Web Page Enrichment using a Rough Set Based Method

When documents are matched to a given query, often the terms in the query are matched to the words in the documents for calculating similarity. But it is a good idea if the given document is represented in an enriched manner with not only the actual words occurring in the document but also with the synonyms of the important words. This would definitely improve the recall of the system. With its ability to deal with vagueness and fuzziness, tolerance rough set seems to be promising tool to model relations between terms and documents. In many information retrieval problems, especially in text classification, determining the relation between term-term and term-document is essential. In this work, the application of TRSM to web page classification was evaluated to determine its effectiveness as a way to enrich a web page

Hierarchical classification for multiple, distributed web databases

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance

Stars and saints: professional conversations for enhancing classroom practices

This paper explores a reflective activity - professional conversation. In so doing, it recalls the recent experience of working alongside 'starring' teachers who are dedicated to serving the poor in areas of deprivation. And this recollection is framed around the advice of saints - secular, religious and philosophical

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.Comment: 6 pages, 5 figures + 9 pages of supplementary informatio

Towards the Semantic Text Retrieval for Indonesian

Indonesia is the fourth most populous country in the world and the Asosiasi Penyelenggara Jasa Internet Indonesia (Indonesian Internet Service Providers Association) recorded that Indonesian Internet subscribers and users has been growing rapidly every year. These facts should encourage research such as computer linguistic and information retrieval for Indonesian language which in fact has not been extensively investigated. The research aims to investigate the tolerance rough sets model (TRSM) in order to propose a framework for a semantic text retrieval system. The proposed framework is intended for Indonesian language specifically hence we are working with Indonesian corpora and applying tools for Indonesian, e.g. Indonesian stemmer, in all of the studies. Cognitive approach is employed particularly during data preparation and analysis. An extensive collaboration with human experts is significant on creating a new Indonesian corpus suitable for our research. The performance of an ad hoc retrieval system becomes the starting point for further analysis in order to learn and understand more about the process and characteristic of TRSM, despite comparing TRSM with other methods and determining the best solution. The results of this process function as the guidance for computational modeling of some TRSM's tasks and finally the framework of a semantic information retrieval system with TRSM as its heart. In addition to the proposed framework, this thesis proposes three methods based on TRSM, which are the automatic tolerance value generator, thesaurus optimization, and lexicon-based document representation. All methods were developed by the use of our own corpus, namely ICL-corpus, and evaluated by employing an available Indonesian corpus, called Kompas-corpus. The evaluation on the methods achieved satisfactory results, except for the compact document representation method; this last method seems to work only in limited domain

On The Robustness of a Neural Network

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second. In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.Comment: 36th IEEE International Symposium on Reliable Distributed Systems 26 - 29 September 2017. Hong Kong, Chin