Computer Science and Engineering at USC

Parsimonius Sociology Theory Construction: From A Computational Framework To Semantic-Based Parsimony Analysis

Author : Mingzhe Du
Advisor : Dr. Jose Vidal
Date : Feb 5, 2020
Time : 3 pm
Place : Meeting Room 2267, Innovation Center

Abstract

In social sciences, theories are used to explain and predict observed phenomena in the natural world. Theory construction is the research process of building testable scientific theories to explain and predict observed phenomena in the natural world. The conceptual new ideas and meanings of theories are conveyed through carefully chosen definitions and terms.

The principle of parsimony, an important criterion for evaluating the quality of theories (e.g., as exemplified by Occam's Razor) mandates that we minimize the number of definitions (terms) used in a given theory. Conventional methods for theory construction and parsimony analysis are based on the heuristic approaches. However, it is not always easy for young researchers to fully understand the theoretical work in a given area because of the problem of ``tacit knowledge'', which often makes results lack coherence and logical integrity. In this research, we propose to help with this problem in three parts.

In particular, for the first part of this study, we present Wikitheoria, a generic knowledge aggregation framework to facilitate the parsimonious approach of theory construction with a cloud-based theory modularization platform and semantic-based algorithms to minimize the number of definitions. The presented approach is demonstrated and evaluated using the modularized theories from the database and sociological definitions retrieved from the system lexicon and sociological literature. This study proves the effectiveness of using the cloud-based knowledge aggregation system and semantic analysis models for promoting the parsimonious sociology theory construction.

In the second part, our study is focused on semantic-based parsimony analysis. We introduce an embedding-based approach using machine learning models to reduce the semantically similar sociological definitions, where definitions are encoded with word embeddings and sentence embeddings. Given several types of embeddings exist, we compare the definition's encodings with the goal of understanding what embeddings are more suitable for knowledge representation, and what classifiers are more capable for capturing semantic similarity in the task of parsimonious theory construction.

In the final part of this study, we propose SOREC, a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of low-level features and high-level features on our dataset. The proposed CBRS substantially outperforms conventional matrix factorization-based CBRS in suggesting semantically related sociological definitions. In this study, we provide a solid baseline for future studies in the research area of sociological definition semantic similarity computation. Moreover, theory construction is a common research process in a lot of human science-related disciplines such as psychology, criminology, and other social sciences. The results of this study can be further applied to the theory construction in these disciplines.

Parsimonius Sociology Theory Construction: From A Computational Framework To Semantic-Based Parsimony Analysis | Computer Science & Engineering January 28, 2020 Wednesday, February 5, 2020 - 3:00pm to 4:00pmMeeting Room 2267, Innovation CenterAuthor : Mingzhe Du Advisor : Dr. Jose Vidal Date : Feb 5, 2020 Time : 3 pm Place : Meeting Room 2267, Innovation Center Abstract In social sciences, theories are used to explain and predict observed p...

Outstanding Senior Award

Each year the Faculty of the Department of Computer Science and Engineering (CSE) award four Outstanding Senior Awards. This process is never easy given the many excellent and accomplished students in our program. This year, we have decided that the 2020 Computer Science and Engineering Outstanding Senior Awards go to:

Computer Engineering Outstanding Senior Award: Andres Pineda

Computer Engineering SCSPE Award: Caleb Conner

Computer Science Outstanding Senior Award: Samyuktha Comandur

Computer Information Systems Outstanding Senior Award: Joshua Mulliken

All awardees will be honored at the University Awards Day ceremony. In addition, Conner will be honored at a special awards banquet of the South Carolina Society of Professional Engineers.

Outstanding Senior Award | Computer Science & Engineering January 27, 2020 Each year the Faculty of the Department of Computer Science and Engineering (CSE) award four Outstanding Senior Awards. This process is never easy given the many excellent and accomplished students in our program. This year, we have decided that the 2020 Computer Science and Enginee...

Website for Baseball Staff

My name is Mike Current, and I am on the baseball staff here at the University of South Carolina. We are looking for someone to help us build a web app that will allow us to view our Tableau dashboards online. Currently, we house our dashboards in a shared Dropbox folder, but we would like to create a site that will allow us to access them on the web. We would like a home page, which would essentially be a table of contents, with links to each of our dashboards. If you need more information on what we are looking to do, please just let me know. Thanks for your help.

Mike Current
[email protected]
Director of Player Development
University of South Carolina
(O) 803-777-1704

Website for Baseball Staff | Computer Science & Engineering January 22, 2020 My name is Mike Current, and I am on the baseball staff here at the University of South Carolina. We are looking for someone to help us build a web app that will allow us to view our Tableau dashboards online. Currently, we house our dashboards in a shared Dropbox folder, but we wou...

REU: Surf the Waves with ASSET (Advanced Secured Sensor Enabling Technologies)

SURF THE WAVES with ASSET: ASSET:ADVANCED SECURED SENSOR ENABLING TECHNOLOGIES

MAY 28-AUG 4,2020

The application early action deadline is February 7, 2020 and regular deadline is April 15, 2020 or until positions are filled

Funded by NSF and DoD (P*s: Dr. Pissinou and Dr. Iyengar)

The Summer 2020 REU SITE of Florida International University Research Experience for Undergraduates in Advanced Secured Sensor Enabling Technologies spans ten weeks, May 28- August 4 2020, offers research and professional development opportunities for 10 undergraduate students.

Available research projects share the theme of advanced secured sensor enabling technologies and span several exciting areas such as

Trajectory Privacy Preservation in Mobile Sensor Networks,

Privacy & Secure Social Sensor Networks,

Mobile Video Authenticity Verifications

Search Rank Validation for Social Media

A Software Library for Visualizing Large Scale Sensor Network Applications,

Trajectory Sensor Stream Cleaning,

Resource Management for Security & Surveillance over a Sensor Network,

Self-configuring, non-cooperative Mobile Sensors

and many others

Details about REU-ASSET including an online application, can be found at

http://it2.fiu.edu/it2_REU.php

https://cse.sc.edu/job/reu-surf-waves-asset-advanced-secured-sensor-enabling-technologies

REU: Surf the Waves with ASSET (Advanced Secured Sensor Enabling Technologies) | Computer Science & Engineering January 22, 2020 SURF THE WAVES with ASSET: ASSET:ADVANCED SECURED SENSOR ENABLING TECHNOLOGIES MAY 28-AUG 4,2020 The application early action deadline is February 7, 2020 and regular deadline is April 15, 2020 or until positions are filled Funded by NSF and DoD (P*s: Dr. Pissinou and Dr. Iyengar) T...

Research Computing at UofSC DoIT Looking for Undergraduate

Research Computing at UofSC DoIT is looking for a self-initiative undergraduate to participate in Deep Learning (DL) optimization and High Performance Computing (HPC) benchmarking project. The successful candidate will work on the state-of-art industry-level HPC clusters and GPFS storage. The duties include: setting up benchmarking suite in cluster parallel environment, building necessary libraries, writing shell scripts to build frameworks and optimizing training DL models. This job opportunity can shine on your resume.

Knowledge/Skills/Abilities:

Experience with Linux administration, Shell Script and Python

Basic knowledge with Big Data, Machine Learning, and/or Deep Learning

Good Mathematics background, like in Linear Algebra, Statistics.

Good communication skill

GPA 3.0 and above

Immediate start is a big plus

We are paying $15 per hour, 10-15 hours per week for the semester with the potential for continuing internship for the summer. Should you be interested, please forward resume to [email protected].

Research Computing at UofSC DoIT Looking for Undergraduate | Computer Science & Engineering January 21, 2020 Research Computing at UofSC DoIT is looking for a self-initiative undergraduate to participate in Deep Learning (DL) optimization and High Performance Computing (HPC) benchmarking project. The successful candidate will work on the state-of-art industry-level HPC clusters and GPFS st...

Potential student with data science and GIS skills

I am writing to inquire if you have students or could forward to students who might be interested in working on project related to water supply policy in Kenya.

The project includes the cleaning and basic analysis of a large, multi year dataset of household water use in Kenya. The student must be detail oriented and have the skills and aptitude to clean and validate panel data. (The task can be performed in Stata, r, Matlab or Python). Given that the data have a spatial component, the student should also have the ability to perform basic GIS mapping and analysis. Someone with an interest in data visualization would be ideal.

Depending on the student’s preferences, I can structure the work as independent research, an independent study, or a paid hourly research assistantship.

I am keen to get started ASAP on this work. Please have interested students contact me [email protected].

Many thanks in advance!

David Fuente
Assistant Professor
School of Earth, Ocean and Environment

Potential student with data science and GIS skills | Computer Science & Engineering January 16, 2020 I am writing to inquire if you have students or could forward to students who might be interested in working on project related to water supply policy in Kenya. The project includes the cleaning and basic analysis of a large, multi year dataset of household water use in Kenya. The s...

University Housing Technology Services Student Employee

Duties

• Maintain and update computers across various University Housing offices on campus
• Troubleshoot and resolve client’s various computer related issues
• Install new hardware and software as needed
• Check in and out various IT equipment to Housing Staff
• Check in and out of temporary building access cards to Housing Staff
• Troubleshoot student and staff access control issues
• Update various software and hardware as needed
• Answer phones as needed
• Duties as assigned

Reports to
Director of University Housing Technology Services
Compensation
$11+/hour
Contact Information
Please email resume and Fall 2018 class schedule to:
Ronald Stroman
Desktop Support Manager
Technology Services
University Housing
1520 Devine Street | Columbia, SC 29208
(p) 803-576-6034
[email protected]

University Housing Technology Services Student Employee | Computer Science & Engineering January 14, 2020 Duties • Maintain and update computers across various University Housing offices on campus • Troubleshoot and resolve client’s various computer related issues • Install new hardware and software as needed • Check in and out various IT equipment to Housing Staff • Check i...

Magellan Scholars: Spring 2020

We congratulate the following Computer Science undergraduate students for receiving a Magellan Scholar Award for Spring 2020.

Stephen Baione: Ensemble of Many Weak Defenses: Defending Deep Neural Networks Against Adversarial Attacks

Blake Edwards: Multi-stage Compression of Deep Neural Networks through Pruning and Knowledge Distillation

Shayon Ghoshroy: Deep Residual Learning for Computational Identification of Amino Acids

Clio Lang: Is the Lesson Learned? A Study on Dissemination of Fake News in 2016 and 2020 Presidential Elections

Christian Loftis: Genetic programming based Symbolic Regression for Material Thermal Conductivity Prediction

Tien Nguyen: How Well Can the Robot Swim: Quantifying the Performance of Aqua_Nav

Julian Rachele: Expanding upon REDCRAFT and REDCAT for Analyzing and Characterizing Proteins from Residual Dipolar Couplings

Naga Venkata Sai Satti: MassBLAST: An Automated and Convenient Webtool for Genome-Wide Sequence Analysis

Magellan Scholars: Spring 2020 | Computer Science & Engineering December 17, 2019 We congratulate the following Computer Science undergraduate students for receiving a Magellan Scholar Award for Spring 2020. Stephen Baione: Ensemble of Many Weak Defenses: Defending Deep Neural Networks Against Adversarial Attacks Blake Edwards: Multi-stage Compression of Deep Ne...

Hunter Damron Receives Honorable Mention for CRA Outstanding Undergraduate Research

We congratulate Hunter Damron for receiving an Honorable Mention for the Computing Research Association's (CRA) Outstanding Undergraduate Researcher Award for 2020. This award program recognizes undergraduate students in North American colleges and universities who show outstanding research potential in an area of computing research.

Hunter Damron Receives Honorable Mention for CRA Outstanding Undergraduate Research | Computer Science & Engineering December 17, 2019 We congratulate Hunter Damron for receiving an Honorable Mention for the Computing Research Association's (CRA) Outstanding Undergraduate Researcher Award for 2020. This award program recognizes undergraduate students in North American colleges and universities who show outstanding...

Google Internships for UofSC Students

Following their visit, Google is interested in hiring more UofSC CSE students:

I apologize for the delay in being able to share this, but Google staffing has just created a special link for us to give to you in order to make the Intern Application Process as simple as possible for your students to apply for Google internships. Some of the deadlines unfortunately are close to passing and so please pass along the below links to apply as soon as possible to any relevant students you have in mind. And best wishes to them! Happy to answer any questions/concerns.

Happy Holidays and look forward to corresponding with you soon in the new year!

Degree: Bachelors
Deadline: September 16, 2019- December 13, 2019
https://careers.google.com/jobs/results/123846938287055558-software-engi...

Degree: Masters
Deadline: September 16, 2019- December 13, 2019
https://careers.google.com/jobs/results/128054769286554310-software-engi...

Degree: PhD
Deadline: September 16, 2019- Feb. 7 2020
https://careers.google.com/jobs/results/106680263242588870-software-engi...

https://cse.sc.edu/job/google-internships-uofsc-students

Google Internships for UofSC Students | Computer Science & Engineering December 10, 2019 Following their visit, Google is interested in hiring more UofSC CSE students: I apologize for the delay in being able to share this, but Google staffing has just created a special link for us to give to you in order to make the Intern Application Process as simple as possible for....

Ensembles of Many Weak Defenses are Strong: Defending Deep Neural Networks Against Adversarial Attacks

Ying Meng and Jianhai Su

Abstract:
Despite achieving state-of-the-art performance across many domains, deep neural networks (DNN) are highly vulnerable to subtle adversarial perturbations. Defense approaches have been proposed in recent years, many of which have been shown inefficient by researchers. Early study suggests that ensembles created by combining multiple weak defenses are still weak. However, we observe that it is possible to construct efficient ensembles using many weak defenses. In this work, we implement and present 5 strategies to construct efficient ensembles from many (possibly weak) defenses that comprise transforming the inputs (e.g. rotation, shifting, noising, denoising, and many more) before feeding them to the classifier. We test our ensembles with adversarial examples generated by various adversaries (27 sets generated by 9 different adversarial attack methods, such as FGSM, JSMA, One-Pixel, etc.) on MNIST and investigate the factors that may impact the effectiveness of an ensemble model. We evaluate our ensembles via 4 threat models (i.e., white-box, gray-box, black-box, and zero-knowledge attacks). Also, we study and attempt to explain, empirically, how a transformation blocks perturbations generated by an adversary.

Ensembles of Many Weak Defenses are Strong: Defending Deep Neural Networks Against Adversarial Attacks | Computer Science & Engineering December 6, 2019 Friday, December 6, 2019 - 2:20pm to 3:20pmStorey Innovation Center (Room 1400)Ying Meng and Jianhai Su Abstract: Despite achieving state-of-the-art performance across many domains, deep neural networks (DNN) are highly vulnerable to subtle adversarial perturbations. Defense approac...

Three Students Selected for Grad Cohort For Women

We would like to congratulate:

Utkarshini Jaimini (PhD Student)

Revathy Venkataramanan (PhD Student)

Ananya Banerjee (Intern)

who have been selected to participate in the CRA-WP Grad Cohort for Women. They are all part of our AI Institute.

Three Students Selected for Grad Cohort For Women | Computer Science & Engineering December 1, 2019 We would like to congratulate: Utkarshini Jaimini (PhD Student) Revathy Venkataramanan (PhD Student) Ananya Banerjee (Intern) who have been selected to participate in the CRA-WP Grad Cohort for Women. They are all part of our AI Institute. Google Plus One