraints in route planning for GIS-T

Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version

Unified Embedding and Metric Learning for Zero-Exemplar Event Detection

Event detection in unconstrained videos is conceived as a content-based video retrieval with two modalities: textual and visual. Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event. Related works train a bank of concept detectors on external data sources. These detectors predict confidence scores for test videos, which are ranked and retrieved accordingly. In contrast, we learn a joint space in which the visual and textual representations are embedded. The space casts a novel event as a probability of pre-defined events. Also, it learns to measure the distance between an event and its related videos. Our model is trained end-to-end on publicly available EventNet. When applied to TRECVID Multimedia Event Detection dataset, it outperforms the state-of-the-art by a considerable margin.Comment: IEEE CVPR 201