2019 Poster Session
GHC hosts a mind-expanding poster session for students, faculty members, and industry professionals. We are honored to be joined by these poster presenters, some of whom are also competing in the distinguished ACM Student Research Competition. Learn about the innovative technology the next generation is dreaming up.
Poster Session 1
Wednesday, October 2
12 — 2:30 p.m.
Poster Session 2
Wednesday, October 2
3 — 5:30 p.m.
Poster Session 3
Thursday, October 3
10 a.m. — 12:30 p.m.
Poster Session 4
Thursday, October 3
2 — 4:30 p.m.
Poster Session 5
Friday, October 4
10 a.m. — 12:30 p.m.
- Cloud Computing
- Computer Architecture and Hardware Engineering
- Computer Graphics
- Databases/Information Retrieval/Data Mining
- Hardware: Hardware outside the CPU
- Hardware: Makers
- Machine Learning
- Mobile Technologies
- Open Source
- Operating Systems
- Algorithms and Data Structures
- Organizational Transformation
Poster Session 1
Wednesday, October 2, 12 — 2:30 p.m.
Computer Science Education, Broadening participation in computer science
Assessing the effects of gamification on computer science students: A gender study on performance
Presenter: Leila Zahedi, Florida International University
Despite recent improvements, we still see women’s underrepresentation in computing fields. By presenting challenging courses in a way that makes learning more enjoyable, students will be more likely to consider computing careers later in life. This quantitative study assesses if gamiﬁcation-using game elements-can improve students’ performance in a gamiﬁed online learning environment.
Broadening Participation: Inclusion of Female Computing Students in Competitive Programming
Presenter: Tamanna Motahar, North South University and University of Massachusetts - Amherst
This study describes an initiative for the inclusiveness of the female undergrad CS students into competitive programming in a developing country through “female only” environment. Many of the female CS students face visible and invisible barriers on their paths to comply with their computing career. This initiative was designed to focus on how they can overcome those barriers of participation.
Computer Science Mentors & Undergraduate Women Share Strategies for Academic Success & Persistence
Presenter: Breauna Spencer, University of California, and University of California, Irvine (UCI)
The study examined how faculty and graduate student mentors including undergraduate women enrolled in CS collectively articulated of the barriers and challenges women encounter in CS including ways to ameliorate these problems. Findings suggest that the mentors and students were equally aware of the challenges women faced in CS, and have crafted best practices and strategies to retain women in CS.
Influences of Friends and Family on Women’s Pursuit of Computing: A Sequential Explanatory Design
Presenter: Maral Kargarmoakhar, Florida International University
The participation of female students in computing fields remain low. This study examined the most important factors influencing female students computing occupation. The impact of family and friends on four different racial groups including White, Black, Hispanic, and Asian (n=1650) were examined. Results indicated friends had a positive significant role on White female students major decisions.
Information and Communications Technology
Accelerating Autonomous Driving with Blockchain Technology
Presenter: Nagashree Tadapatri Gundu Rao, SAP
More than 1 billion cars travel the streets of the world today. While cars have made our lives easier and comfortable, they have also made our lives riskier. Autonomous vehicles bring structure to the chaos. With the advancements in automotive industry and technologies like Blockchain, self-driving cars are getting closer to becoming a viable method of transportation that will make driving safer.
A Cooperative Driving Performance Rating System for Defensive Reckless Driving Alerting
Presenter: Lan Zhang, University of Florida
Reckless driving severely threatens the safety of people, which accounts for around 33% of all fatalities in major vehicle accidents. Most existing efforts focus on the detection and adjustment of a vehicle’s own driving behaviors with limited effectiveness. We propose a defensive alerting system to automatically and intelligently detect and notify the threats posed by reckless vehicles.
Natural Language Processing
Customer Sentiment Analysis with Extracted Keywords Using Lexical Chains and SentiWords
Presenter: Chhavi Sharma, Microsoft
This paper uses customer reviews to extract keywords for a product and gives sentiment score using ‘SentiWords’ on top of extracted keywords. Reviews are scored using lexical chains, and WordNet provides synsets to identify relevant words. These keywords and sentiment score are useful for providing summary of customer sentiments without having to read the entire data and help in business analysis.
A Parallel Hierarchical Attention Network for Style Change Detection
Presenter: Marjan Hosseinia, University of Houston
We propose a parallel attention network for style change detection. Given a document, it verifies if it has at least one writing style change. Unlike the conventional recurrent networks that use word sequences to learn the language model, our model learns the hierarchical structure of English language using a pretrained statistical parser. It stays at the second rank of PAN 2018 with 82% accuracy.
Poster Session 2
Wednesday, October 2, 3 — 5:30 p.m.
ACM Student Research Competition (ACM SRC)
CNN+LSTM model for classification of Twitter messages
Presenter: Alisa Gazizullina, Innopolis University
Tweets majorly containing words of idiosyncratic nature are difficult to analyze. In order to prevent loss of the information introduced by context and out-of-vocabulary words appearing in the training set, we design models capturing the specific nature of Twitter posts. Our hybrid CNN+RNN model trained on fastText embeddings outperforms current state-of-the-art models predicting message deletion.
Reducing tag identification time in a molecular tagging system
Presenter: Aishwarya Mandyam, University of Washington, Seattle - Computer Science and Engineering
A molecular tagging system with DNA based tags can provide a secure, difficult to fake identifier. An optimal system minimizes the time needed to accurately identify DNA molbits, which are unique DNA sequences. I created an error analysis pipeline that quantifies the minimum subset of reads on a MinION Nanopore sequencer necessary to produce a similar identification error rate as the entire set.
Human Protein Atlas Image Classification
Presenter: Akshita Bhagia, University of Massachusetts, Amherst
This task aims to assign protein organelle localization labels to protein samples. The ResNet34 model has been fine-tuned using different learning rates for different parts of the network. Focal loss is used as the loss function. Output is the set of target labels, and different thresholding schemes are evaluated for obtaining labels from class scores. Performance is evaluated on macro F1-score.
Internet of Things
Correlated Sensor-based Data Fusion in Mobile Crowdsensing
Presenter: Samia Tasnim, Florida International University
With the increase in smartphone usage, a new mode of data collection named mobile crowdsensing (MCS) has emerged. However, its open structure allows malicious entities to interrupt a system by reporting erroneous data. The goal of this research is to show that the introduction of a reputation system in the correlated sensor-based data fusion will enhance the overall quality of the sensed data.
Exploring Ensemble Learning for Anomaly Detection on Smart Home IoT System
Presenter: Zhaochen Gu, University of North Texas
The massive scale of Internet of Things (IoT) device deployment enables access to a wide gamut of data generated by these devices. Anomalies may occur where the devices fail to self-diagnose themselves. We aim to build a model from heterogeneous data and differentiate good situations from the bad. Our solution to anomaly detection of smart home devices is our aggregate model learning system.
Avoiding Energy Hole and Coverage Hole Problem in Wireless Sensor Networks Using Matrix Completion
Presenter: Ridhi LNU, Expedia and Indira Gandhi Delhi Technical University for Women
The aim is to avoid energy and coverage holes using correlation and matrix completion in a network. Nodes deployed in a corona-like structure; coalitions are made using correlation among these nodes. The data of coalitions sent to the sink is processed using matrix completion to retrieve data of all nodes at the sink; that reduced the number of messages sent to sink; thus conserving energy.
A Novel Timestamping Mechanism for Clouds and Its Application on Available Bandwidth Estimation
Presenter: Phuong Ha, University of Nebraska Lincoln
The packet time information at receivers carries useful network information. It is challenging to accurately measure this information in a cloud network due to various factors such as virtual machine (VM) scheduling. We propose a novel packet timestamping mechanism and demonstrate its application using an improved available bandwidth estimation tool.
Poster Session 3
Thursday, October 3, 10 a.m. — 12:30 p.m.
Big Data/Supercomputing, High Performance/Parallel Computing
Hybrid Parallelization of Particle in Cell Monte Carlo Collision (PIC-MCC) algorithm
Presenter: Unnati Parekh, Dhirubhai Ambani Institute of Information and Communication Technology
Kinetic simulation of plasma with PIC method involves numerical constraints on simulation parameters and time-scale making it computationally prohibitive on CPUs for large problem sizes. We illustrate hybrid parallelization of PIC code and compare the performance with shared-memory based parallel code. Our technique scales efficiently with increasing problem size and number of cores in cluster.
Transforming Query Sequences for High-Throughput B+ Tree Processing on Many-Core Processors
Presenter: Ruiqin Tian, College of William and Mary
With modern CPUs, it is possible to process larger batches in parallel in latch-free B+ tree query processing model without extra delays. With increased batch size, there will be more optimization opportunities exposed beyond parallelism, especially under highly skewed query distributions, such as avoiding redundant and unnecessary queries. We propose QTrans to efficiently solve this problem.
Scalable Algorithmic Methods for Large-scale Graph Mining
Presenter: Naw Safrin Sattar, University of New Orleans
Graph mining is of great importance in solving real-world problems in many application domains. Community detection is a computationally challenging problem, solved efficiently with Louvain Algorithm.We design parallel algorithms using OpenMP, MPI,use dynamic load-balancing to achieve around 12-fold speedup.We also present a brief overview of our work on temporal graphs.
Biotechnology/Bioinformatics, Health Sciences, Computational Biology
Predictive Modeling of RNAs for Permanent Shunt Placement in Pediatric Intraventricular Hemorrhage
Presenter: Komal Agrawal, Translational Genomics Research Institute
Intraventricular hemorrhage (IVH) is a complication in premature infants. Babies that develop hydrocephalus as a result of IVH often require a permanent ventriculoperitoneal shunt. Here, we will identify RNAs correlated with shunt placement and develop a predictive model based on RNA expression. Our predictive model could potentially assist clinical decision-making for patients with IVH.
Using Virtual Reality to Examine the Anxiety-Related Response
Presenter: Rachneet Kaur, University of Illinois at Urbana Champaign
We examine a novel virtual reality (VR) based experimental setup to study the anxiety-related responses in balance-demanding target-reaching leaning. An EEG and EKG data collection system is established to analyze the real-time responses from subject’s brain and heart. The Brain-computer interface system integrating VR and online anxiety feedback provides the technology to study task performance.
Learning models for writing better doctor prescriptions
Presenter: Tingting Xu, Boston University
We develop a data-driven treatment recommendation framework to predict physicians’ prescription effects, learn and improve the doctors’ prescription policy by optimizing over the estimated policy model. With the recommended prescription policy, diabetic patients see significant blood glucose reduction on average and obtain a better therapeutic effect than the state-of-art deterministic algorithms.
Constructing an Ecologically-Valid Formal Markovian Model of Human Activities
Presenter: Beiyu Lin, Washington State University
While pervasive computing technologies becoming mainstream for observing human behavior, few formal models have been theorized of behavior based on collected sensor data. We construct Markov models that express the nature of human behavior captured by smart home sensor data. We will evaluate if behavior becomes less Markovian and more random as individuals experience cognitive decline.
Single molecule subcellular analysis of RNA distribution in three dimensions using open source tools
Presenter: Pearl Ryder, Emory University
Cells are multifunctional units of life that coordinate tasks by generating intracellular domains of organelles and macromolecules, such as proteins and RNA. We developed an open-source approach to precisely analyze the distribution of individual RNAs relative to centrosomes, organelles involved in cell proliferation. Our approach can be generalized for spatial analysis of other microscopy images.
Augmenting Visual SLAM with WiFi Sensing For Indoor Applications
Presenter: Zakieh Sadat Hashemifar, State University of New York at Buffalo
A key enabling technology for spatial applications is understanding of the device’s location and the map of environment, referred as Simultaneous Localization and Mapping (SLAM). Visual SLAM algorithms suffer from perceptual aliasing and high computational cost. Since WiFi routers are ubiquitous in urban environments, we utilized the WiFi received signal strength to alleviate those challenges.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
Presenter: Ekaterina Tolstaya, University of Pennsylvania
We consider the problem of distributed control in large networks of mobile robots with limited communication. By extending aggregation graph neural networks to time varying signals and networks, we learn a common local controller which exploits information from distant teammates using only local communication. We demonstrate the performance of the decentralized flocking controller in simulation.
Catbot: Automated Triage Bot for Incident Management
Presenter: Kanika Sabharwal, Bloomberg
In this poster, we highlight how an automated bot can be a great solution for incident management in a company and a better solution than traditional incident management paradigms. To support our claim, we introduce Catbot, the incident management bot for our team in Bloomberg, its architecture, and how using it has had a big impact towards making incident management more effective in our team.
Website Performance: Cache can cost
Presenter: Kavita Asiwal, Intuit
Using a browser’s ability to cache resources eliminates the time to download resources and allows an application to load faster. Most engineers consider ways of using browser cache when designing a resource deployment strategy. This poster will highlight the cost of loading resources from a disk cache, the conditions when cache costs are high, and ways to reduce this cost.
Is Mobile App Tracking Really Anonymous and Aggregative?
Presenter: Xueling Zhang, University of Texas, San Antonio
Mobile app companies utilize third-party tracking service to learn their app users’ behavior. An app developer could set user attribute on tracking. This paper studies what attribute did app developers choose for tracking, whether they choose personally identifiable information (PII) such as social security number or email. We performed a study on 1,031 apps and found 125 use unencrypted PII.
Ancile: Attack Surface Reduction Through Application Specialization
Presenter: Priyam Biswas, Purdue University
Control-Flow Hijacking is the most common attack vector against C/C++ programs. Automatically specializing code for particular usage scenario is a promising new technique for software security. I present Ancile which implements required functionality analysis, achieves significantly smaller subset of functions by specializing the code, and substantially raising the bar for CFH attacks.
Poster Session 4
Thursday, October 3, 2—4:30 p.m.
An End-To-End Learning for Autonomous Driving: Steering Angle Prediction and Lane Keeping
Presenter: Yaqin Wang, Indiana University - Purdue University
he end-to-end approach is one of the frequently used approaches for the autonomous driving system. In this study, we adopt the end-to-end approach to train a deep convolutional neural network to steer a car in the Udacity simulator. Our proposed model map raw pixels from cameras of three different angles and generate steering commands for autonomous driving.
Ambient Assisted Dementia Care using Smart Home featuring Activity Recognition and Decision Support
Presenter: Gayathri K.S, Sri Venkateswara College of Engineering
Smart home, a ubiquitous environment aims to offer Ambient Assisted Living. The activity modeling framework integrates ambient intelligence into the home environment. This research proposes an assistive dementia care system through smart home that offers functional assistance to the dement occupant during critical situations without the help of caretaker.
CREU Students – Collaborative Research Experiences for Undergraduates
Chemical Cyber-attack Awareness
Presenter: Nia Blake, Winston-Salem State University
The American public never receives information about the vast majority of cyberattacks against U.S. industrial systems that control the operations of refineries, pipelines power plants, chemical plants, and other factories. These incidents are classified by the FBI and other agencies, and kept secret by U.S. companies unless a breach involves the theft of personal identifying information.
A Machine Learning Approach to Sleep Quality Prediction for Inpatient Rehabilitation
Presenter: Allison Fellger, Gonzaga University
Actigraph watches are a tool through which we can view the minute-by-minute sleep activity of unhealthy populations with minimum intrusion. While this data can be quite noisy, machine learning can help us analyze these irregular patterns. Through a machine learning approach, we predicted nighttime sleep duration for a group of post-stroke and post-traumatic brain injury patients.
DREU Students – Distributed Research Experiences for Undergraduates
Why Do Instagram Users Create Multiple Accounts?
Presenter: Liza Sivriver, Washington University in St. Louis
Increasingly, Instagram users maintain both public- and private-facing accounts, known as rinstas and finstas, respectively. This poster explores users’ goals in maintaining multiple accounts and how accounts interact with one another. I developed a system to compare and analyze differences between users’ rinstas and finstas with respect to content, network structure, and social metrics.
Evaluating the Perceptual Validity of Distance Metrics in Character Animation
Presenter: Katherine Gemperline, Xavier University
In character animation, it is important for many applications to know how similar two motions are. For this, the Root Mean Square Error is a commonly used distance metric. However, it does not always correctly map to our perception of similarities. In my research, I check several types of motion errors with the same similarity values. I investigate how these motions are perceived to help develop more perceptually valid distance metrics in the future.
Poster Session 5
Friday, October 4, 10—12:30 p.m.
External Consistent Transactions Using Logical Clocks
Presenter: Masoomeh Javidi Kishi, Lehigh University
We present SSS, a scalable transactional key-value store deploying a novel distributed concurrency control that provides external consistency and never aborts read-only transactions due to concurrency. SSS uses a combination of vector clocks and a new technique, called snapshot-queuing. Results show significant speedup over state-of-the-art competitors in read-dominated workloads.
Detecting latent bugs in Partially Synchronous Distributed Systems Using SMT Solvers
Presenter: Vidhya Tekken Valapil, Michigan State University
In this poster, we will discuss about the problem of detecting latent concurrency bugs in partially synchronized distributed systems. Partially synchronized distributed systems do not have a specific total order of events in the system. So evaluating all possible serializations of events in the system to guarantee correctness is a challenging task and we propose to use SMT solvers to handle this.
Computer Architecture and Hardware Engineering
High-level Functional Hardware Design
Presenter: Mahshid Shahmohammadian, Drexel University
Low-level languages for circuit descriptions, like VHDL, easily lead to errors in large designs. Much higher-level tools like Simulink yield fewer errors but lack modularity and efficiency. We show how language features of Haskell, like high-order functions, parametric polymorphism, and type classes, can provide a high-level design experience that reduces errors without sacrificing efficiency.
Energy Efficient Desktop Computers
Presenter: Jennifer Alphonse, Intel Corporation
Over the last two decades, the number of computer systems has exponentially increased. The number of mobile users has quintupled since the early 2000s. With this increase is the increased demand for power — a valuable commodity. This poster shows how software and hardware optimizations are used to design green, energy efficient systems that meet the aggressive low power requirements of desktop computers.
Mechanics-Aware Modeling of Cloth Appearance
Presenter: Zahra Montazeri, University of California, Irvine
Fabrics are essential in our daily lives so designing and modeling them virtually is important. Capturing high-detailed appearance is challenging while ignoring the fiber-level details yield to a fake look for cloth. We introduced a new-of-a-kind technique to model cloth under physical forces. Our model accurately reproduce the change of appearance when fabrics interact with the environment.
Automating Quality Control at Disney: A Novel Aspect Ratio Defect Detection Tool
Presenter: Anna Wolak, The Walt Disney Company
Before films are released to the public, they must be inspected for defects. Currently, quality control teams must perform the majority of these examinations manually, which is not only time-intensive but also prone to human error. At Walt Disney Studios, we have developed an automated tool for aspect ratio shift detection that outperforms state-of-the-art, out-of-the- box software applications.