Computer Sciences Commons^™

Seen And Heard

In The Loop

IRL Programs Debut; Short & Sweet Pandemic Film Fest; New MS in Artificial Intelligence; Virtual Experts Talks; DePaul Trustee Producing Documentary; DemonHacks Hackathon

Alumna Profile: Code Warrior

In The Loop

Competing in triathlons helped Ovetta Sampson (CDM MS ’16) stride past personal setbacks. The DePaul graduate’s career path evokes that athletic competition as well. She has moved from journalist to principal creative director at Microsoft, where she leads a team she says tackles “big, human-centered problems for big companies” in artificial intelligence, automation, digital transformation and manufacturing.

Research Focus: Pattern Recognition

In The Loop

A CDM health informatics team joins a global race to advance COVID-19 diagnostics through X-ray insights.

Introduction To Assembly Language Programming: From Soup To Nuts: Arm Edition, Charles W. Kann

Open Educational Resources

This is an ARM Assembly Language Textbook designed to be used in classes such as Computer Organization, Operating Systems, Compilers, or any other class that needs to provide the students with a overall of Arm Assembly Language. As with all Soup to Nuts books, it is intended to be a resource where each chapter builds on the material from previous chapters, and leads the reader from a rudimentary knowledge of assembly language to a point where they can use it in their studies.

Spring 2021

In The Loop

IRL Programs Debut; Short & Sweet Pandemic Film Fest; New MS in Artificial Intelligence; Virtual Experts Talks; DePaul Trustee Producing Documentary; DemonHacks Hackathon; Silicon Valley 2.0: The DePaul Innovation Development Lab connects students and companies to spark solutions to technological challenges; Code Warrior: Ovetta Sampson has risen to challenges in digital design, journalism and athletics while inspiring others; Pattern Recognition: A CDM health informatics team joins a global race to advance COVID-19 diagnostics through X-ray insights

Software-Based Side Channel Attacks And The Future Of Hardened Microarchitecture, Nathaniel Hatfield

Senior Honors Theses

Side channel attack vectors found in microarchitecture of computing devices expose systems to potentially system-level breaches. This thesis consists of a comprehensive report on current exploits of this nature, describing their fundamental basis and usage, paving the way to further research into hardware mitigations that may be utilized to combat these and future vulnerabilities. It will discuss several modern software-based side channel attacks, describing the mechanisms they utilize to gain access to privileged information. Attack vectors will be exemplified, along with applicability to various architectures utilized in modern computing. Finally, discussion of how future architectural changes must successfully harden chips …

Separator Of Diametral Path Graphs, Cuong Than

School of Computing: Dissertations, Theses, and Student Research

Nowadays, graph algorithms have been applied to many practical issues such as networking, very large-scale integration (VLSI), and transport systems, etc. Constructing an algorithm to find a maximum independent set (MIS) is one of the first NP-Hard problems which have been studied for a long time. Most scientists believe that there is no polynomial-time algorithm for this problem in general graphs. However, many efficient algorithms and approximation schemes to resolve the MIS problem are found in some special graph classes. In this thesis, we are going to introduce a separator construction for a diametral path graph. By using a property …

Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg

Honors Projects

Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.

Honors Symposium: ML is …

Profile Modeling In Hierarchical Deep Architecture By Mutual Support, Honglai Peng, Meng Han, Jing (Selena) He

Master of Science in Computer Science Theses

Despite significant advances in the field of face analysis over last decade, the current studies are still limited to specific face computation tasks using deep learning approaches. In this paper, we propose an end-to-end hierarchical deep learning structure, called Multi-Features Convolutional Neural Networks (MFCNN), which can comprehensively implement face analysis including age, gender, race and emotion. Moreover, we take the advantages of the mutual support among different facial features from individual tasks to improve the performance of our model. We also contribute one all-labeling dataset called Multiple Facial Features Computation (MFFC) based on Apparent-age-V2 dataset. Firstly, we train four different …

Model Counting Meets F0 Estimation, A. Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel

School of Computing: Faculty Publications

Constraint satisfaction problems (CSP’s) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP’s and computation of zeroth frequency moments (F₀) for data streams.

Our investigations lead us to observe striking similarity …

Game-Based Learning In Science: Can Video Games Simplify Organic Chemistry?, Rachel Israel

Senior Honors Theses

Organic chemistry has been taught in the same way for decades, and students still have difficulty understanding and comprehending the subject material. Perhaps it is time to change the methods by which this subject is taught. Video games have been successfully used in education to create learning environments that increase student motivation and engagement as well as challenge students and promote collaboration. It is difficult for students to maintain a growth mindset in organic chemistry within the classroom. However across different genres, video games create a unique environment where an individual is encouraged to try again when they fail. This …

Storing Intermediate Results In Space And Time: Sql Graphs And Block Referencing, Basem Ibrahim Elazzabi

Dissertations and Theses

With the advancement of data-collection technology and with more data being available for data analysts for data-intensive decision making, many data analysts use client-based data-analysis environments to analyze that data. Client-based environments where only a personal computer or a laptop is used to perform data analysis tasks are common. In such client-based environments, multiple tools and systems are typically needed to accomplish data-analysis tasks. Stand-alone systems such as spreadsheets, R, Matlab, and Tableau are usually easy to use, and they are designed for the typical, non-technical data analyst. However, these systems are limited in their data-analysis capabilities. More complex data …

Pothole Detection Under Diverse Conditions Using Object Detection Models, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever

Conference papers

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalisable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles, and resolutions. In this paper, we present our …

Detection Of Health-Related Behaviours Using Head-Mounted Devices, Shengjie Bi

Dartmouth College Ph.D Dissertations

The detection of health-related behaviors is the basis of many mobile-sensing applications for healthcare and can trigger other inquiries or interventions. Wearable sensors have been widely used for mobile sensing due to their ever-decreasing cost, ease of deployment, and ability to provide continuous monitoring. In this dissertation, we develop a generalizable approach to sensing eating-related behavior.

First, we developed Auracle, a wearable earpiece that can automatically detect eating episodes. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the head. This audio data is then processed by a …

Modernizing Legacy Business Practices And Maintaining Backwards Compatibility When Replacing Legacy Software, Thomas Hillebrandt

Honors Program: Senior Projects (Public)

As technology advances and hardware as well as user expectations becomes more advanced, software systems must change alongside or go obsolete. When software is no longer developed, decisions must be made regarding its future. Through various methods, legacy software may continue to see usage far past its obsolescence, however legacy software will sooner or later face replacement by new applications, built for state-of-the-art machines, to comply with modern requirements. When writing new software to replace older programs, the added challenge for developers is to help the client also modernize their workflow. When a program has been in long time use …

Development Of Multi-Objective Based Spectrum-Aware Routing Protocol For Cognitive Radio Ad Hoc Networks, Rashmi Naveen Raj

Manipal Institute of Technology, Manipal Theses and Dissertations

No abstract provided.

A Zenith Z-100 Emulator, Joseph Matta

Master’s Theses and Projects

The Zenith Z-100 computer was released by the Zenith Data Systems Corporation in 1982 as a competitor to the IBM PC. There are no known complete software emulations of the system. A Z-100 emulator is considered to be complete if it runs all functions of its monitor ROM BIOS program and is able to boot and run its two operating systems. One reason previous emulation attempts are not complete is that they ineffectively implement the floppy disk controller, preventing a proper transfer of the operating system from disk into memory. This project is an attempt to write a complete emulation …

Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai

Computer Science and Computer Engineering Undergraduate Honors Theses

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, …

Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano

Computer Science and Computer Engineering Undergraduate Honors Theses

Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …

Revolt Pimenov Taught Us How To Be Scientists, Vladik Kreinovich

Departmental Technical Reports (CS)

In 2021, we are celebrating the 90th birthday of Revolt Pimenov, a specialist in space-time geometry. He was my teacher. In this article, I am trying to summarize what he taught to his students.

Data-Driven Approaches To Complex Materials: Applications To Amorphous Solids, Dil Kumar Limbu

Dissertations

While conventional approaches to materials modeling made significant contributions and advanced our understanding of materials properties in the past decades, these approaches often cannot be applied to disordered materials (e.g., glasses) for which accurate total-energy functionals or forces are either not available or it is infeasible to employ due to computational complexities associated with modeling disordered solids in the absence of translational symmetry. In this dissertation, a number of information-driven probabilistic methods were developed for the structural determination of a range of materials including disordered solids to transition metal clusters. The ground-state structures of transition-metal clusters of iron, nickel, and …

City Goers: An Exploration Into Creating Seemingly Intelligent A.I. Systems, Matthew Brooke

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial Intelligence systems have come a long way over the years. One particular application of A.I. is its incorporation in video games. A key goal of creating an A.I. system in a video game is to convey a level of intellect to the player. During playtests for Halo: Combat Evolved, the developers at Bungie noticed that players deemed tougher enemies as more intelligent than weaker ones, despite the fact that there were no differences in behavior in the enemies. The tougher enemies provided a greater illusion of intelligence to the players. Inspired by this, I set out to create a …

Why Kappa Regression?, Julio C. Urenda, Orsolya Csiszár, József Dombi, György Eigner, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent book provide examples that a new class of probability distributions and membership functions -- called kappa-regression distributions and membership functions -- leads to better data processing results than using previously known classes. In this paper, we provide a theoretical explanation for this empirical success -- namely, we show that these distributions are the only ones that satisfy reasonable invariance requirements.

Fuzzy Techniques, Laplace Indeterminacy Principle, And Maximum Entropy Approach Explain Lindy Effect And Help Avoid Meaningless Infinities In Physics, Julio C. Urenda, Sean R. Aguilar, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many real-life situations, the only information that we have about some quantity S is a lower bound T ≤ S. In such a situation, what is a reasonable estimate for S? For example, we know that a company has survived for T years, and based on this information, we want to predict for how long it will continue surviving. At first glance, this is a type of a problem to which we can apply the usual fuzzy methodology -- but unfortunately, a straightforward use of this methodology leads to a counter-intuitive infinite estimate for S. There is an empirical …

What Is Wrong With Micromanagement: Economic View, Sean R. Aguilar, Olga Kosheleva

Departmental Technical Reports (CS)

Purpose: It is well known that micromanagement -- excessive control of employees -- is detrimental to the employees' morale and thus, decreases their productivity. But what if the managers keep people happy -- will there still be negative consequences of micromanagement? This is the problem analyzed in this paper.

Design/methodology/approach: To analyze our problem, we use general -- but simplified -- mathematical models of how productivity depends on the working rate.

Findings: We show that even in the absence of psychological discomfort, micromanagement is still detrimental to productivity. Interestingly, the negative effect of micromanagement increases as the population becomes more …

Is Our World Becoming Less Quantum?, Lidice Castro, Vladik Kreinovich

Departmental Technical Reports (CS)

According to the general idea of quantization, all physical dependencies are only approximately deterministic, and all physical "constants" are actually varying. A natural conclusion -- that some physicists made -- is that Planck's constant (that determines the magnitude of quantum effects) can also vary. In this paper, we use another general physics idea -- the second law of thermodynamics -- to conclude that with time, this constant can only decrease. Thus, with time (we are talking cosmological scales, of course), our world is becoming less quantum.

How Accurate Are Fuzzy Control Recommendations: Interval-Valued Case, Juan Carlos Figueroa-Garcia, Vladik Kreinovich

Departmental Technical Reports (CS)

As a result of applying fuzzy rules, we get a fuzzy set describing possible control values. In automatic control systems, we need to defuzzify this fuzzy set, i.e., to transform it to a single control value. One of the most frequently used defuzzification techniques is centroid defuzzification. From the practical viewpoint, an important question is: how accurate is the resulting control recommendation? The more accurately we need to implement the control, the more expensive the resulting controller.

The possibility to gauge the accuracy of the fuzzy control recommendation follows from the fact that, from the mathematical viewpoint, centroid defuzzification is …

Order Relations Are Ubiquitously Fundamental: Alexandrov(-Zeeman) Theorem Extended From Space-Time Physics To Logical Reasoning And Decision Making, Vladik Kreinovich, Olga Kosheleva, Laxman Bokati, Laura Berrout

Departmental Technical Reports (CS)

In all areas of human activity, there are natural ordering relations: causality in space-time physics, preference in decision making, and logical inference in reasoning. In space-time physics, a 1950 theorem by A. D. Alexandrov proved that causality relation is fundamental: many other features, including numerical characteristics of time and space, can be reconstructed from this relation. In this paper, we provide simple proofs that, similarly, the corresponding ordering relations are fundamental in decision making and in logical reasoning.

Shall We Ignore All Intermediate Grades?, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In most European universities, the overall student's grade for a course is determined exclusively by this student's performance on the final exam. All intermediate grades -- on homework, quizzes, and previous texts -- are, in effect, ignored. This arrangement helps gauge the student's performance by the knowledge that the student shows at the end of the course. The main drawback of this approach is that some students do not start studying until later, thinking that they can catch up and even get an excellent grade -- and this hurts their performance. To motivate students to study hard throughout the semester, …

Extension To Multidimensional Problems Of A Fuzzy-Based Explainable & Noise-Resilient Algorithm, Javier Viana, Stephan Ralescu, Kelly Cohen, Anca Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

While Deep Neural Networks (DNNs) have shown incredible performance in a variety of data, they are brittle and opaque: easily fooled by the presence of noise, and difficult to understand the underlying reasoning for their predictions or choices. This focus on accuracy at the expense of interpretability and robustness caused little concern since, until recently, DNNs were employed primarily for scientific and limited commercial work. An increasing, widespread use of artificial intelligence and growing emphasis on user data protections, however, motivates the need for robust solutions with explainable methods and results. In this work, we extend a novel fuzzy based …