Engineering Commons^™

The Interval Grey Numbers Ranking Based On Risk Preferences, Zhaobin Li, Zhuo Zhang, Jian Liu, Shuai Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a new method for ranking interval grey numbers to address the challenge in multi-criteria decision-making problems with interval grey numbers has been proposed. This new method involves the risk preferences of decision makers. First, we propose a new method to rank the interval grey numbers by comparing the possibility degree or whitened value. Second, we classify the decision makers into three different types according to their risk preferences then we establish the corresponding risk preference assumptions to solve the problem that different interval grey numbers with the same possibility degree or whitened value. Finally, we use a …

Resource Efficient Design Of Quantum Circuits For Quantum Algorithms, Himanshu Thapliyal

Commonwealth Computational Summit

No abstract provided.

Additional Data Via Autonomous Systems To Supplement Traditional Sparse Sources For Weather Forecasting And Atmospheric Science, Suzanne Weaver Smith

Commonwealth Computational Summit

No abstract provided.

Integrative Computational Modeling For Developing Means To Manipulate Biological Cells And For Solving Complex Engineering Problems, Yu Zhao, Guigen Zhang

Commonwealth Computational Summit

A brief discussion on reductive vs integrative investigation

A case study: how integrative computational modeling helps advance the understanding and application of dielectrophoresis (DEP) in various situations

Other applications in advancing the design and development of nanopore, medical devices, novel materials, actuation devices, and coupled spectroscopic techniques, etc.

The Effect Of Inlet Pulsations On Primary Atomization Of Liquid Jets, Kyle Windland, Prashant Khare

Commonwealth Computational Summit

Objectives

• Elucidate the physics underlying the primary atomization of liquid jets.
• Investigate the effect of inlet pulsations on the atomization process.
• Identify the reliability of numerical predictions using uncertainty quantification techniques (UQ) and sensitivity analyses.

Investigation Of Photo-Responsive Titania Surfaces, Evan Hyden, Matthew J. Beck

Commonwealth Computational Summit

Titania (TiO₂) is an oxide commonly used for photocatalysis. This proclivity to form free radicals when exposed to light has also prompted interest in applying porous TiO₂ as a reactive/responsive membrane. Surfaces of both main polymorphs of TiO₂ (especially rutile {110} and anatase {101}) have been shown to be stable and photo-active.

Polyacrylic acid (PAA) is a molecule of interest as changes in pH can cause the molecule to contract or expand, due to changes in the charge distribution relating to the carboxyl group. Due to TiO₂’s ability to generate photocarriers, we hypothesize that …

Using Explainability For Constrained Matrix Factorization, Behnoush Abdollahi, Olfa Nasraoui

Commonwealth Computational Summit

Explainable Model

• Black Box (opaque) predictors such as Deep learning and Matrix Factorization are accurate,
• ... but lack interpretability and ability to give explanations.
• White Box models such as rules and decision trees are interpretable (explainable),
• ... but lack accuracy.

Computational Materials Characterization, Discovery, And Design With High Performance Computing, Qunfei Zhou, Xiaotao Liu, Tyler Maxwell, Thomas John Balk, Matthew J. Beck

Commonwealth Computational Summit

No abstract provided.

Integrative Computational Modeling For Developing Means To Manipulate Biological Cells And For Solving Complex Engineering Problems, Yu Zhao, Guigen Zhang

Commonwealth Computational Summit

Computational modeling has become more widely used to guide the design of microfluidic devices for manipulating cells using Dielectrophoresis (DEP), and devise novel means for advancing the study of cellular science and engineering. Conventionally, cells are treated as volumeless points in the system, which allows study of the movement of groups of particles under the effect of field. However, this approach often neglects the distortion effect of particle on external field, as well as interactions among particles. Moreover, it ignores the complex inner structures of cell, which are the causes of distinctive cell behavior. To more accurately model the behavior …

High-Fidelity Simulations Of Water Jet In Air Crossflow, Austin Johnston, Prashant Khare

Commonwealth Computational Summit

Objectives

• Investigate detailed physics underlying liquid jets in crossflow configurations applicable to various applications such as, gas-turbine, scramjet, and afterburner fuel injection.
• Develop models to predict the statistical behaviors of resulting droplets.

Parallelization Of A Three-Dimensional Full Multigrid Algorithm To Simulate Tumor Growth, Dylan Goodin, Chin F. Ng, Hermann B. Frieboes

Commonwealth Computational Summit

We present the performance gains of an openMP implementation of a fully adaptive nonlinear full multigrid (FMG) algorithm to simulate three-dimensional multispecies desmoplastic tumor growth on computer systems of varying processing capabilities. The FMG algorithm is applied to solve a recently published thermodynamic mixture model that uses a diffuse interface approach with fourth-order reaction-advection-diffusion PDEs (Cahn-Hilliard-type equations) that are coupled, nonlinear, and numerically stiff. The model includes multiple cell species and extracellular matrix (ECM), with adhesive and elastic energy contributions in chemical potential terms, as well as including blood and lymphatic vessels represented as continuous vasculatures. Advection-reaction-diffusion PDEs are employed …

Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu

LSU Doctoral Dissertations

Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …

Open Source Artificial Intelligence In A Biological/Ecological Context, Trevor Grant

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Deep Learning In R, Troy Hernandez

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Formal Performance Guarantees For An Approach To Human In The Loop Robot Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— A key challenge in the automatic verification of robot mission software, especially critical mission software, is to be able to effectively model the performance of a human operator and factor that into the formal performance guarantees for the mission. We present a novel approach to modelling the skill level of the operator and integrating it into automatic verification using a linear Gaussians model parameterized by experimental calibration. Our approach allows us to model different skill levels directly in terms of the behavior of the lumped, robot plus operator, system.

Using MissionLab and VIPARS (a behavior-based robot mission verification …

What Does Explainable Ai Really Mean? A New Conceptualization Of Perspectives, Derek Doran, Sarah Schulz, Tarek R. Besold

Computer Science and Engineering Faculty Publications

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human …

A Framework For Recommendation Of Highly Popular News Lacking Social Feedback, Nuno Moniz, Luís Torgo, Magdalini Eirinaki, Paula Branco

Faculty Publications

Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and …

Teaching Image Processing And Visualization Principles To Medicine Students, Christina Gillmann, Thomas Wischgoll, Jose T. Hernandez, Hans Hagen

Computer Science and Engineering Faculty Publications

Although image processing becomes increasingly important in most applications such as medicine, image processing and visualization is usually not a part of the medical education and therefore not widely spread in clinical daily routine. Contrary to students from computer science, medical students are usually not familiar to computational models or algorithms and require a different view of the algorithms instead of knowing each computational detail. To solve this problem this paper presents the concept of a lecture that aims to impart image processing and visualization principals for students in medicine in order to pioneer a higher acceptance and propagation of …

Power-Efficient And Highly Scalable Parallel Graph Sampling Using Fpgas, Usman Tariq, Umer Cheema, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Energy efficiency is a crucial problem in data centers where big data is generally represented by directed or undirected graphs. Analysis of this big data graph is challenging due to volume and velocity of the data as well as irregular memory access patterns. Graph sampling is one of the most effective ways to reduce the size of graph while maintaining crucial characteristics. In this paper we present design and implementation of an FPGA based graph sampling method which is both time- and energy-efficient. This is in contrast to existing parallel approaches which include memory-distributed clusters, multicore and GPUs. Our …

Simple Implementation Of An Elgamal Digital Signature And A Brute Force Attack On It, Valeriia Laryoshyna

Student Works

This study is an attempt to show a basic mathematical usage of the concepts behind digital signatures and to provide a simple approach and understanding to cracking basic digital signatures. The approach takes on simple C programming of the ElGamal digital signature to identify some limits that can be encountered and provide considerations for making more complex code. Additionally, there is a literature review of the ElGamal digital signature and the brute force attack.

The research component of this project provides a list of possible ways to crack the basic implementations and classifies the different approaches that could be taken …

Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Zheng Yang, Yunhao Liu, Chunyi Peng

Research Collection School Of Computing and Information Systems

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in …

Methodology To Perform Cyber Lethality Assessment, Matthew W. Zurasky

Engineering Management & Systems Engineering Theses & Dissertations

The Naval Surface Warfare Center, Dahlgren Division (NSWCDD) Lethality and Effectiveness Branch is the Navy’s subject matter experts (SME) on target vulnerability, weapon lethality, and weapon effectiveness. Branch personnel currently exercise expertise in the kinetic and directed energy weapon domains. When the Navy develops weapons in the kinetic and directed energy domains, there are clear and well established procedures and methodologies for performing target characterization that support weapon-target pairing. Algorithms exist to describe the likelihood of damage effects. It is natural that in the paradigm shift to cyberspace warfare that the Branch provide these same services to the warfighter in …

Modeling Energy Consumption Of High-Performance Applications On Heterogeneous Computing Platforms, Gary D. Lawson Jr.

Computational Modeling & Simulation Engineering Theses & Dissertations

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.

Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship …

Streaming Vr For Immersion: Quality Aspects Of Compressed Spatial Audio, Miroslaw Narbutt, Sean O’Leary, Andrew Allen, Jan Skoglund, Andrew Hines

Conference papers

Delivering a 360-degree soundscape that matches full sphere visuals is an essential aspect of immersive VR. Ambisonics is a full sphere surround sound technique that takes into account the azimuth and elevation of sound sources, portraying source location above and below as well as around the horizontal plane of the listener. In contrast to channel-based methods, ambisonics representation offers the advantage of being independent of a specific loudspeaker set-up. Streaming ambisonics over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience (QoE). This work investigates the effect of audio channel compression via the …

Semantic Reasoning In Zero Example Video Event Retrieval, M. H. T. De Boer, Yi-Jie Lu, Hao Zhang, Klamer Schutte, Chong-Wah Ngo, Wessel Kraaij

Research Collection School Of Computing and Information Systems

Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples, but without examples it is still hard to (1) determine which concepts are useful to pre-train (Vocabulary challenge) and (2) which pre-trained concept detectors are relevant for a certain unseen high-level event (Concept Selection challenge). In our article, we present our Semantic Event Retrieval Systemwhich (1) shows the importance of high-level concepts …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

Combinatorial Auction For Transportation Matching Service: Formulation And Adaptive Large Neighborhood Search Heuristic, Baoxiang Li, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper considers the problem of matching multiple shippers and multi-transporters for pickups and drop-offs, where the goal is to select a subset of group jobs (shipper bids) that maximizes profit. This is the underlying winner determination problem in an online auction-based vehicle sharing platform that matches transportation demand and supply, particularly in a B2B last-mile setting. Each shipper bid contains multiple jobs, and each job has a weight, volume, pickup location, delivery location and time window. On the other hand, each transporter bid specifies the vehicle capacity, available time periods, and a cost structure. This double-sided auction will be …

Cross-Modal Recipe Retrieval With Rich Food Attributes, Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Food is rich of visible (e.g., colour, shape) and procedural (e.g., cutting, cooking) attributes. Proper leveraging of these attributes, particularly the interplay among ingredients, cutting and cooking methods, for health-related applications has not been previously explored. This paper investigates cross-modal retrieval of recipes, specifically to retrieve a text-based recipe given a food picture as query. As similar ingredient composition can end up with wildly different dishes depending on the cooking and cutting procedures, the difficulty of retrieval originates from fine-grained recognition of rich attributes from pictures. With a multi-task deep learning model, this paper provides insights on the feasibility of …

Improvement Of Phylogenetic Method To Analyze Compositional Heterogeneity, Zehua Zhang, Kecheng Guo, Gaofeng Pan, Jijun Tang, Fei Guo

Faculty Publications

Background: Phylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species.

Results: Here, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local …

Developing Grounded Goals Through Instant Replay Learning, Lisa Meeden, Douglas S. Blank

Computer Science Faculty Research and Scholarship

This paper describes and tests a developmental architecture that enables a robot to explore its world, to find and remember interesting states, to associate these states with grounded goal representations, and to generate action sequences so that it can re-visit these states of interest. The model is composed of feed-forward neural networks that learn to make predictions at two levels through a dual mechanism of motor babbling for discovering the interesting goal states and instant replay learning for developing the grounded goal representations. We compare the performance of the model with grounded goal representations versus random goal representations, and find …