Defense Notices

Recently machine learning has been creating great strides in many areas of work field such as health, finance, education, sports etc., which has encouraged demand for machine learning systems. By definition machine learning automates the task of learning in terms of rule induction, classification, regression etc. This is then used to draw knowledgeable insights and to forecast an event before it actually takes place. Despite this automation, machine learning still does not automate the task of selecting the best algorithm(s) for a specific dataset. With the rapidly growing machine learning algorithms it has become difficult for novices as well as researchers to choose the best algorithm. The crux of a machine learning system is (1) to solve fundamental problems of preprocessing the data to help machine learning algorithm understand the data better; (2) to solve the problem of choosing meaningful features hence reducing the noise from the data; and (3) to choose the best resulting machine learning algorithm which is performed by doing grid search over hyperparameters of various machine learning algorithms and afterwards doing metric comparison amongst all outcomes. These are the problems addressed by Wolf. 
Automation is the fuel that drives Wolf. Automating time-consuming and repeatable tasks are the defining characteristics of the project. The rising scope of Artificial Intelligence (AI) and machine learning increases the need for automation to simplify the process, hence help researchers and data scientists dig deeper into the problem and understand it well, rather than spending time in tweaking the algorithms. The positive correlation of growing intelligence and the complexity of solutions has shifted the trend from Artificial Intelligence (AI) to Automated Intelligence, a paradigm on which Wolf is based. 
Wolf has been built to have an impact on a wider audience. The automation of machine learning pipeline saves ~40% of the work effort spent towards implementing and testing algorithm. It helps people with different levels of expertise and requirements, helps novices to identify best combinations of algorithms without having in depth knowledge of algorithms and helps researchers and businesses better their machine learning knowledge to figure out best resulting hyperparameters. 

As it is becoming a significant part of our daily life, wireless mobile handsets have become faster and smarter. One of the main remaining requirement by users is to have a longer lasting wireless cellular devices. Many techniques have been used to increase the capacity of the battery (Ampere per Hour), but that increases the safety concern. 
Instead, it is better to have mobile handsets that consume less energy i.e increase energy efficiency. Therefore, in this research proposal, we study and analyze the radio 
frequency(RF) transceiver energy consumption, which is the largest energy consumed in the cellular device. We consider a model of large number of parameters in order to make it more realistic. First a transmitter energy of single antenna device is considered for a fixed target probability of error in the receiver for multilevel quadratic amplitude modulations (MQAM). It will be found that the power amplifier (PA) consumes the highest portion of transceiver energy due to the low efficiency of the PA.
Furthermore, when MQAM and raised cosine filter are used, the impact of peak to average ratio (PAR) on PA becomes another source of energy wasting in the PA. This issue is analyzed in this research proposal with a number of promising solutions. This analysis of energy consumption for single antenna devices will help us analyze the energy consumption of multiple antennas devices. In this regard, we discuss the energy efficiency of multiple input multiple output (MIMO) antenna with known channel state information (CSI) at the transmitter. However, the study of energy efficiency of MIMO without CSI using space time coding will be our next step. 

IRIM (Interesting Rule Induction Module) is a rule induction system designed to induce particularly strong, simple rule sets. Additionally, IRIM does not require prior discretization of numerical attribute values. IRIM does not necessarily produce consistent rules that fully describe the target concepts, however, the rules induced by IRIM often lead to novel revelations of hidden relationships in a dataset. In this paper, we attempt to extend the IRIM system to be able to handle missing attribute values (in particular, lost and do-not-care attribute values) more thoroughly than ignoring the cases that they belong to. Further, we include an implementation of IRIM in the modern programming language Python that has been written for easy inclusion in within a Python data mining package or library. The provided implementation makes use of the Pandas module which is built on top of a C back end for quick performance relative to the performance normally found with Python. 

Data classification is a methodology of data mining used to retrieve meaningful information from data. A model is built from the input training set which is later used to classify new observations. One of the most widely used models is a decision tree which uses a tree like structure to list all possible outcomes. Decision trees are preferred for their simple structure, requiring little effort for data preparation and easy interpretation. This project implements ID3, an algorithm for building the decision tree using information gain. The decision tree is converted to a set of rules and the error rate is calculated using the test dataset. The dataset is usually stored in a relational database in the form tables. In practice, it might be desired that data be stored across multiple databases. In such scenarios, retrieving and coordinating data from the databases could be a challenging task. This project provides the implementation of ID3 algorithm with the convenience of reading data stored at multiple data sources.

Data classification is a methodology of data mining used to retrieve meaningful information from data. A model is built from the input training set which is later used to classify new observations. One of the most widely used models is a decision tree which uses a tree like structure to list all possible outcomes. Decision trees are preferred for their simple structure, requiring little effort for data preparation and easy interpretation. This project implements ID3, an algorithm for building the decision tree using information gain. The decision tree is converted to a set of rules and the error rate is calculated using the test dataset. The dataset is usually stored in a relational database in the form tables. In practice, it might be desired that data be stored across multiple databases. In such scenarios, retrieving and coordinating data from the databases could be a challenging task. This project provides the implementation of ID3 algorithm with the convenience of reading data stored at multiple data sources.

Fair machine learning is an emerging and urgent research topic that aims to avoid discriminatory predictions against protected groups of people in real-world decision makings. This project aims to advance the field in two dimensions. First, we propose a more practical measurement of individual fairness called inequity coefficient, which integrates the current individual fairness framework that lacks of practice and the current situation testing practice that lacks of principle. We develop certain foundations of the measurement and present its practice. Second, we propose a first study of fairness in the context of transfer learning, with focuses on the hypothesis transfer and multi-task settings over two tasks. We illustrate a new challenge called discriminatory transfer, where discrimination is enforced by traditional task relatedness constraints that only aim to find accurate hypotheses. We propose a set of new algorithms that aim to avoid discriminatory transfer across tasks or promote fairness within each task.

Fair machine learning is an emerging and urgent research topic that aims to avoid discriminatory predictions against protected groups of people in real-world decision makings. This project aims to advance the field in two dimensions. First, we propose a more practical measurement of individual fairness called inequity coefficient, which integrates the current individual fairness framework that lacks of practice and the current situation testing practice that lacks of principle. We develop certain foundations of the measurement and present its practice. Second, we propose a first study of fairness in the context of transfer learning, with focuses on the hypothesis transfer and multi-task settings over two tasks. We illustrate a new challenge called discriminatory transfer, where discrimination is enforced by traditional task relatedness constraints that only aim to find accurate hypotheses. We propose a set of new algorithms that aim to avoid discriminatory transfer across tasks or promote fairness within each task.

Amazon.com is one of the largest online retail stores in the world. Besides selling millions of product on their website, Amazon provides a variety of Web services including Amazon Review and Recommendation System. Users are encouraged to write product reviews to help others to understand products’ features and make purchase decisions. However, product reviews, as a type of user generated content (UGC), suffer from quality and trust problems. To help evaluating the quality of reviews, Amazon also provides the users with the helpfulness vote feature so that a user can support a review that he considers helpful. In this project we aim to study the relation between helpfulness votes and the ranks of the reviews. In particular, we are looking for answers to questions such as “how does the helpfulness votes affect review ranks?” and “how review rank and its presentation mechanism affect people’s voting behavior?” To investigate on these questions, we built a crawler to collect reviews and votes of reviews from Amazon at a daily basis. Then, we conducted an analysis on a dataset with over 50,000 Amazon reviews to identify the voting patterns and their impact on the review ranks. Our results show that there exists a positive correlation between the review ranks and the helpfulness votes. 

Amazon.com is one of the largest online retail stores in the world. Besides selling millions of product on their website, Amazon provides a variety of Web services including Amazon Review and Recommendation System. Users are encouraged to write product reviews to help others to understand products’ features and make purchase decisions. However, product reviews, as a type of user generated content (UGC), suffer from quality and trust problems. To help evaluating the quality of reviews, Amazon also provides the users with the helpfulness vote feature so that a user can support a review that he considers helpful. In this project we aim to study the relation between helpfulness votes and the ranks of the reviews. In particular, we are looking for answers to questions such as “how does the helpfulness votes affect review ranks?” and “how review rank and its presentation mechanism affect people’s voting behavior?” To investigate on these questions, we built a crawler to collect reviews and votes of reviews from Amazon at a daily basis. Then, we conducted an analysis on a dataset with over 50,000 Amazon reviews to identify the voting patterns and their impact on the review ranks. Our results show that there exists a positive correlation between the review ranks and the helpfulness votes.​

Rough set theory is a popular approach for decision rule induction. However, it requires the objects in the information system to be completely described. Many real life data sets are incomplete, so we cannot apply directly rough set theory for rule induction. This project implements and compares two generalizations of rough set theory, used for rule induction from incomplete data: Tolerance Relation and Valued Tolerance Relation. A comparative analysis is conducted for the lower and upper approximations and decision rules induced by the two methods. Our experiments show that Valued Tolerance Relation provides better approximations than Simple Tolerance Relation when the percentage of missing attribute values in the datasets is high.