Computer Sciences Commons^™

The Symptom Of Ethics: Rethinking Ethics In The Face Of The Machine, David J. Gunkel

Human-Machine Communication

This essay argues that it is the machine that constitutes the symptom of ethics— “symptom” understood as that excluded “part that has no part” in the system of moral consideration. Ethics, which has been historically organized around a human or at least biological subject, needs the machine to define the proper limits of the moral community even if it simultaneously excludes such mechanisms from any serious claim on moral consideration. The argument will proceed in five steps or movements. The first part will define and characterize “the symptom” as it has been operationalized in the work of Slovenian philosopher Slavoj …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

State-Of-The-Art Versus Deep Learning: A Comparative Study Of Motor Imagery Decoding Techniques, Olawunmi Olaboopo George, Sarthak Dabas, Abdur Sikder, Roger O. Smith, Praveen Madiraju, Nasim Yahyasoltani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

State-of-the-art techniques (SOTA) for motor imagery decoding have largely involved the use of common spatial patterns (CSP) and power spectral density (PSD), for feature extraction. Other frequency transforms, such as wavelets and empirical mode decomposition (EMD) have also been used but the aforementioned two have been the most popular. For classification, linear discriminant analysis (LDA) and support vector machines (SVM) have been mostly used. It is, however, worth investigating other approaches, such as deep learning, which offer a potential for improvement, but are not yet mainstream. Deep learning techniques based on neural networks (NNs) have been underexplored in motor imagery …

Testability Of Instrumental Variables In Linear Non-Gaussian Acyclic Causal Models, Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang

Machine Learning Faculty Publications

This paper investigates the problem of selecting instrumental variables relative to a target causal influence X → Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria …

Ubjective Information And Survival In A Simulated Biological System, Tyler S. Barker, Massimiliano Pierobon, Peter J. Thomas

School of Computing: Faculty Publications

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. …

Image Provenance Analysis, Daniel Moreira, William Theisen, Walter Scheirer, Aparna Bharati, Joel Brogan, Anderson Rocha

Computer Science: Faculty Publications and Other Works

The literature of multimedia forensics is mainly dedicated to the analysis of single assets (such as sole image or video files), aiming at individually assessing their authenticity. Different from this, image provenance analysis is devoted to the joint examination of multiple assets, intending to ascertain their history of edits, by evaluating pairwise relationships. Each relationship, thus, expresses the probability of one asset giving rise to the other, through either global or local operations, such as data compression, resizing, color-space modifications, content blurring, and content splicing. The principled combination of these relationships unveils the provenance of the assets, also constituting an …

Closing Social

Computer Science Graduate Research Workshop

No abstract provided.

Prediction Of Balance Control And Instability In Walking At Sea, Jungyeon Choi

Computer Science Graduate Research Workshop

No abstract provided.

Interactive Visualizations For Explanation In Ai, Ashley Ramsey

Computer Science Graduate Research Workshop

No abstract provided.

Automated Program Repair For Api Misuse Vulnerabilities, Dip Kiran Pradhan Newar

Computer Science Graduate Research Workshop

No abstract provided.

New Approaches To Convex Polygon Formations, Rui Yang

Computer Science Graduate Research Workshop

No abstract provided.

Workload Prediction With Cost-Aware Data Analytics System, Anshuman Das Mohapatra

Computer Science Graduate Research Workshop

No abstract provided.

Artificial-Intelligence-Based Approaches For Estimating Irregular Walking Surface With Wearable Sensor, Ru Ng

Computer Science Graduate Research Workshop

No abstract provided.

Building Interpretable Methods For Identifying Bridge Maintenance Patterns, Akshay Kale

Computer Science Graduate Research Workshop

No abstract provided.

Using Deep Neural Network And Transformers To Extract Graphene Compounds And Properties, Ayman Ibn Jaman

Computer Science Graduate Research Workshop

No abstract provided.

Deep Learning-Based Image Watermarking With Transform Invariant Representation Learning, Arjon Das

Computer Science Graduate Research Workshop

No abstract provided.

Opening Social

Computer Science Graduate Research Workshop

No abstract provided.

Industrial Digital Twins At The Nexus Of Nextg Wireless Networks And Computational Intelligence: A Survey, Shah Zeb, Aamir Mahmood, Syed Ali Hassan, Md. Jalil Piran, Mikael Gidlund, Mohsen Guizani

Machine Learning Faculty Publications

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and …

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …

Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger

Faculty Publications

Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …

Lyapunov-Based Economic Model Predictive Control For Detecting And Handling Actuator And Simultaneous Sensor/Actuator Cyberattacks On Process Control Systems, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The controllers for a cyber-physical system may be impacted by sensor measurement cyberattacks, actuator signal cyberattacks, or both types of attacks. Prior work in our group has developed a theory for handling cyberattacks on process sensors. However, sensor and actuator cyberattacks have a different character from one another. Specifically, sensor measurement attacks prevent proper inputs from being applied to the process by manipulating the measurements that the controller receives, so that the control law plays a role in the impact of a given sensor measurement cyberattack on a process. In contrast, actuator signal attacks prevent proper inputs from being applied …

Unsupervised Automatic Speech Recognition: A Review, Hanan Aldarmaki, Asad Ullah, Sreepratha Ram, Nazar Zaki

Natural Language Processing Faculty Publications

Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of interest. In this paper, we review the research literature to identify models and ideas that could lead to fully unsupervised ASR, including unsupervised sub-word and word modeling, unsupervised segmentation of the speech signal, and unsupervised mapping from speech segments to text. The objective of the study is to identify the limitations of what can be learned from speech data alone and to understand the minimum …

Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization, Isurumali Neththikumara, Todd Satogata, Alex Bogacz, Ryan Bodenstein, Arthur Vandenhoeke

College of Sciences Posters

Beamline optics design for recirculatory linear accelerators requires special attention to suppress beam instabilities arising due to collective effects. The impact of these collective effects becomes more pronounced with the addition of energy recovery (ER) capability. Jefferson Lab’s multi-pass, multi-GeV ER proposal for the CEBAF accelerator, ER@CEBAF, is a 10- pass ER demonstration with low beam current. Tighter control of the beam parameters at lower energies is necessary to avoid beam break-up (BBU) instabilities, even with a small beam current. Optics optimizations require balancing both beta excursions at high-energy passes and overfocusing at low-energy passes. Here, we discuss an optics …

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections, Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti, Sorawich Maichum

College of Sciences Posters

We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on …

Medical Devices And Cybersecurity, Hilary Finch

School of Cybersecurity Posters

I begin by looking at the role of cybersecurity in the medical world. The healthcare industry adopted information technology quite quickly. While the advancement was obviously beneficial and necessary to keep up with an ever-growing demand, the healthcare industry did not place any kind of pointed focus on the security of their IT department, or the sensitive information housed therein.

When rapid advancements of technology outpaced the gradual advancement of hospital cybersecurity, security concerns became a difficult issue to control. There is a serious need for more advancements in hospital security. Each interconnected medical device has its own unique security …

Why Rectified Linear Unit Is Efficient In Machine Learning: One More Explanation, Barnabas Bede, Vladik Kreinovich, Uyen Pham

Departmental Technical Reports (CS)

In many applications, in particular, in econometric application, deep learning techniques are very effective. In this paper, we provide a new explanation for why rectified linear units -- the main units of deep learning -- are so effective. This explanation is similar to the usual explanation of why Gaussian (normal) distributions are ubiquitous -- namely, it is based on an appropriate limit theorem.

A Parallel Algorithm Template For Updating Single-Source Shortest Paths In Large-Scale Dynamic Networks, Arindam Khanda, Sriram Srinivasan, Sanjukta Bhowmick, Boyana Norris, Sajal K. Das

Computer Science Faculty Research & Creative Works

The Single Source Shortest Path (SSSP) problem is a classic graph theory problem that arises frequently in various practical scenarios; hence, many parallel algorithms have been developed to solve it. However, these algorithms operate on static graphs, whereas many real-world problems are best modeled as dynamic networks, where the structure of the network changes with time. This gap between the dynamic graph modeling and the assumed static graph model in the conventional SSSP algorithms motivates this work. We present a novel parallel algorithmic framework for updating the SSSP in large-scale dynamic networks and implement it on the shared-memory and GPU …

K-Means Clustering Using Gravity Distance, Ajinkya Vishwas Indulkar

Masters Theses & Specialist Projects

Clustering is an important topic in data modeling. K-means Clustering is a well-known partitional clustering algorithm, where a dataset is separated into groups sharing similar properties. Clustering an unbalanced dataset is a challenging problem in data modeling, where some group has a much larger number of data points than others. When a K-means clustering algorithm with Euclidean distance is applied to such data, the algorithm fails to form good clusters. The standard K-means tends to split data into smaller clusters during a clustering process evenly.

We propose a new K-means clustering algorithm to overcome the disadvantage by introducing a different …

The Causal Fairness Field Guide: Perspectives From Social And Formal Sciences, Alycia Carey, Xintao Wu

Computer Science and Computer Engineering Faculty Publications and Presentations

Over the past several years, multiple different methods to measure the causal fairness of machine learning models have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of causality-based fairness notions with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of causality-based fairness notions produced by both social and formal (specifically machine learning) sciences in this field guide. In addition to giving the mathematical backgrounds of several popular causality-based fair machine …