7,671 research outputs found
Jeeva: Enterprise Grid-enabled Web Portal for Protein Secondary Structure Prediction
This paper presents a Grid portal for protein secondary structure prediction
developed by using services of Aneka, a .NET-based enterprise Grid technology.
The portal is used by research scientists to discover new prediction structures
in a parallel manner. An SVM (Support Vector Machine)-based prediction
algorithm is used with 64 sample protein sequences as a case study to
demonstrate the potential of enterprise Grids.Comment: 7 page
Zero-Shot Learning for Semantic Utterance Classification
We propose a novel zero-shot learning method for semantic utterance
classification (SUC). It learns a classifier for problems where
none of the semantic categories are present in the training set. The
framework uncovers the link between categories and utterances using a semantic
space. We show that this semantic space can be learned by deep neural networks
trained on large amounts of search engine query log data. More precisely, we
propose a novel method that can learn discriminative semantic features without
supervision. It uses the zero-shot learning framework to guide the learning of
the semantic features. We demonstrate the effectiveness of the zero-shot
semantic learning algorithm on the SUC dataset collected by (Tur, 2012).
Furthermore, we achieve state-of-the-art results by combining the semantic
features with a supervised method
SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources
SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance
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