An Efficient and Effective Algorithm for Hierarchical Classification of Search Results

This paper presents an efficient yet effective algorithm to hierarchically organize search results. Rather than using clustering technique, this paper employs domain ontology in order to obtain better hierarchical classification. Domain ontology defines information architecture in a specific domain. The hierarchical classification process consists of two stages. First, in off-line mode, a classifier is employed to determine category in ontology that is similar to a Webpage. Second, when processing a user’s search query, all search results are hierarchically categorized using the classification scheme provided in the metadata of retrieved documents

Toward a New Approach in Fruit Recognition using Hybrid RGBD Features and Fruit Hierarchy Property

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features  and  the  fruit  hierarchy  property.  In  the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red, Green, Blue, Depth) images. Meanwhile, given a set of fruit species and variety, with a preexisting hierarchy among them, we consider the problem of assigning images to one of these fruit variety from the point of view of a hierarchy. We report on computational experiment using this approach. We show that the use of hierarchy structure along with hybrid RGBD features can improve the classification performance

Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding

 Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data.  Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance

Automatic Title Generation in Scientific Articles for Authorship Assistance: A Summarization Approach

This paper presents a studyon automatic title generation for scientific articles considering sentence information types known as rhetorical categories. A title can be seenas a high-compression summary of a document. A rhetorical category is an information type conveyed by the author of a text for each textual unit, for example: background, method, or result of the research. The experiment in this studyfocused on extracting the research purpose and research method information for inclusion in a computer-generated title. Sentences are classifiedinto rhetorical categories, after which these sentences are filtered using three methods. Three title candidates whose contents reflect the filtered sentencesare then generated using a template-based or an adaptive K-nearest neighbor approach. The experiment was conducted using two different dataset domains: computational linguistics and chemistry. Our study obtained a 0.109-0.255 F1-measure score on average for computer-generated titles compared to original titles. In a human evaluation the automatically generated titles were deemed 'relatively acceptable' in the computational linguistics domain and 'not acceptable' in the chemistry domain. It can be concluded that rhetorical categories have unexplored potential to improve the performance of summarization tasks in general

Review of Local Descriptor in RGB-D Object Recognition

The emergence of an RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB images gives hope to the computer vision community. Moreover, the use of local features began to increase over the last few years and has shown impressive results, especially in the field of object recognition. This article attempts to provide a survey of the recent technical achievements in this area of research. We review the use of local descriptors as the feature representation which is extracted from RGB-D images, in instances and category-level object recognition. We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor. Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning. In this article, we show that the involvement of depth data successfully improves the accuracy of object recognition