Suhas Ranganath

Interactive DSP laboratories on mobile phones and tablets

The use of mobile devices and tablets in engineering education has been gaining lot of interest, due to its interactive capabilities and its ability to stimulate student interest. On the other hand, this technology can also enable instructors to broaden the scope of their curriculum and increase student participation. In this paper, we describe an interactive application to perform signal processing simulations on iOS devices such as the iPhone and the iPad. Furthermore, we describe two laboratory exercises to introduce continuous/discrete convolution and filter design. The exercises and the proposed application will be evaluated by students of an undergraduate DSP course at Arizona State University during Fall 2011. Finally, we describe the planned assessment methodology which will enable us to provide prescriptive recommendations for using i-JDSP in DSP courses.

A tool for collecting provenance data in social media

In recent years, social media sites have provided a large amount of information. Recipients of such information need mechanisms to know more about the received information, including the provenance. Previous research has shown that some attributes related to the received information provide additional context, so that a recipient can assess the amount of value, trust, and validity to be placed in the received information. Personal attributes of a user, including name, location, education, ethnicity, gender, and political and religious affiliations, can be found in social media sites. In this paper, we present a novel web-based tool for collecting the attributes of interest associated with a particular social media user related to the received information. This tool provides a way to combine different attributes available at different social media sites into a single user profile. Using different types of Twitter users, we also evaluate the performance of the tool in terms of number of attribute values collected, validity of these values, and total amount of retrieval time.

Health monitoring laboratories by interfacing physiological sensors to mobile android devices

The recent sensing capabilities of mobile devices along with their interactivity and popularity in the student community can be used to create a unique learning environment in engineering education. Android Java-DSP (AJDSP) is a mobile educational application that interfaces with sensors and enables simulation and visualization of signal processing concepts. In this paper, we present the work done towards building non-invasive physiological signal monitoring tools in AJDSP through hardware interfaces to both external sensors and on-board device sensors. Examples of laboratory exercises that can be introduced in classes are presented. The proposed software tools can be used to provide intuitive understanding in wireless sensing and feature extraction to demonstrate the application of DSP to health monitoring systems. The effectiveness of the software modules in enhancing student understanding is demonstrated with the help of preliminary assessments.

Work in progress: Performing signal analysis laboratories using Android devices

In this paper, we present a graphical-programming application to support signal processing education on the Android operating system. This application features a simulation environment and a palette of DSP functions, which will allow students to perform laboratories using Android smartphones and tablets. In order to demonstrate the application of the software in a classroom setting, a number of laboratories which incorporate the proposed functionalities have been developed. A set of assessments designed to evaluate the effectiveness of the software is also presented.

Understanding and Identifying Advocates for Political Campaigns on Social Media

Social media is increasingly being used to access and disseminate information on sociopolitical issues like gun rights and general elections. The popularity and openness of social media makes it conducive for some individuals, known as advocates, who use social media to push their agendas on these issues strategically. Identifying these advocates will caution social media users before reading their information and also enable campaign managers to identify advocates for their digital political campaigns. A significant challenge in identifying advocates is that they employ nuanced strategies to shape user opinion and increase the spread of their messages, making it difficult to distinguish them from random users posting on the campaign. In this paper, we draw from social movement theories and design a quantitative framework to study the nuanced message strategies, propagation strategies, and community structure adopted by advocates for political campaigns in social media. Based on observations of their social media activities manifesting from these strategies, we investigate how to model these strategies for identifying them. We evaluate the framework using two datasets from Twitter, and our experiments demonstrate its effectiveness in identifying advocates for political campaigns with ramifications of this work directed towards assisting users as they navigate through social media spaces.

A tool for assisting provenance search in social media

In recent years, social media sites are witnessing an information explosion. Determining the reliability of such a large amount of information is a major area of research. Information provenance (aka, sources or origin) provides a way to measure the reliability of information in social networks. The main challenge in seeking provenance is the availability of suitable data consisting of sufficient unique propagation paths. Knowledge of the actual propagation paths for a piece of information will be a valuable asset in provenance search. This paper presents a tool for capturing the propagation network of a given tweet or URL (Uniform Resource Locator) in the Twitter network. Researchers can use this tool to collect information propagation data, design effective strategies for determining the provenance, and gain information about the tweet such as impact, growth rate and users influencing the spread. Two case studies are presented to demonstrate the effectiveness of the system for seeking provenance information.

Finding Time-Critical Responses for Information Seeking in Social Media

Social media is being increasingly used to request information and help in situations like natural disasters, where time is a critical commodity. However, generic social media platforms are not explicitly designed for timely information seeking, making it difficult for users to obtain prompt responses. Algorithms to ensure prompt responders for questions in social media have to understand the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to model the future availability and past response behavior of the candidate responders. We integrate these criteria with their interests to identify users who can provide timely and relevant responses to questions posted in social media. We propose a learning algorithm to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media.

Facilitating Time Critical Information Seeking in Social Media

Social media plays a major role in helping people affected by natural calamities. These people use social media to request information and help in situations where time is a critical commodity. However, generic social media platforms like Twitter and Facebook are not conducive for obtaining answers promptly. Algorithms to ensure prompt responders for questions in social media have to understand and model the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to identify users who can provide timely and relevant responses to questions posted on social media. We first draw from these theories to model the future availability and past response behavior of the candidate responders and integrate these criteria with user relevance. We propose a learning algorithm from these criteria to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media and use them to evaluate our framework. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media.

Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests

The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future . Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.

Understanding and Identifying Rhetorical Questions in Social Media

Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and are an important tool employed by users to express their viewpoints. Therefore, rhetorical questions might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this article, we develop a framework to identify rhetorical questions by modeling some motivations of the users to post them. We focus on two motivations of the users drawing from linguistic theories to implicitly convey a message and to modify the strength of a statement previously made. We develop a quantitative framework from these motivations to identify rhetorical questions in social media. We evaluate the framework using two datasets of questions posted on a social media platform Twitter and demonstrate its effectiveness in identifying rhetorical questions. This is the first framework, to the best of our knowledge, to model the possible motivations for posting rhetorical questions to identify them on social media platforms.