Computer Sciences Commons^™

Exploring Confidentiality Issues In Hyperledger Fabric Business Applications, Shivam Bajpayi, Pedro Moreno-Sanchez, Donghang Lu, Sihao Yin

The Summer Undergraduate Research Fellowship (SURF) Symposium

The rise of Bitcoin and cryptocurrencies over the last decade have made its underlying technology (blockchain) come into the spotlight. Blockchain is a secure ledger of linked records called blocks. These records are cryptographically immutable and any tampering with the block is evident through a change in the cryptographic signature of the block. Among the blockchains deployed in practice today, Hyperledger Fabric is a platform that allows businesses to make use of blockchains in their applications. However, confidentiality issues arise with respects to the blocks in this blockchain network due to the fact that blocks might contain sensitive information accessible …

Measuring Symmetry In Real-World Scenes Using Derivatives Of The Medial Axis Radius Function, Morteza Rezanejad, John D. Wilder, Kaleem Siddiqi, Sven Dickinson, Allan Jepson, Dirk B. Walther

MODVIS Workshop

Symmetry has been shown to be an important principle that guides the grouping of scene information. Previously, we have described a method for measuring the local, ribbon symmetry content of line-drawings of real-world scenes (Rezanejad, et al., MODVIS 2017), and we demonstrated that this information has important behavioral consequences (Wilder, et al., MODIVS 2017). Here, we describe a continuous, local version of the symmetry measure, that allows for both ribbon and taper symmetry to be captured. Our original method looked at the difference in the radius between successive maximal discs along a symmetric axis. The number of radii differences in …

Intersection Of Art And Science Ii, Petronio Bendito, Tim Korb

Lawson Building Exhibitions on the Intersection of Art and Science

The Intersection of Art and Science exhibition is an interdisciplinary educational project that examines a wide range of expressive approaches explored by international artists working at the intersection of art, mathematics, computer science, and technology. It is a joint collaboration between the Department of Computer Science and the Patti and Rusty Rueff School of Visual and Performing Arts at Purdue University. Featured artists: Manuel Baez, Sarah Berube, Robert Bosch, Sage Jenson, Jean-Marc Castera, Marc Chamberland, Marion Chamberland, Laura De Decker, Bathsheba Grossman, Colin Liotta, Vincent J. Matsko, Kerry Mitchell, Janna Schimka, Rolf Schmuck, and Markus Schwehm. The exhibition was curated …

A Longitudinal Analysis On The Feasibility Of Iris Recognition Performance For Infants 0-2 Years Old, Torrey Hutchison

Purdue Polytechnic Masters Theses

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) …

Wrangle Your Data Like A Pro With The Data Processing Power Of Python, Geoffrey P. Timms, Jeremy M. Brown

Charleston Library Conference

Management, delivery, and marketing of library resources and collections necessitate interaction with a plethora of data from many sources and in many forms. Accessing and transforming data into meaningful information or different formats used in library automation can be time consuming, but a working knowledge of a programming language can improve efficiency in many facets of librarianship. From processing lists to creating extensible markup language (XML), from editing machine-readable cataloging (MARC) records before upload to automating statistical reports, the Python programming language and third-party application programming interfaces (APIs) can be used to accomplish both behind-the-scenes tasks and end-user facing projects. …

Visually Analyzing The Impacts Of Essential Air Service Funding Decisions, Rohan Kashuka, Chittayong Surakitbanharn, Calvin Yau, David S. Ebert

The Summer Undergraduate Research Fellowship (SURF) Symposium

Essential Air Service (EAS) is a U.S. government subsidy program which ensures maintenance of commercial airline services in small deregulated communities. The program’s budget currently is around $250 million annually, which is used as subsidy for airlines to maintain a minimal level of scheduled air service in relatively smaller airports. It is evident that 2% of the FAA budget is being spent to maintain air service in smaller communities, but there is not enough evidence to prove that all the current decisions made by Congress about EAS are advantageous. To understand these decisions, 15 years of data produced by the …

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model) …

Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Graphene is a 2-dimensional element of high practical importance. Despite its exceptional properties, graphene’s real applications in industrial or commercial products have been limited. There are many methods to produce graphene, but none has been successful in commercializing its production. Roll-to-roll plasma chemical vapor deposition (CVD) is used to manufacture graphene at large scale. In this research, we present a Bayesian linear regression model to predict the roll-to-roll plasma system’s electrode voltage and current; given a particular set of inputs. The inputs of the plasma system are power, pressure and concentration of gases; hydrogen, methane, oxygen, nitrogen and argon. This …

Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei

The Summer Undergraduate Research Fellowship (SURF) Symposium

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones …

Development Of A Water Quality Status And Trend Detection Tool*, Ruchir Aggarwal, Valeria Mijares, Margaret W. Gitau

The Summer Undergraduate Research Fellowship (SURF) Symposium

Water Quality Index (WQI) models have been developed since the early 1970s. They present a means by which water quality status and trends can be compared across time and space on the basis of a composite value computed using existing water quality data. There is a need for a tool that can bring the different water quality parameters together and calculate the WQIs so as to facilitate data use in predictive modeling and water quality management. We are developing a software tool that can be used by water quality managers and others with different technical backgrounds to calculate WQI of …

Purdue Airsense: An Affordable Way To Measure And Study Air Pollution, Stephane Junior Nouafo Wanko, Shadi Tariq Azouz, Ruihang Du, Brandon Boor, Greg Michalski

The Summer Undergraduate Research Fellowship (SURF) Symposium

Air pollution is a major health hazard worldwide, accounting for one-eighth of all deaths in 2012 (World Health Organization). Globally, there is a severe lack of ground-based spatiotemporal monitoring of gaseous and particulate air pollutants, particularly in Africa, South and Central America, and the Middle East. This is in great part due to the high costs of air quality instrumentation that meet accuracy and reliability criteria set by monitoring agencies. The air quality data that is available is often not presented to the public in a user-friendly manner. Taking advantage of recent developments in low-cost sensing technologies, an integrated sensor …

Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson

The Summer Undergraduate Research Fellowship (SURF) Symposium

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, …

Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers …

Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan

The Summer Undergraduate Research Fellowship (SURF) Symposium

Atomistic and molecular simulations have become an important research field due to the progress made in computer performance and the necessity of new and improved materials. Despite this, first principle simulations of large molecules are still not possible because the high computational time and resources required. Other methods, such as molecular dynamics, allow the simplification of calculations by defining energy terms to describe multiple atom interactions without compromising accuracy significantly. A group of these energy terms is called a force field, and each force field has its own descriptions and parameters. The objective of this project was to develop a …

Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko

The Summer Undergraduate Research Fellowship (SURF) Symposium

Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present a …

Parallelization Of Molecular Docking Algorithms Using Cuda For Use In Drug Discovery, Brandon Stewart, Jonathan Fine, Gaurav Chopra Phd

The Summer Undergraduate Research Fellowship (SURF) Symposium

Traditional drug discovery methodology uses a multitude of software packages to design and evaluate new drug-like compounds. While software packages implement a wide variety of methods, the serial (i.e. single core) implementation for many of these algorithms, prohibit large scale docking, such as proteome-wide docking (i.e. thousands of compounds with thousands of proteins). Several docking algorithms can be parallelized, significantly reducing the runtime of the calculations, thus enabling large-scale docking. Implementing algorithms that take advantage of the distributed nature of graphical processing units (GPUs) via the Compute Unified Device Architecture (CUDA) enables us to efficiently implement massively parallel algorithms. Two …

A Pattern Approach To Examine The Design Space Of Spatiotemporal Visualization, Chen Guo

Department of Computer Graphics Technology Degree Theses

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The …

The Introduction Of Informal Cooperative Learning Into Our Programming Laboratories, Guity Ravai, Ludmila Nunes, Ronald Erdei

IMPACT Presentations

Presented at the Women in Engineering ProActive Network (WEPAN) Change Leader Forum: Creating a Mindset for Action in Westminster, CO, USA

Learning To Identify Depth Edges In Real-World Images With 3d Ground Truth, Krista A. Ehinger, Kevin T. Joseph, Wendy J. Adams, Erich W. Graf, James H. Elder

MODVIS Workshop

No abstract provided.

Scoring Scene Symmetry, Morteza Rezanejad, John D. Wilder, Sven Dickinson, Allan Jepson, Dirk B. Walther, Kaleem Siddiqi

MODVIS Workshop

No abstract provided.

Large-Scale Discovery Of Visual Features For Object Recognition, Drew Linsley, Sven Eberhardt, Dan Shiebler, Thomas Serre

MODVIS Workshop

A central goal in vision science is to identify features that are important for object and scene recognition. Reverse correlation methods have been used to uncover features important for recognizing faces and other stimuli with low intra-class variability. However, these methods are less successful when applied to natural scenes with variability in their appearance.

To rectify this, we developed Clicktionary, a web-based game for identifying features for recognizing real-world objects. Pairs of participants play together in different roles to identify objects: A “teacher” reveals image regions diagnostic of the object’s category while a “student” tries to recognize the object. Aggregating …

Perception Of Performance Indicators In An Agri-Food Supply Chain, Sweta Chopra, Chad Laux, Edie Schmidt, Prashant Rajan

Faculty Publications

Availability of nutritious, safe food, and guaranteed resources for a household to acquire food at all times results in food security. Availability of food at affordable price remains the greater challenge. Currently, the Indian government runs the Public Distribution System to provide commodity subsidies to households. Various government and private stakeholders are involved in making this procurement, storage, processing and distribution process work. The involvement of a large number of stakeholders, each with different interests, increases the complexity and difficulty to identify performance indicators of the supply chain. Current research evaluates the relationship among the participating stakeholders in the public …

The Introduction Of Informal Cooperative Learning Into Our Programming Laboratories, Guity Ravai, Ronald Erdei, Ludmila Nunes, Sahithya Kodam

IMPACT Presentations

Presentation given at the Midwest SoTL Conference. Discusses the effects of implementing informal cooperative learning techniques in an introductory programming course.

Prediction Of Local Quality Of Protein Structure Models Considering Spatial Neighbors In Graphical Models., Woong Hee Shin, Xuejiao Kang, Jian Zhang, Daisuke Kihara

Department of Biological Sciences Faculty Publications

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method …

Efficient Processing Of Similarity Queries With Applications, Mingjie Tang

Open Access Dissertations

Today, a myriad of data sources, from the Internet to business operations to scientific instruments, produce large and different types of data. Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological applications, call for identifying and processing similarities in "big" data. As a result, it is imperative to develop new similarity query processing approaches and systems that scale from low dimensional data to high dimensional data, from single machine to clusters of hundreds of machines, and from disk-based to memory-based processing. This dissertation introduces and studies several similarity-aware query operators, analyzes and optimizes their performance.

The first …

Students' Explanations In Complex Learning Of Disciplinary Programming, Camilo Vieira

Open Access Dissertations

Computational Science and Engineering (CSE) has been denominated as the third pillar of science and as a set of important skills to solve the problems of a global society. Along with the theoretical and the experimental approaches, computation offers a third alternative to solve complex problems that require processing large amounts of data, or representing complex phenomena that are not easy to experiment with. Despite the relevance of CSE, current professionals and scientists are not well prepared to take advantage of this set of tools and methods. Computation is usually taught in an isolated way from engineering disciplines, and therefore, …

Lagrangian Analysis Of Vector And Tensor Fields: Algorithmic Foundations And Applications In Medical Imaging And Computational Fluid Dynamics, Zi'ang Ding

Open Access Dissertations

Both vector and tensor fields are important mathematical tools used to describe the physics of many phenomena in science and engineering. Effective vector and tensor field visualization techniques are therefore needed to interpret and analyze the corresponding data and achieve new insight into the considered problem. This dissertation is concerned with the extraction of important structural properties from vector and tensor datasets. Specifically, we present a unified approach for the characterization of distinguished manifolds that form the skeleton of vector and tensor fields and play a key role in understanding their properties.

This dissertation makes several important contributions in this …

Security Techniques For Sensor Systems And The Internet Of Things, Daniele Midi

Open Access Dissertations

Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues.

We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in …

Qos And Trust Prediction Framework For Composed Distributed Systems, Dimuthu Undupitiya Gamage

Open Access Dissertations

The objective of this dissertation is to propose a comprehensive framework to predict the QoS and trust (i.e, the degree of compliance of a service to its specification) values of composed distributed systems created out of existing quality-aware services. We improve the accuracy of the predictions by building context-aware models and validating them with real-life case studies. The context is the set of environmental factors that affect QoS attributes (such as response time and availability), and trust of a service or a composed system. The proposed framework uses available context-QoS dependency information of individual services and information about the interaction …