Defense Notices

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.

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.

Multi-agent systems are a common paradigm for building distributed systems in different domains such as networking, health care, swarm sensing, robotics, and transportation. Systems are usually designed or adjusted in order to reflect the performance trade-offs made according to the characteristics of the mission requirement. 
Research has acknowledged the crucial role that communication plays in solving many performance problems. Conversely, research efforts that address communication decisions are usually designed and evaluated with respect to a single predetermined performance goal. This work introduces Goal-Driven Communication, where communication in a multi-agent system is determined according to flexible performance goals. 
This work proposes an evolutionary approach that, given a performance goal, produces a communication strategy that can improve a multi-agent system’s performance with respect to the desired goal. The evolved strategy determines what, when, and to whom the agents communicate. The proposed approach further enables tuning the trade-off between the performance goal and communication cost, to produce a strategy that achieves a good balance between the two objectives, according the system designer’s needs. 

Communication systems laboratory is a hands-on way to effectively visualize the real life applications of communication systems in its simplest form. Recently, hardware equipment such as spectrum analyzer, oscilloscope, and function generator were replaced by Pico Scope 6, a software based data analyzer. The Pico Scope 6 is a user friendly software which enables its users to capture and analyze analog and digital signals with a comparatively higher accuracy. Additionally, it is an economically viable solution, from both the procurement and maintenance stand point. The current effort focuses on developing a laboratory user manual, based on Pico Scope 6, for undergraduates of the Department of Electrical Engineering and Computer Science (EECS). The series of laboratory exercises developed follows the course outline of Introduction to Communication Systems – EECS 562. The expected outcomes of this laboratory manual is an improved understanding of analog modulations, digital modulations, and noise analysis of communication systems.

The operational amplifier is perhaps the most useful integrated device in existence today. It is widely used in analogue computers simulation systems and in a variety of electronic applications such as amplification filtering, buffering and comparison of signed levels. In this design project, we use the operational amplifier for amplification. Two-stage opamp is one of the most commonly used opamp architectures. A two stage differential amplifier is designed with an objective of a minimum gain of 65 dB. The gain achieved is 74.6 dB and 71.4 MHz 3dB gain bandwidth, which is useful for medium frequency operations. The schematic circuit is constructed using Metal Oxide Semiconductor Field Effect Transistor and the technology used for the final layout is Complementary metal–oxide–semiconductor (CMOS) using Cadence.