Computer Sciences Commons^™

Literature Review: How U.S. Government Documents Are Addressing The Increasing National Security Implications Of Artificial Intelligence, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

This article emphasizes the increasing importance of artificial intelligence (AI) in military and national security policy making. It seeks to inform interested individuals about the proliferation of publicly accessible U.S. government and military literature on this multifaceted topic. An additional objective of this endeavor is encouraging greater public awareness of and participation in emerging public policy debate on AI's moral and national security implications..

All You Need To Know About Cybersecurity Ever! In 45 Minutes, Joe Beckman

Purdue Road School

This session will cover the information every local government official needs to know to keep their data safe from hackers.

Wireless Underground Communications In Sewer And Stormwater Overflow Monitoring: Radio Waves Through Soil And Asphalt Medium, Usman Raza, Abdul Salam

Faculty Publications

Storm drains and sanitary sewers are prone to backups and overflows due to extra amount wastewater entering the pipes. To prevent that, it is imperative to efficiently monitor the urban underground infrastructure. The combination of sensors system and wireless underground communication system can be used to realize urban underground IoT applications, e.g., storm water and wastewater overflow monitoring systems. The aim of this article is to establish a feasibility of the use of wireless underground communications techniques, and wave propagation through the subsurface soil and asphalt layers, in an underground pavement system for storm water and sewer overflow monitoring application. …

An Extensible Geospatial Data Framework (Geoedf) For Fair Science, Carol Song, Rajesh Kalyanam, Lan Zhao

Purdue GIS Day

The growing urgency in dealing with the 21st century’s grand challenges associated with increasing population, food and water security, frequently occurring natural disasters, and changing climate demands innovative, collaborative, and multidisciplinary solutions for sustainability and resilience. However, scientific data, especially geospatial data, presents significant barriers to the effective access, use and sharing of data as they come in large volumes, from different sources, and with widely varying formats, resolutions, or annotation schemas that can differ among disciplines or even research groups. This presentation describes a recently funded NSF CSSI project to develop an open source, extensible geospatial data framework (GeoEDF), …

Read And Publish: What Can Libraries Expect?, Josh Horowitz

Charleston Library Conference

The author provides a publisher's perspective on the challenges and opportunities faced by a mid-sized society in navigating the current transition to open access licensing models.

Data Curation Workshop: Tips And Tools For Today, Matthew M. Benzing

Charleston Library Conference

The current state of research data is like a disorganized photo collection: a mix of formats scattered across different media without a lot of authority control. That is changing as the need to make data available to researchers across the world is becoming recognized. Researchers know that their data needs to be maintained and made accessible, but often they do not have the time or the inclination to get involved in all of the details. This provides an excellent opportunity for librarians. Data curation is the process of preparing data to be made available in a repository with the goal …

Guest Editorial, Jiju Anthony, Chad Laux, Beth Cudney

Faculty Publications

No abstract provided.

Peppytides, Dave Zwicky

2019 Symposium on Electronic Theses and Dissertations

Lightning talk for Symposium on Electronic Theses and Dissertations (ETD) at Purdue University on May 23, 2019.

Smiler: Consistent And Usable Saliency Model Implementations, Toni Kunic, Calden Wloka, John K. Tsotsos

MODVIS Workshop

The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project …

Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos

MODVIS Workshop

No abstract provided.

The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Towards Human Retinal Cones Spatial Distribution Modeling, Matteo Paolo Lanaro, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Alessandro Rizzi

MODVIS Workshop

No abstract provided.

Recovering Depth From Stereo Without Using Any Oculomotor Information, Tadamasa Sawada

MODVIS Workshop

The human visual system uses binocular disparity to perceive depth within 3D scenes. It is commonly assumed that the visual system needs oculomotor information about the relative orientation of the two eyes to perceive depth on the basis of binocular disparity. The necessary oculomotor information can be obtained from an efference copy of the oculomotor signals, or from a 2D distribution of the vertical disparity, specifically, from the vertical component of binocular disparity. It is known that oculomotor information from the efference copy and from the vertical disparity distribution can affect the perception of depth based on binocular disparity. But, …

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground …

An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam

Faculty Publications

Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances …

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of …

A Storytelling, Social-Belonging Intervention In An Introductory Computer Science Course, Shanon Reckinger, Chris Gregg

ASEE IL-IN Section Conference

A brief social-belonging intervention was tested in two introductory computer science (CS) courses. This intervention used storytelling to help improve a sense of belonging and establish the importance of persistence in the classroom. In previous experiments using this one-time intervention, there were significant results (Walton & Brady, 2017). Recent CS graduates were interviewed about their own struggles and failures in their computer science courses. These interviews were videotaped and edited to follow the storytelling pattern of a struggle, followed by an attribution, and concluding with redemption. Interviewees were selected to represent a diverse group of students including both dominant majority …

Teaching Young Learners Computational Thinking, Tingxuan Li, Shengwei An, Xuan Wang, Hengrong Du, Guanhong Tao

Engagement & Service-Learning Summit

No abstract provided.

Collecting Virtual And Augmented Reality In The Twenty-First Century Library, Matthew Hannah, Sarah Huber, Sorin Adam Matei

Matei Interdisciplinary Research Collaboratory

In this paper, we discuss possible pedagogical applications for virtual and augmented reality (VR and AR), within a humanities/social sciences curriculum, articulating a critical need for academic libraries to collect and curate 3D objects. We contend that building infrastructure is critical to keep pace with innovative pedagogies and scholarship. We offer theoretical avenues for libraries to build a repository 3D object files to be used in VR and AR tools and sketch some anticipated challenges. To build an infrastructure to support VR/AR collections, we have collaborated with College of Liberal Arts to pilot a program in which Libraries and CLA …

International Nuclear Cyber Security Threat Landscape, Shannon Eggers, Charles Nickerson

Purdue Workshop on Nonproliferation: Technology and Approaches

The Idaho National Laboratory (INL) Nuclear Cyber Team (NCT) is a recognized leader in international nuclear cyber program assessments as well as nuclear cyber training development and delivery. This paper discusses how the NCT is implementing their vision to protect global nuclear infrastructure from cyber threats. Also outlined is the team’s perspectives on the global nuclear cyber threat landscape and the top ten approachable cyber security measures that can be applied to immediately improve a country’s nuclear cyber security posture.

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Mass Spectrometry Image Creator (Msic): Ion Mobility / Mass Spectrometry Imaging Workflow In Python, Stephen Creger, Julia Laskin, Daniela Mesa Sanchez

The Summer Undergraduate Research Fellowship (SURF) Symposium

Mass spectrometry (MS) is a powerful characterization technique that enables identification of compounds in complex mixtures. Acquiring mass spectra in a spatially-resolved manner (i.e. over a grid), allows the data to be used to generate images that show the spatial distribution and relative intensities of every compound in a sample. These images can be used to monitor and identify biomarkers, explore the metabolism of compounds within tissues, and much more. However, the limitations of mass spectrometry can result in ambiguous compound identifications. Another characterization tool, ion mobility spectrometry (IM) can be integrated into existing MS routines to address this problem; …

Tool For Correlating Ebsd And Afm Data Arrays, Andrew Krawec, Matthew Michie, John Blendell

The Summer Undergraduate Research Fellowship (SURF) Symposium

Ceramic and semiconductor research is limited in its ability to create holistic representations of data in concise, easily-accessible file formats or visual data representations. These materials are used in everyday electronics, and optimizing their electrical and physical properties is important for developing more advanced computational technologies. There is a desire to understand how changing the composition of the ceramic alters the shape and structure of the grown crystals. However, few accessible tools exist to generate a dataset with the proper organization to understand correlations between grain orientation and crystallographic orientation. This paper outlines an approach to analyzing the crystal structure …

Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced …

Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick

The Summer Undergraduate Research Fellowship (SURF) Symposium

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – …

Expected Length Of The Longest Chain In Linear Hashing, Pongthip Srivarangkul, Hemanta K. Maji

The Summer Undergraduate Research Fellowship (SURF) Symposium

Hash table with chaining is a data structure that chains objects with identical hash values together with an entry or a memory address. It works by calculating a hash value from an input then placing the input in the hash table entry. When we place two inputs in the same entry, they chain together in a linear linked list. We are interested in the expected length of the longest chain in linear hashing and methods to reduce the length because the worst-case look-up time is directly proportional to it.

The linear hash function used to calculate hash value is defined …

A Divide-And-Conquer Approach To Syntax-Guided Synthesis, Peiyuan Shen, Xiaokang Qiu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Program synthesis aims to generate programs automatically from user-provided specifications. One critical research thrust is called Syntax-Guideds Synthesis. In addition to semantic specifications, the user should also provide a syntactic template of the desired program, which helps the synthesizer reduce the search space. The traditional symbolic approaches, such as CounterExample-Guided Inductive Synthesis (CEGIS) framework, does not scale to large search spaces. The goal of this project is to explore a compositional, divide-n-conquer approach that heuristically divides the synthesis task into subtasks and solves them separately. The idea is to decompose the function to be synthesized by creating a set of …

Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …

Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …