Computer Sciences Commons^™

New Types Of Topologies And Neutrosophic Topologies, Florentin Smarandache

Neutrosophic Systems with Applications

In this paper, we recall the six new types of topologies that we introduced in the last years (2019-2022), such as: Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, NeutroTopology, AntiTopology, SuperHyperTopology, and Neutrosophic SuperHyperTopology.

Exploring Barriers To The Adoption Of Internet Of Things-Based Precision Agriculture Practices, Gaganpreet Singh Hundal, Chad Laux, Dennis R. Buckmaster, Mathias J. Sutton, Michael Langemeier

Faculty Publications

The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review …

Network Slicing Via Transfer Learning Aided Distributed Deep Reinforcement Learning, Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle

School of Computing: Faculty Publications

Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the …

New Types Of Topologies And Neutrosophic Topologies, Florentin Smarandache

Neutrosophic Systems with Applications

In this paper, we recall the six new types of topologies that we introduced in the last years (2019-2022), such as: Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, NeutroTopology, AntiTopology, SuperHyperTopology, and Neutrosophic SuperHyperTopology.

Divergent Directionality Of Immune Cell-Specific Protein Expression Between Bipolar Lithium Responders And Non-Responders Revealed By Enhanced Flow Cytometry, Keming Gao, Nicholas M. Kaye, Marzieh Ayati, Mehmet Koyuturk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan

Computer Science Faculty Publications

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4+ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4+ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow …

Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia

Theses and Dissertations

An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth.

Snapshot Metrics Are Not Enough: Analyzing Software Repositories With Longitudinal Metrics, Nicholas Synovic, Matt Hyattt, Rohan Sethi, Sohini Thota, Shilpika, Allan J. Miller, Wenxin Jiang, Emmanuel S. Amobi, Austin Pinderski, Konstantin Läufer, Nicholas J. Hayward, Neil Klingensmith, James C. Davis, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time -- longitudinal metrics that give insight about process, not just product. In this work, we present PRiME (PRocess MEtrics), a tool for computing and visualizing process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda. …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Fatmaelzahraa Eltaher Ph.D, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Csc 71010/Csci 77100: Programming Languages/Software Engineering, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Situational awareness during any disaster depends on effective communication and location tracking. In the case of underground mines, where the communication methods are mostly central, the whole communication channel would be rendered unusable during a disaster. To this end, we propose the use of Delay Tolerant Networks (DTN) to allow the miners to function in a distributed manner and help in locating the injured miners and routing distress messages. Due to the unavailability of GPS signals, the pillar numbers are used to identify the locations of the miners. For spatio-temporal routing of messages, we formulate a new scheme using Contact …

Scalable Skill-Oriented Task Allocation In Crowdsourcing Within A Serverless Ecosystem, Biswajeet Sethi, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Allocating the most competent crowdworkers to each upcoming task is a fundamental challenge in crowdsourcing. The mechanism becomes complicated when the arriving tasks require a high level of expertise within a constrained budget. The validation of skill matching between tasks and crowdworkers adds a new dimension to the traditional problem of task allocation. In addition, in real-world scenarios, the influx of both tasks and workers is dynamic, making it nearly impossible to predict the precise amount of computational resources required for the crowdsourcing platform to operate efficiently. Serverless computing is a new pay-per-use, auto-scalable, Function-as-a-Service based model, that ensures parallel …

Sptframe: A Framework For Spatio-Temporal Information Aware Message Dissemination In Software Defined Vehicular Networks, Ankur Nahar, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

The volume of vehicular network traffic is very context (time and geographic location) and technology-dependent. Considering both multi-hop geocast and single-hop broadcast techniques, the route availability can be affected by transient and permanent traffic variations. Therefore, our research tackles one of the most pressing challenges in vehicular ad-hoc networks (VANETs), i.e., accommodating fine-grained spatio-temporal variance in vehicular density over time and space. This article proposes a new framework called SpTFrame to achieve fast message dissemination. The proposed approach uses a software-defined vehicular networks (SDVNs) architecture along with a deep reinforcement learning (DRL) model. SpTFrame employs a convolutional neural network (CNN) …

A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …

A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …

Five Ideas For How Professors Can Deal With Gpt-3 ... For Now, Travis Ryan Pickell, Brian R. Doak

Faculty Publications - George Fox School of Theology

"The most immediate question that needs to be addressed is pedagogical: how can we continue to teach in the GPT Age?...Beyond the questions of pedagogical best practices, GPT-3 raises deeper philosophical and pragmatic questions about the nature and purpose of higher education."

Introduction, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Working With Control-Flow Graphs, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Reengineering And Refactoring, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Building An Ast Eclipse Plug-In, Raffi Khatchadourian

Open Educational Resources

Complete the Building an AST Eclipse Plug-in assignment. Once it works, find a medium-sized open-source Java project to run your plugin on. You may want to explore GitHub. Import the project into Eclipse and run your plug-in on it. Report on the following, which may require you to change some of the source code so that it is convenient:

1. Project name.
2. Project URL.
3. Project description.
4. The number of classes in the project.
5. The number of user-defined methods in the project.
6. For each class, the number of method calls.
7. Statistics about the method calls:
   1. The total number of method calls …

Wala Quick Start, Raffi Khatchadourian

Open Educational Resources

Setting up and trying the TJ Watson Library for Analysis (WALA).

Towards A Framework For Privacy-Preserving Pedestrian Analysis, Anil Kunchala, Mélanie Bouroche, Bianca Schoen-Phelan

Conference papers

The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving …

Data Augmentation For Neutron Spectrum Unfolding With Neural Networks, James Mcgreivy, Juan J. Manfredi, Daniel Siefman

Faculty Publications

Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and …

Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams, Nora Basha, Bechir Hamdaoui, Kathiravetpillai Sivanesan, Mohsen Guizani

Machine Learning Faculty Publications

Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters' inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channel-resilient …

Safe Space: To Help Minimize Cyberbullying And Support Social Well-Being, Eiman Rana

CISLA Senior Integrative Projects

The perverseness of cyberbullying as a growing and serious form of abuse with the potential for harm among children and youth needs to be recognized. In a resource-limited setting such as South Asia where youth have very little access to counseling within schools, the implications for mental health should be recognized. This research study explores whether our youth is capable of using technology in the right way to minimize cyberbullying and support social well-being.

Index Terms - Cyberbullying, Social Well-being, User Experience Design, and Usability Study.

Self-Omics: A Self-Supervised Learning Framework For Multi-Omics Cancer Data, Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub

Computer Vision Faculty Publications

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data. However, there is a lack of studies that use SSL methods to exploit inter-omics relationships on unlabelled multi-omics data. In this work, we develop a novel and efficient pre-training paradigm that consists of various SSL components, including but not limited …

Unmasking Deception In Vanets: A Decentralized Approach To Verifying Truth In Motion, Susan Zehra, Syed R. Rizvi, Steven Olariu

College of Sciences Posters

VANET, which stands for "Vehicular Ad Hoc Network," is a wireless network that allows vehicles to communicate with each other and with infrastructure, such as Roadside Units (RSUs), with the aim of enhancing road safety and improving the overall driving experience through real-time exchange of information and data. VANET has various applications, including traffic management, road safety alerts, and navigation. However, the security of VANET can be compromised if a malicious user alters the content of messages transmitted, which can harm both individual vehicles and the overall trust in VANET technology. Ensuring the correctness of messages is crucial for the …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

College of Sciences Posters

To identify robots and human users in web archives, we conducted a study using the access logs from the Internet Archive’s (IA) Wayback Machine in 2012 (IA2012), 2015 (IA2015), and 2019 (IA2019), and the Portuguese Web Archive (PT) in 2019 (PT2019). We identified user sessions in the access logs and classified them as human or robot based on their browsing behavior. In 2013, AlNoamany et al. [1] studied the user access patterns using IA access logs from 2012. They established four web archive user access patterns: single-page access (Dip), access to the same page at multiple archive times (Dive), access …

Adhd Prediction Through Analysis Of Eye Movements With Graph Convolution Network, Gavindya Jayawardena, Sampath Jayarathna, Yi He

College of Sciences Posters

Processing speech with background noise requires appropriate parsing of the distorted auditory signal, fundamental language abilities as well as higher signal-to-noise ratio. Adolescents with ADHD have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity. In this study we utilize Audiovisual Speech-In-Noise performance and eye-tracking measures of young adults with ADHD compared to age-matched controls, and generate graphs for ADHD evaluation using the eye-tracking data. We form graphs utilizing the eight eye-tracking features (fixation count, average, total, and standard deviation of fixation duration, max and min saccade peak velocity, min, average, and standard deviation …

Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Future particle accelerators will exceed by far the current data size (10¹⁵) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …

Interval-Valued And Set-Valued Extensions Of Discrete Fuzzy Logics, Belnap Logic, And Color Optical Computing, Victor L. Timchenko, Yury P. Kondratenko, Vladik Kreinovich

Departmental Technical Reports (CS)

It has been recently shown that in some applications, e.g., in ship navigation near a harbor, it is convenient to use combinations of basic colors -- red, green, and blue -- to represent different fuzzy degrees. In this paper, we provide a natural explanation for the efficiency of this empirical fact: namely, we show that it is reasonable to consider discrete fuzzy logics, it is reasonable to consider their interval-valued and set-valued extensions, and that a set-valued extension of the 3-values logic is naturally equivalent to the use of color combinations.