Learning to Rank Academic Experts in the DBLP Dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with arXiv:1302.041

Automatic annotation for weakly supervised learning of detectors

PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully supervised, that is they require a large body of training data where the locations of the objects/actions in images/videos have been manually annotated. With the emergence of digital media, and the rise of high-speed internet, raw images and video are available for little to no cost. However, the manual annotation of object and action locations remains tedious, slow, and expensive. As a result there has been a great interest in training detectors with weak supervision where only the presence or absence of object/action in image/video is needed, not the location. This thesis presents approaches for weakly supervised learning of object/action detectors with a focus on automatically annotating object and action locations in images/videos using only binary weak labels indicating the presence or absence of object/action in images/videos. First, a framework for weakly supervised learning of object detectors in images is presented. In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically annotating object locations in weakly labelled data is presented which, unlike existing approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial annotation is then used to start an iterative process in which standard object detectors are used to refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift from the object of interest, a scheme for detecting model drift is also presented. Furthermore, unlike most other methods, our weakly supervised approach is evaluated on data without manual pose (object orientation) annotation. Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues, is carried out. From the analysis, a new method based on negative mining (NegMine) is presented for the initial annotation of both object and action data. The NegMine based approach is a much simpler formulation using only inter-class measure and requires no complex combinatorial optimisation but can still meet or outperform existing approaches including the previously pre3 sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing approaches to boost their performance. Finally, the thesis will take a step back and look at the use of generic object detectors as prior knowledge in weakly supervised learning of object detectors. These generic object detectors are typically based on sampling saliency maps that indicate if a pixel belongs to the background or foreground. A new approach to generating saliency maps is presented that, unlike existing approaches, looks beyond the current image of interest and into images similar to the current image. We show that our generic object proposal method can be used by itself to annotate the weakly labelled object data with surprisingly high accuracy

svmPRAT: SVM-based Protein Residue Annotation Toolkit

<p>Abstract</p> <p>Background</p> <p>Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models.</p> <p>Results</p> <p>We present a general purpose protein residue annotation toolkit (<it>svm</it><monospace>PRAT</monospace>) to allow biologists to formulate residue-wise prediction problems. <it>svm</it><monospace>PRAT</monospace> formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of <it>svm</it><monospace>PRAT</monospace> is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue <it>svm</it><monospace>PRAT</monospace> captures local information around the reside to create fixed length feature vectors. <it>svm</it><monospace>PRAT</monospace> implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training effective predictive models.</p> <p>Conclusions</p> <p>In this work we evaluate <it>svm</it><monospace>PRAT</monospace> on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. <it>svm</it><monospace>PRAT</monospace> has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems.</p> <p><it>Availability</it>: <url>http://www.cs.gmu.edu/~mlbio/svmprat</url></p

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624