Computer Sciences Commons^™

Sequence-Based Bioinformatics Approaches To Predict Virus–Host Relationships In Archaea And Eukaryotes, Yingshan Li

School of Computing: Dissertations, Theses, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan

School of Computing: Dissertations, Theses, and Student Research

Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

School of Computing: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Room-Temperature Polariton Quantum Fluids In Halide Perovskites, Kai Peng, Renjie Tao, Louis Haeberlé, Quanwei Li, Dafei Jin, Graham R. Fleming, Stéphane Kéna-Cohen, Xiang Zhang, Wei Bao

School of Computing: Faculty Publications

Quantum fluids exhibit quantum mechanical effects at the macroscopic level, which contrast strongly with classical fluids. Gain-dissipative solid-state exciton-polaritons systems are promising emulation platforms for complex quantum fluid studies at elevated temperatures. Recently, halide perovskite polariton systems have emerged as materials with distinctive advantages over other room-temperature systems for future studies of topological physics, non-Abelian gauge fields, and spin-orbit interactions. However, the demonstration of nonlinear quantum hydrodynamics, such as superfluidity and Čerenkov flow, which is a consequence of the renormalized elementary excitation spectrum, remains elusive in halide perovskites. Here, using homogenous halide perovskites single crystals, we report, in both one- …

Computer Engineering Education, Marilyn Wolf

School of Computing: Conference and Workshop Papers

Computer engineering is a rapidly evolving discipline. How should we teach it to our students?

This virtual roundtable on computer engineering education was conducted in summer 2022 over a combination of email and virtual meetings. The panel considered what topics are of importance to the computer engineering curriculum, what distinguishes computer engineering from related disciplines, and how computer engineering concepts should be taught.

Incoherent And Online Dictionary Learning Algorithm For Motion Prediction, Farrukh Hafeez, Usman Ullah Sheikh, Asif Iqbal, Muhammad Naveed Aman

School of Computing: Faculty Publications

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the offline augmented semi-non-negative sparse coding (ASNSC) algorithm. We do this by adding a penalty …

Icebar: Feedback-Driven Iterative Repair Of Alloy Specifications, Simón Gutiérrez Brida, Germán Regis, Guolong Zheng, Hamid Bagheri, Thanhvu Nguyen, Nazareno Aguirre, Marcelo Frias

School of Computing: Faculty Publications

Automated program repair (APR) techniques have shown great success in automatically finding fixes for programs in programming languages such as C or Java. In this work, we focus on repairing formal specifications, in particular for the Alloy specification language. As opposed to most APR tools, our approach to repair Alloy specifications, named ICEBAR, does not use test-based oracles for patch assessment. Instead, ICEBAR relies on the use of property-based oracles, commonly found in Alloy specifications as predicates and assertions. These property-based oracles define stronger conditions for patch assessment, thus reducing the notorious overfitting issue caused by using test-based oracles, …

The Road Not Taken: Exploring Alias Analysis Based Optimizations Missed By The Compiler, Khushboo Chitre, Piyus Kedia, Rahul Purandare

School of Computing: Faculty Publications

Context-sensitive inter-procedural alias analyses are more precise than intra-procedural alias analyses. However, context-sensitive inter-procedural alias analyses are not scalable. As a consequence, most of the production compilers sacrifice precision for scalability and implement intra-procedural alias analysis. The alias analysis is used by many compiler optimizations, including loop transformations. Due to the imprecision of alias analysis, the program’s performance may suffer, especially in the presence of loops.

Previous work proposed a general approach based on code-versioning with dynamic checks to disambiguate pointers at runtime. However, the overhead of dynamic checks in this approach is 𝑂(𝑙𝑜𝑔 𝑛), which is substantially high to …

Deja Vu: Semantics-Aware Recording And Replay Of High-Speed Eye Tracking And Interaction Data To Support Cognitive Studies Of Software Engineering Tasks—Methodology And Analyses, Vlas Zyrianov, Cole S. Peterson, Drew T. Guarnera, Joshua Behler, Praxis Weston, Bonita Sharif Ph.D., Jonathan I. Maletic

School of Computing: Faculty Publications

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not always possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over …

Neural Network Repair With Reachability Analysis, Xiaodong Yang, Tom Yamaguchi, Tran Hoang-Dung, Bardh Hoxha, Taylor T. Johnson, Danil Prokhorov

School of Computing: Faculty Publications

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. Formally verifying the safety and robustness of well-trained DNNs and learning-enabled cyber-physical systems (Le-CPS) under adversarial attacks, model uncertainties, and sensing errors is essential for safe autonomy. This research proposes a framework to repair unsafe DNNs in safety-critical systems with reachability analysis. The repair process is inspired by adversarial training which has demonstrated high effectiveness in improving the safety and robustness of DNNs. Different from traditional adversarial training approaches where adversarial examples are utilized from …

Pitfalls And Guidelines For Using Time-Based Git Data, Samuel W. Flint, Jigyasa Chauhan, Robert Dyer

School of Computing: Faculty Publications

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could aect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of …

Decipherment Challenges Due To Tamga And Letter Mix-Ups In An Old Hungarian Runic Inscription From The Altai Mountains, Peter Revesz

School of Computing: Faculty Publications

An Old Hungarian Runic inscription from the Altai Mountains with 40 signs has posed some special challenges for decipherment due to several letter mix-ups and the use of a tamga sign, which is the first reported use of a tamga within this type of script. This paper gives a complete and correct translation and draws some lessons that can be learned about decipherment. It introduces sign similarity matrices as a method of detecting accidental misspellings and shows that sign similarity matrices can be efficiently computed. It also explains the importance of simultaneously achieving the three criteria for a valid decipherment: …

Parasol: Efficient Parallel Synthesis Of Large Model Spaces, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive …

What Makes The Article “Condition Monitoring And Fault Diagnosis Of Electrical Motors—A Review” So Popular?, Wei Qiao

School of Computing: Faculty Publications

Electric motors are widely used in the industrial, commercial, residential, and transportation sectors to power the systems that provide goods and services to end users. The failure of electric motors may cause significant production or service interruption and financial losses. To improve the quality of service of systems driven by electric motors, it is vital to continuously improve the reliability of electric motors. Driven by this demand, various condition monitoring and fault diagnostic techniques for electric motors have been developed by academia and industry over the past decades.

The article “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” written …

Nanomechanical Resonators: Toward Atomic Scale, Bo Xu, Pengcheng Zhang, Jiankai Zhu, Zuheng Liu, Alexander Eichler, Xu-Qian Zheng, Jaesung Lee, Aneesh Dash, Swapnil More, Song Wu, Yanan Wang, Hao Jia, Akshay Naik, Adrian Bachtold, Rui Yang, Philip X.-L. Feng, Zenghui Wang

School of Computing: Faculty Publications

The quest for realizing and manipulating ever smaller man-made movable structures and dynamical machines has spurred tremendous endeavors, led to important discoveries, and inspired researchers to venture to previously unexplored grounds. Scientific feats and technological milestones of miniaturization of mechanical structures have been widely accomplished by advances in machining and sculpturing ever shrinking features out of bulk materials such as silicon. With the flourishing multidisciplinary field of low-dimensional nanomaterials, including one-dimensional (1D) nanowires/nanotubes and two-dimensional (2D) atomic layers such as graphene/ phosphorene, growing interests and sustained effort have been devoted to creating mechanical devices toward the ultimate limit of miniaturization--genuinely …

Enabling Intelligent Iots For Histopathology Image Analysis Using Convolutional Neural Networks, Mohammed H. Alali, Arman Roohi, Shaahin Angizi, Jitender S. Deogun

School of Computing: Faculty Publications

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization …

Feed Forward Neural Networks With Asymmetric Training, Archit Srivastava

School of Computing: Dissertations, Theses, and Student Research

Our work presents a new perspective on training feed-forward neural networks(FFNN). We introduce and formally define the notion of symmetry and asymmetry in the context of training of FFNN. We provide a mathematical definition to generalize the idea of sparsification and demonstrate how sparsification can induce asymmetric training in FFNN.

In FFNN, training consists of two phases, forward pass and backward pass. We define symmetric training in FFNN as follows-- If a neural network uses the same parameters for both forward pass and backward pass, then the training is said to be symmetric.

The definition of asymmetric training in artificial …

Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices, Sarat Sasank Barla, Sai Surya Sanjay Alamuru, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper investigates the similarity between the Indus Valley script and the Kannada, Malayalam, Tamil, and Telugu scripts that are used to write Dravidian languages. The closeness of these scripts is determined by applying a feature analysis of each sign of these scripts and creating similarity matrices that describe the similarity of any pair of signs from two different scripts. The feature list that we use for the analysis of these Dravidian language-related scripts includes six new features beyond the thirteen features that were used for the study of Minoan Linear A and related scripts by Revesz. These new features …

Deep Reinforcement Learning For End-To-End Network Slicing: Challenges And Solutions, Qiang Liu, Nakjung Choi, Tao Han

School of Computing: Faculty Publications

5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these use cases cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according to specific resource demands. A network slice may have hundreds of configurable parameters over multiple technical domains that define the performance of the network slice, which makes it impossible to use traditional model-based solutions to orchestrate resources for network slices. In this article, we discuss how to design and deploy deep reinforcement learning (DRL), a model-free approach, to address the network slicing problem. …

Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina

School of Computing: Dissertations, Theses, and Student Research

Molecular Communication is an emerging paradigm with the potential to revolutionize the technology behind wearable and implantable devices and the broad range of functions they support, from tracking physical activity to medical diagnostics. This can be achieved through intra-body communication networks that take advantage of natural biological processes as a means of transmitting, propagating and receiving information. In this thesis we focus particularly on using the neuron as a means to facilitate information transfer for interconnected wearable or implantable devices through a technique known as sub-threshold electrical stimulation. We develop upon a prior work by introducing a linear model of …

Profiling A Community-Specific Function Landscape For Bacterial Peptides Through Protein-Level Meta-Assembly And Machine Learning, Mitra Vajjala, Brady Johnson, Lauren Kasparek, Michael Leuze, Qiuming Yao

School of Computing: Faculty Publications

Small proteins, encoded by small open reading frames, are only beginning to emerge with the current advancement of omics technology and bioinformatics. There is increasing evidence that small proteins play roles in diverse critical biological functions, such as adjusting cellular metabolism, regulating other protein activities, controlling cell cycles, and affecting disease physiology. In prokaryotes such as bacteria, the small proteins are largely unexplored for their sequence space and functional groups. For most bacterial species from a natural community, the sample cannot be easily isolated or cultured, and the bacterial peptides must be better characterized in a metagenomic manner. The bacterial …

Internal Model Control (Imc)-Based Active And Reactive Power Control Of Brushless Double-Fed Induction Generator With Notch Filter, Ahsanullah Memon, Mohd Wazir Bin Mustafa, Zohaib Hussain Laghari, Touqeer Ahmed Jumani, Waqas Anjum, Shafi Ullah, Muhammad Naveed Aman

School of Computing: Faculty Publications

The increase in demand for electricity and, in particular, green energy has put renewable energy systems at the focal point of energy policy worldwide. The higher reliability of brushless doubly fed induction generators (BDFIGs) makes them suitable for offshore and remote wind energy generation (WEG) applications. Besides, controlling the active and reactive powers in an electrical power system is critical for optimal voltage regulation, reduced power losses, and enhanced utilization of installed equipment. However, the existing literature on BDFIG’s active and reactive power control highlights the poor dynamic response and high transients with harmonic generation during inductive load insertion. It …

Combining Solution Reuse And Bound Tightening For Efficient Analysis Of Evolving Systems, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Software engineers have long employed formal verification to ensure the safety and validity of their system designs. As the system changes—often via predictable, domain-specific operations—their models must also change, requiring system designers to repeatedly execute the same formal verification on similar system models. State-of-the-art formal verification techniques can be expensive at scale, the cost of which is multiplied by repeated analysis. This paper presents a novel analysis technique—implemented in a tool called SoRBoT—which can automatically determine domain-specific optimizations that can dramatically reduce the cost of repeatedly analyzing evolving systems. Different from all prior approaches, which focus on either tightening the …

Consemblex: A Consensus-Based Transcriptome Assembly Approach That Extends Consemble And Improves Transcriptome Assembly, Richard Mwaba

School of Computing: Dissertations, Theses, and Student Research

An accurate transcriptome is essential to understanding biological systems enabling omics analyses such as gene expression, gene discovery, and gene-regulatory network construction. However, assembling an accurate transcriptome is challenging, especially for organisms without adequate reference genomes or transcriptomes. While several methods for transcriptome assembly with different approaches exist, it is still difficult to establish the most accurate methods. This thesis explores the different transcriptome assembly methods and compares their performances using simulated benchmark transcriptomes with varying complexity. We also introduce ConSemblEX to improve a consensus-based ensemble transcriptome assembler, ConSemble, in three main areas: we provide the ability to use any …

On Approximating Total Variation Distance, Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

School of Computing: Faculty Publications

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the computational problem of determining the TV distance between two product distributions over the domain {0, 1}ⁿ. We establish the following results.

1. Exact computation of TV distance between two product distributions is #P-complete. This is in stark contrast with other distance measures such as KL, Chi-square, and Hellinger which tensorize over the marginals.

2. Given two product distributions P and Q with marginals of P being at least 1/2 and marginals of Q being at most …

Single-Pass Inline Pipeline 3d Reconstruction Using Depth Camera Array, Zhexiong Shang, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. …

Real Time Call-Flagging System To Respond To Suicidal Ideation In Call Centers, Vishnu Menon, Joseph Carrigan, Charles Floeder, Thomas Walton, Devin Mcguire

Honors Program: Senior Projects (Public)

The 2021-2022 Signature Performance Design Studio team developed a live audio call-flagging system that enables faster responses and new response pathways to veteran crises by call service representatives and their management team. Using a custom made deep learning model, live audio streaming server, and Teams broadcasting add-on, the system empowers Signature Performance call service representatives to make quicker and more well informed decisions to provide veteran’s the best care possible.

Analysis Of The Effectiveness Of Different Techniques For Creating Cross-Platform Compatible Software, Michael Westberg

Honors Program: Senior Projects (Public)

Creating cross-platform compatible software is a major issue in a world where users utilize a variety of devices and platforms. To ensure that a piece of software is accessible to as many users as possible, software must be cross-platform compatible. There are four main approaches that can be done to achieve this state of being cross-platform compatible, each with both advantages and disadvantages. These methods are: creating the software as separate binaries, using a scripting language with a cross-platform interpreter, compiling to an intermediate language, and creating the software as a web application. This paper will discuss how each of …

A Framework Comparison: .Net And Laravel, Guy Richard

Honors Program: Senior Projects (Public)

No abstract provided.

Inter-Cell Slicing Resource Partitioning Via Coordinated Multi-Agent Deep Reinforcement Learning, Tianlun Hu, Qi Liao, Qiang Liu, Dan Wellington, Georg Carle

School of Computing: Faculty Publications

Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The …