Computer Sciences Commons^™

Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan

Doctoral Dissertations

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or …

Persistent Stealthy Attacks And Their Detection In Large Distributed Cyber-Physical Systems, Simon Bech Thougaard

Doctoral Dissertations

"Cyber-Physical Systems (CPS) are increasingly targeted by attackers using a wide and evolving array of methods. When these systems are distributed, every node represents a potential vulnerability, and secure system design must take this into account. Distributed CPSs also have the potential to better detect and handle attacks, by leveraging redundancies of physical measurements between adjacent nodes. The main purpose of this research is to determine the conditions that render a distributed CPS more resistant to attacks, and the conditions that render it more vulnerable. The work is centered around two separate applications: The Smart Grid and Autonomous Drone Swarms. …

Secure And Efficient Information Management In Delay(Disruption) Tolerant Network, Shudip Datta

Doctoral Dissertations

"In environments like international military coalitions on the battlefield or multi-party relief work in a disaster zone, multiple teams are deployed to serve different mission goals by the command-and-control center (CC). They may need to survey damages and send information to the CC for situational awareness and also transfer messages to each other for mission purposes. However, due to the damaged network infrastructure in the emergency, nodes need to relay messages using the store and forward paradigm, also called Delay-tolerant Networks (DTNs). In DTN, the limited bandwidth, energy, and contacts among the nodes, and their interdependency impose several challenges such …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …

Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi

Computer Science Faculty Research & Creative Works

Oxidative stress may contribute to the pathology of many diseases, and endogenous thiols, especially glutathione (GSH) and its metabolites, play essential roles in the maintenance of normal redox status. Understanding how these metabolites change in response to oxidative insult can provide key insights into potential methods of prevention and treatment. Most existing methodologies focus only on the GSH/GSH disulfide (GSSG) redox couple, but GSH regulation is highly complex and depends on several pathways with multiple redox-active sulfur-containing species. In order to more fully characterize thiol redox status in response to oxidative insult, a high-performance liquid chromatography with tandem mass spectrometry …

An Energy-Efficient Smart Space System Using Lora Network With Deadline And Security Constraints, Preti Kumari, Hari Prabhat Gupta, Rahul Mishra, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding …

Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Todays exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independentblock-wise model that decomposes the …

Reducing Kidney Discard With Artificial Intelligence Decision Support: The Need For A Transdisciplinary Systems Approach, Richard Threlkeld, Lirim Ashiku, Casey I. Canfield, Daniel Burton Shank, Mark A. Schnitzler, Krista L. Lentine, David A. Axelrod, Anil Choudary Reddy Battineni, Henry Randall, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard.

Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and …

Online Optimization Of File Transfers In High-Speed Networks, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

File transfers in high-speed networks require network and I/O parallelism to reach high speeds, however, creating arbitrarily large numbers of I/O and network threads overwhelms system resources and causes fairness issues. In this paper, we introduce Falcon that combines a novel utility function with state-of-the-art online optimization algorithms to discover the degree of I/O and network parallelism for file transfer that can maximize the throughput while keeping system overhead low and ensuring fairness among competing transfers. Our extensive evaluations in several dedicated and production high-speed networks show that Falcon can find near optimal solution in as little as 20 seconds …

Learning Transfers Via Transfer Learning, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Detecting performance anomalies is key to efficiently utilize network resources and improve the quality of service. Researchers proposed various approaches to identify the presence of anomalies by analyzing performance statistics using heuristic (e.g., change point detection) and Machine Learning (ML) models. Although these models yield high accuracy in the networks that they are trained for, their performance degrade severely when transferred to different network settings. This is because of the fact that existing models detect anomalies by capturing the changes in transfer throughput and observed RTT values, which are dependent to network settings. In this paper, we propose a novel …

Warmonger: Inflicting Denial-Of-Service Via Serverless Functions In The Cloud, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the Warmonger attack, we ran months-long experiments, collected and analyzed the …

Towards Generalizable Network Anomaly Detection Models, Md Arifuzzaman, Shafkat Islam, Engin Arslan

Computer Science Faculty Research & Creative Works

Finding the root causes of network performance anomalies is critical to satisfy the quality-of-service requirements. In this paper, we introduce machine learning (ML) models to process TCP socket statistics to pinpoint underlying reasons of performance issues such as packet loss and jitter. More importantly, we introduce a novel feature engineering method to transform network-dependent metrics (e.g., total packet count and round-trip time) in training datasets into network independent forms to be able to transfer the models to new network settings without requiring retraining them. Experimental results in various network settings show that the proposed feature engineering approach improves the performance …

Entity Retrieval Using Fine-Grained Entity Aspects, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use in Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the …

Effect Of Team Cohesion Nn Flow: An Empirical Study Of Team-Based Gamification For Enterprise Resource Planning Systems In Online Classes, Yu Zhao, Mark Srite, Sumin Kim, Jinwoong Lee

Business and Information Technology Faculty Research & Creative Works

Pedagogy using gamification has recently received much attention as a way of enhancing student learning and retention. Additionally, academic institutions are making extensive use of online resources to expand teaching beyond the traditional classroom setting. Both academic institutions and companies utilize virtual teams to accomplish remote teamwork, particularly in a post-COVID environment. Despite the growing interest in incorporating gamification into teaching for business school courses, prior researchers have paid little attention to team-based gamification in the online learning environment. The purpose of this study is to examine if team members' perceived team cohesion influences their perceptions of flow. Also, we …

Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data: Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Dajiang Zhu

Computer Science Faculty Research & Creative Works

As reversing the pathology of Alzheimer's disease (AD) is impossible, the diagnosis of mild cognitive impairment (MCI), which is considered as the precursor of AD, has become a more tractable goal. Because both brain structural and functional alterations have been observed in MCI patients, many multimodal fusion approaches have been proposed to classify MCI from normal controls (NC) in clinical studies. Given the complex relationships between brain structure and function, deep learning-based models can be helpful in revealing potential non-linear relationships buried in multimodal neuroimaging data. Meanwhile, RNA expression microarray profile can be a complementary feature in brain diseases analysis …

Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli

Computer Science Faculty Research & Creative Works

The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework 'Apache Spark' capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models …

Accelerating Multigrid-Based Hierarchical Scientific Data Refactoring On Gpus, Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and …

Revisiting Huffman Coding: Toward Extreme Performance On Modern Gpu Architectures, Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello

Computer Science Faculty Research & Creative Works

Today’s high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today’s HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, …

Automatically Selecting Follow-Up Questions For Deficient Bug Reports, Mia Mohammad Imran, Agnieszka Ciborowska, Kostadin Damevski

Computer Science Faculty Research & Creative Works

The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open-source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted …

A Man Out West Is A Man, Nikola Andric

Undergraduate Research Conference at Missouri S&T

The goal of this research is to analyze who the cowboys were and show how the standard set for the cowboy in novels remained consistent during the period between 1902 and 1953. The presentation allows readers to think of their idea of a cowboy and then compare it with the idea of the cowboy drawn from the books used in the research. It is a great opportunity to find out who the perfect cowboy is, what cowboys' relationships with their horses look like, what is considered good, and what is considered evil during those fifty years. Above all, the most …

Small-Scale Wind Power Prediction, Nathan Skelton

Undergraduate Research Conference at Missouri S&T

This study is focused on the development of viable power generation modeling for small scale wind power installations. Armed with a viable model, businesses and individuals would have another option to reliably power small-scale buildings or installations. This study defines small-scale wind power turbines as those installed on buildings, or otherwise operating under 30 meters (~100 feet). Both of these installation environments provide inconsistent wind speeds, among other properties, that most wind power models do not address. Through the development of a sample wind turbine and accompanying weather station, multiple methods of prediction have been implemented with varying degrees of …

An Iterative Hybrid Algorithm For Roots Of Non-Linear Equations, Chaman Lal Sabharwal

Computer Science Faculty Research & Creative Works

Finding the roots of non-linear and transcendental equations is an important problem in engineering sciences. In general, such problems do not have an analytic solution; the researchers resort to numerical techniques for exploring. We design and implement a three-way hybrid algorithm that is a blend of the Newton–Raphson algorithm and a two-way blended algorithm (blend of two methods, Bisection and False Position). The hybrid algorithm is a new single pass iterative approach. The method takes advantage of the best in three algorithms in each iteration to estimate an approximate value closer to the root. We show that the new algorithm …

Ft-Cnn: Algorithm-Based Fault Tolerance For Convolutional Neural Networks, Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus …

Lanchester's Equations And Cyberwarfare, George Markowsky, Linda Markowsky

Computer Science Faculty Research & Creative Works

In his classic book Aircraft in Warfare, F. W. Lanchester discussed different types of warfare and presented equations, called the Lanchester equations, that can be used to model the results of battles between two forces of different sizes or capabilities. This paper introduces the Lanchester equations and provides a theoretical discussion leading to an analysis of the relative value of increasing the effectiveness of military assets vs. increasing the quantity of those assets. In particular, we show that increasing the effectiveness contributes only linearly to the power of a combatant, but increasing the quantity contributes quadratically. This paper also presents …

Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang

Computer Science Faculty Research & Creative Works

Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes …

Efficient Route Selection For Drone-Based Delivery Under Time-Varying Dynamics, Arindam Khanda, Federico Coro, Francesco Betti Sorbelli, Cristina M. Pinotti, Sajal K. Das

Computer Science Faculty Research & Creative Works

The use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-to-reach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones' energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In …

Exploring Autoencoder-Based Error-Bounded Compression For Scientific Data, Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide …

Detection Dns Tunneling Botnets, Bohdan Savenko, Sergii Lysenko, Kira Bobrovnikova, Oleg Savenko, George Markowsky

Computer Science Faculty Research & Creative Works

Botnets are often used in cyberattacks on network services and individual users, so the ability to detect botnets is very important. Botnets use DNS tunneling to send malicious command-and-control (CC) commands to victims' hosts. Unfortunately, DNS tunneling attacks are very hard to detect. The paper presents a new approach for DNS tunneling botnet detection, which considers all the features and architectural characteristics of botnets. The technique described in this paper is highly efficient at detecting DNS tunneling attacks.

Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention For Facial Action Analysis, Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin

Computer Science Faculty Research & Creative Works

Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e, g. facial action units), learn discriminative features, and learn inter-class correlation. However, few related works pay attention to the robustness of the attention module itself. Through experiments, we found neural attention maps initialized with different feature maps yield diverse representations when learning to attend the identical Region of Interest (ROI). In other words, similar to general feature learning, the representational …

Improving Lossy Compression For Sz By Exploring The Best-Fit Lossless Compression Techniques, Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

In the past decades, various lossy compressors have been studied broadly due to the ever-increasing volume of data being produced by today's scientific applications. SZ has been one of the best error-bounded lossy compressors ever raised, and it has a flexible framework that includes four adjustable steps: prediction, quantization, variable-length encoding, and lossless compression. In this paper, we improve the lossy compression performances of the SZ compression model by exploring different existing lossless compression techniques using the Squash data compression benchmark. Specifically, we first characterize the bytes outputted by the first three steps in SZ, then we investigate the best …