Integrating Fuzzy Decisioning Models With Relational Database Constructs

Human learning and classification is a nebulous area in computer science. Classic decisioning problems can be solved given enough time and computational power, but discrete algorithms cannot easily solve fuzzy problems. Fuzzy decisioning can resolve more real-world fuzzy problems, but existing algorithms are often slow, cumbersome and unable to give responses within a reasonable timeframe to anything other than predetermined, small dataset problems. We have developed a database-integrated highly scalable solution to training and using fuzzy decision models on large datasets. The Fuzzy Decision Tree algorithm is the integration of the Quinlan ID3 decision-tree algorithm together with fuzzy set theory and fuzzy logic. In existing research, when applied to the microRNA prediction problem, Fuzzy Decision Tree outperformed other machine learning algorithms including Random Forest, C4.5, SVM and Knn. In this research, we propose that the effectiveness with which large dataset fuzzy decisions can be resolved via the Fuzzy Decision Tree algorithm is significantly improved when using a relational database as the storage unit for the fuzzy ID3 objects, versus traditional storage objects. Furthermore, it is demonstrated that pre-processing certain pieces of the decisioning within the database layer can lead to much swifter membership determinations, especially on Big Data datasets. The proposed algorithm uses the concepts inherent to databases: separated schemas, indexing, partitioning, pipe-and-filter transformations, preprocessing data, materialized and regular views, etc., to present a model with a potential to learn from itself. Further, this work presents a general application model to re-architect Big Data applications in order to efficiently present decisioned results: lowering the volume of data being handled by the application itself, and significantly decreasing response wait times while allowing the flexibility and permanence of a standard relational SQL database, supplying optimal user satisfaction in today\u27s Data Analytics world. We experimentally demonstrate the effectiveness of our approach

Services Learning Practicum

The Service Learning Practicum is a document driven knowledge management system. The purpose of the Practicum is to involve MSCIT students in IT projects that support and provide solutions to non-profit organizations or non-governmental organizations (NGOs). The main documents that drive the practicum are the student application; the NGO needs statement; and the student\u27s thesis, design, and research documents. The practicum unites graduate students with NGOs. Both benefit from this union because the student is able to reach academic goals and the NGOs are able to implement low-cost or no-cost solutions for their IT needs. This project implements a graphic user interface for the collection, storage, and access of these documents

Factorising Meaning and Form for Intent-Preserving Paraphrasing

We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an encoder-decoder model to reconstruct a question from a paraphrase with the same meaning and an exemplar with the same surface form, leading to separated encoding spaces. We use a Vector-Quantized Variational Autoencoder to represent the surface form as a set of discrete latent variables, allowing us to use a classifier to select a different surface form at test time. Crucially, our method does not require access to an external source of target exemplars. Extensive experiments and a human evaluation show that we are able to generate paraphrases with a better tradeoff between semantic preservation and syntactic novelty compared to previous methods.Comment: ACL 202

Emerging Technologies for Automated Information Services and Management(ET)Training Course (October 13, 1997 - January 16, 1998)

This report described role of training in LIS field and different library automation softwar