Computer Sciences Commons^™

The Future Of Ai Accountability In The Financial Markets, Gina-Gail S. Fletcher, Michelle M. Le

Faculty Scholarship

Consumer interaction with the financial market ranges from applying for credit cards, to financing the purchase of a home, to buying and selling securities. And with each transaction, the lender, bank, and brokerage firm are likely utilizing artificial intelligence (AI) behind the scenes to augment their operations. While AI’s ability to process data at high speeds and in large quantities makes it an important tool for financial institutions, it is imperative to be attentive to the risks and limitations that accompany its use. In the context of financial markets, AI’s lack of decision-making transparency, often called the “black box problem,” …

Timestamp Estimation From Outdoor Scenes, Tawfiq Salem, Jisoo Hwang, Rafael Padilha

Annual ADFSL Conference on Digital Forensics, Security and Law

The increasing availability of smartphones allowed people to easily capture and share images on the internet. These images are often associated with metadata, including the image capture time (timestamp) and the location where the image was captured (geolocation). The metadata associated with images provides valuable information to better understand scenes and events presented in these images. The timestamp can be manipulated intentionally to provide false information to convey a twisted version of reality. Images with manipulated timestamps are often used as a cover-up for wrongdoing or broadcasting false claims and competing views on the internet. Estimating the time of capture …

Anatomy Of An Internet Hijack And Interception Attack: A Global And Educational Perspective, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Annual ADFSL Conference on Digital Forensics, Security and Law

The Internet’s underlying vulnerable protocol infrastructure is a rich target for cyber crime, cyber espionage and cyber warfare operations. The stability and security of the Internet infrastructure are important to the function of global matters of state, critical infrastructure, global e-commerce and election systems. There are global approaches to tackle Internet security challenges that include governance, law, educational and technical perspectives. This paper reviews a number of approaches to these challenges, the increasingly surgical attacks that target the underlying vulnerable protocol infrastructure of the Internet, and the extant cyber security education curricula; we find the majority of predominant cyber security …

Learning A Scalable Algorithm For Improving Betweenness In The Lightning Network, Vincent Davis

Theses and Dissertations--Computer Science

This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it occurs in the Bitcoin Lightning Network. In this approach, each node is embedded with a feature vector whereby an Advantage Actor-Critic model identifies key nodes in the network that a joining node should open channels with to maximize its own expected routing opportunities. This model is trained using a custom built environment, lightning-gym, which can randomly generate small scale-free networks or import snapshots of the Lightning Network. After 100 training episodes on networks with 128 nodes, this A2C agent can recommend channels in the Lightning …

Enriching Smart Cities By Optimizing Electric Vehicle Ride-Sharing Through Game Theory, Darko Radakovic, Anuradha Singh, Aparna S. Varde, Pankaj Lal

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via …

Progress In Protein Structure Prediction: An Xai Perspective, Yosef E. Granillo, Badri Adhikari

Undergraduate Research Symposium

The full extent of the impact of deep learning models on structural biology will depend on their ability to provide novel biological insights. The field of structure prediction, where deep learning has produced miraculously accurate results, is at a critical stage of benefiting from the methods in interpretable deep learning. The exact mechanisms by which advanced computational models learn to interpret protein shapes and functions during their training remain largely unclear. These questions underscore the need for further research, as understanding these mechanisms is crucial for researchers to trust the predictions of these models. However, interpretable machine learning is ripening …

Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …

Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, sideslipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's …

An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das

Computer Science Faculty Research & Creative Works

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, …

Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das

Computer Science Faculty Research & Creative Works

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest …

Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively …

Humans And The Core Partition: An Agent-Based Modeling Experiment, Andrew J. Collins, Sheida Etemadidavan

Engineering Management & Systems Engineering Faculty Publications

Although strategic coalition formation is traditionally modeled using cooperative game theory, behavioral game theorists have repeatedly shown that outcomes predicted by game theory are different from those generated by actual human behavior. To further explore these differences, in a cooperative game theory context, we experiment to compare the outcomes resulting from human participants’ behavior to those generated by a cooperative game theory solution mechanism called the core partition. Our experiment uses an interactive simulation of a glove game, a particular type of cooperative game, to collect the participant’s decision choices and their resultant outcomes. Two different glove games are considered, …

Exploring Blockchain Adoption Supply Chains: Opportunities And Challenges, Adrian V. Gheorghe, Omer F. Keskin, Farinaz Sabz Ali Pour

Engineering Management & Systems Engineering Faculty Publications

In modern supply chains, acquisition often occurs with the involvement of a network of organizations. The resilience, efficiency, and effectiveness of supply networks are crucial for the viability of acquisition. Disruptions in the supply chain require adequate communication infrastructure to ensure resilience. However, supply networks do not have a shared information technology infrastructure that ensures effective communication. Therefore decision-makers seek new methodologies for supply chain management resilience. Blockchain technology offers new decentralization and service delegation methods that can transform supply chains and result in a more flexible, efficient, and effective supply chain. This report presents a framework for the application …

Interleaving A Symbolic Story Generator With A Neural Network-Based Large Language Model, Jingwen Xiang, Zoie Zhao, Mackie Zhou, Megan Mckenzie, Alexis Kilayko, Jamie C. Macbeth, Scott Carter, Katharine Sieck, Matthew Klenk

Computer Science: Faculty Publications

Research in deep learning has recently produced models of natural language that are capable of generating natural language output which, at a glance, has strong similarities to that written by intelligent humans. However, the texts produced by deep learning-based large language models (LLMs), upon deeper examination, reveal the challenges that they have in producing outputs that maintain logical coherence. One specific application area of interest for LLMs is in fictional narrative generation, a mode of operation in which stories are generated by the model in response to a prompt text that indicates the start of a story or the desired …

Federated Learning For Lung Sound Analysis, Afia Farjana

Dissertations and Theses

Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, building machine learning models with limited datasets at different sites is not trivial. In addition, medical imaging is no exception. In medical imaging informatics, precise detection of lung disease helps clinicians treat patients effectively while averting possible fatalities. To effectively build machine learning models, we propose to study federated learning mechanisms, so we can use/learn datasets from different sources/regions/sites. . Individual sites may jointly train a global model using this approach, referred to as federated learning. In other words, without explicitly sharing datasets, federated …

Studying Spread Patterns Of Covid-19 Based On Spatiotemporal Data, Beiyu Lin, Xiaowei Jia, Zhiqian Chen

Computer Science Faculty Publications

The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases.

In this tutorial, we will review the trending state-of-theart …

A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged …

Federated Secure Data Sharing By Edge-Cloud Computing Model*, Arijit Karati, Sajal K. Das

Computer Science Faculty Research & Creative Works

Data sharing by cloud computing enjoys benefits in management, access control, and scalability. However, it suffers from certain drawbacks, such as high latency of downloading data, non-unified data access control management, and no user data privacy. Edge computing provides the feasibility to overcome the drawbacks mentioned above. Therefore, providing a security framework for edge computing becomes a prime focus for researchers. This work introduces a new key-aggregate cryptosystem for edge-cloud-based data sharing integrating cloud storage services. The proposed protocol secures data and provides anonymous authentication across multiple cloud platforms, key management flexibility for user data privacy, and revocability. Performance assessment …

Improving Age Of Information With Interference Problem In Long-Range Wide Area Networks, Preti Kumari, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a …

Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required …

Validating Software States Using Reverse Execution, Nathaniel Christian Boland

Browse all Theses and Dissertations

A key feature of software analysis is determining whether it is possible for a program to reach a certain state. Various methods have been devised to accomplish this including directed fuzzing and dynamic execution. In this thesis we present a reverse execution engine to validate states, the Complex Emulator. The Complex Emulator seeks to validate a program state by emulating it in reverse to discover if a contradiction exists. When unknown variables are found during execution, the emulator is designed to use constraint solving to compute their values. The Complex Emulator has been tested on small assembly programs and is …

Synthetic Aperture Ladar Automatic Target Recognizer Design And Performance Prediction Via Geometric Properties Of Targets, Jacob W. Ross

Browse all Theses and Dissertations

Synthetic Aperture LADAR (SAL) has several phenomenology differences from Synthetic Aperture RADAR (SAR) making it a promising candidate for automatic target recognition (ATR) purposes. The diffuse nature of SAL results in more pixels on target. Optical wavelengths offers centimeter class resolution with an aperture baseline that is 10,000 times smaller than an SAR baseline. While diffuse scattering and optical wavelengths have several advantages, there are also a number of challenges. The diffuse nature of SAL leads to a more pronounced speckle effect than in the SAR case. Optical wavelengths are more susceptible to atmospheric noise, leading to distortions in formed …

A Universal Cybersecurity Competency Framework For Organizational Users, Patricia A. Baker

CCAC Theses and Dissertations

The global reliance on the Internet to facilitate organizational operations necessitates further investments in organizational information security. Such investments hold the potential for protecting information assets from cybercriminals. To assist organizations with their information security, The National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework (NCWF) was created. The framework referenced the cybersecurity work, knowledge, and skills required to competently complete the tasks that strengthen their information security. Organizational users’ limited cybersecurity competency contributes to the financial and information losses suffered by organizations year after year. While most organizational users may be able to respond positively to a cybersecurity threat, …

Explicating Consumer Adoption Of Wearable Technologies: A Case Of Smartwatches From The Asean Perspective, Veerisa Chotiyaputta, Donghee Shin

All Works

This research aims to determine the key antecedent factors in consumers' adoption of and their intention to recommend smartwatch wearable technology. The proposed research model combines the current technology acceptance and innovation diffusion theories with perceived aesthetic and perceived privacy risk to explain individuals' smartwatch adoption and subsequent recommendation to other people. Based on a sample of 299 completed individual online surveys, the research employed partial least squares (a variance-based analysis method) for the model and hypotheses testing. The results showed some similarities as well as differences from the previous literature. The study found that performance expectancy, habit, and perceived …

Automated Chest X-Ray Analysis: Biomedical/Non-Biomedical Foreign Object Detection, Shotabdi Roy

Dissertations and Theses

The presence of non-biomedical foreign objects (NBFO) such as coins, buttons, jewelry, etc. and biomedical foreign objects (BFO) such as medical tubes, and devices in Chest X-Rays (CXRs) make accurate interpretation difficult as they do not indicate known biological abnormalities like excess fluids, Tuberculosis (TB) or cysts. Accurate diagnosis and screening, require these NBFO and BFO to be detected, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. During an automated CXR screening process, NBFOs can adversely impact the process as typical machine learning algorithms would consider these objects to be …

Balancing User Experience For Mobile One-To-One Interpersonal Telepresence, Kevin Pfeil

Electronic Theses and Dissertations, 2020-2023

The COVID-19 virus disrupted all aspects of our daily lives, and though the world is finally returning to normalcy, the pandemic has shown us how ill-prepared we are to support social interactions when expected to remain socially distant. Family members missed major life events of their loved ones; face-to-face interactions were replaced with video chat; and the technologies used to facilitate interim social interactions caused an increase in depression, stress, and burn-out. It is clear that we need better solutions to address these issues, and one avenue showing promise is that of Interpersonal Telepresence. Interpersonal Telepresence is an interaction paradigm …

Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi

Electronic Theses and Dissertations, 2020-2023

With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that …

Towards Leveraging Sparse Infrared Datasets For Multiple View Synthesis, Few Shot Learning And Background Invariant Recognition, Maliha Arif

Electronic Theses and Dissertations, 2020-2023

This dissertation presents a study of various machine learning techniques for recognizing vehicular objects in infrared images. State of the art methods for computer vision have not been widely explored for this part of the electromagnetic spectrum (EM). Challenges that arise due to the dearth of infrared training images, terrain clutter, and thermal phenomenology have not been fully addressed. Infrared dataset collection and annotation is both difficult and expensive. What if there is a way we can generate infrared images and diminish the need for collecting data out in the field? Our first research study encompasses an encoder-decoder model that …

Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron

College of Graduate Studies: Theses & Dissertations

The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.

We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …

Perceptions And Needs Of Artificial Intelligence In Health Care To Increase Adoption: Scoping Review, Han Shi Jocelyn Chew, Palakorn Achananuparp

Research Collection School Of Computing and Information Systems

Background: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, …