Alice In Cyberspace 2024, Stanley Mierzwa

Center for Cybersecurity

‘Alice in Cyberspace’ Conference Nurtures Women’s Interest, Representation in Cybersecurity

Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro

Publications

The intuitive interaction capabilities of augmented reality make it ideal for solving complex 3D problems that require complex spatial representations, which is key for astrodynamics and space mission planning. By implementing common and complex orbital mechanics algorithms in augmented reality, a hands-on method for designing orbit solutions and spacecraft missions is created. This effort explores the aforementioned implementation with the Microsoft Hololens 2 as well as its applications in industry and academia. Furthermore, a human-centered design process and study are utilized to ensure the tool is user-friendly while maintaining accuracy and applicability to higher-fidelity problems.

Identification En Laboratoire Des Éléments Essentiels Au Processus D’Intégration Sécuritaire De Cellules Cobotiques, Sabrina Jocelyn, Élise Ledoux, Damien Burlet-Vienney, Isabelle Berger, Isvieysys Armas Marrero, Chun Hong Law, Yuvin Chinniah, Abdallah Ben Mosbah, Ilian A. Bonev, Denis Denys, Laurent Giraud

Rapports de recherche scientifique

Les cobots sont apparus vers 2010 en industrie et les accidents sont très peu documentés. La gestion des risques en cobotique représente un réel défi. La littérature scientifique montre l’existence de divers modèles, méthodes et outils pour gérer les risques en cobotique, en mettant l’opérateur humain au cœur de l’intégration des applications collaboratives. Cependant, un autre humain clé de la mise en œuvre de ces applications est négligé la plupart du temps. Il s’agit de l’intégrateur, celui qui doit concevoir la cellule cobotique. À notre connaissance, deux études portant sur un même projet de conception d’un logiciel aidant à mettre …

A Fully Automated Global Post-Hoc Method Based On Abstract Argumentation For Explainable Artificial Intelligence And Its Application On Fully Connected Dense Deep Neural Networks, Giulia Vilone

Dissertations

Explainable Artificial Intelligence (XAI) has rapidly grown in the past decade due to the prevalence of machine learning, especially deep learning, in fields like healthcare and finance. While these models excel in accuracy, their complexity hampers transparency and interpretability. Ensuring understandable explanations for AI predictions fosters trust, prevents errors, complies with regulations, and enhances model refinement. The research project outlined in this thesis unfolds in phases. It commences with a comprehensive review of existing XAI studies, contributing to the field’s knowledge by proposing a taxonomy that organises theories and notions related to explainability, the evaluation approaches for XAI methods, and …

Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

On The Performance Of A Photonic Reconfigurable Electromagnetic Band Gap Antenna Array For 5g Applications, Taha A. Elwi, Fatma Taher, Bal S. Virdee, Mohammad Alibakhshikenari, Ignacio J.Garcia Zuazola, Astrit Krasniqi, Amna Shibib Kamel, Nurhan Turker Tokan, Salahuddin Khan, Naser Ojaroudi Parchin, Patrizia Livreri, Iyad Dayoub, Giovanni Pau, Sonia Aissa, Ernesto Limiti, Mohamed Fathy Abo Sree

All Works

In this paper, a reconfigurable Multiple-Input Multiple-Output (MIMO) antenna array is presented for 5G portable devices. The proposed array consists of four radiating elements and an Electromagnetic Band Gap (EBG) structure. Planar monopole radiating elements are employed in the array with Coplanar Waveguide Ports (CWPs). Each CWP is grounded on one side to a reflecting L-shaped structure that has an effect of improving the antenna's directivity. It is shown that by inductively connecting Minkowski fractal structure of 1^{st} order to the radiating element, the impedance matching is improved that results in enhancement in the array's bandwidth performance. The EBG structure …

K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur

Publications

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …

Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth

Publications

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

How Can A Cloud Computing It Framework Be Created And Applied Effectively In The Online Printing Industry?, Stefan Meissner

Dissertations

This research aims to design a cloud computing IT framework for the online printing industry based on a detailed literature review, the development of proof of concepts (PoC), and the conduction of a focus group. The framework can be adopted by the online printing industry or by vendors of print-specific applications to optimize their products for the online printing industry. The author has been working in the online printing process optimization and automation since 2007. During this time, he got deep insight into many industry-specific applications, their architectural design, and their challenges being used in the context of online printing. …

Dynamic Modeling And Control Of A Solid State Semiconductor-Based Transformer, Microgrid And Storage Systems, Rubén Darío Viñán-Velasco

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Smart Grids are power grid models designed with the idea of including the growing new technologies, from generation to storage devices, and are a response to the growing demands from consumers and the presence of electronic components being commonplace in the modern devices. The design requires a dynamic alternative in order to build an independent grid that can also work in cooperation with other micro-grids and the power grid in an integrated way. Smart-grids present several advantages over the traditional power grid scheme, but the economic costs of the components required to implement smart-grids is currently a great limitation. This …

Methods That Support The Validation Of Agent-Based Models: An Overview And Discussion, Andrew Collins, Matthew Koehler, Christopher Lynch

Engineering Management & Systems Engineering Faculty Publications

Validation is the process of determining if a model adequately represents the system under study for the model’s intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models’ mechanisms and in the real-world situations they …

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

Anonymous Attribute-Based Broadcast Encryption With Hidden Multiple Access Structures, Tran Viet Xuan Phuong

School of Cybersecurity Faculty Publications

Due to the high demands of data communication, the broadcasting system streams the data daily. This service not only sends out the message to the correct participant but also respects the security of the identity user. In addition, when delivered, all the information must be protected for the party who employs the broadcasting service. Currently, Attribute-Based Broadcast Encryption (ABBE) is useful to apply for the broadcasting service. (ABBE) is a combination of Attribute-Based Encryption (ABE) and Broadcast Encryption (BE), which allows a broadcaster (or encrypter) to broadcast an encrypted message, including a predefined user set and specified access policy to …

Investigation And Implementation Of Miniaturized Microwave System For Linear Array Antenna Loaded With Omega Structures Planar Array, Ahmed F. Miligy, Fatma Taher, Mohamed Fathy Abo Sree, Sara Yehia Abdel Fatah, Thamer Alghamdi, Moath Alathbah

All Works

This paper investigates and implements a miniaturized microwave system for microstrip linear array antenna that operates in X-band (10.1 GHz), S-band (3.4 GHz) and C-band (5.6 GHz). The microwave system consists of three parts: a power divider, a directional coupler, and a matching network stub. These systems feed a linear array (16 elements) of patch antennas loaded with resonance planar omega structures array (160 elements) distributed in both patch (64 elements) and ground (96 elements) as the metamaterial structures for miniaturization purpose. The 1-to-2 divider feeds two directional couplers that act as phase shifters. The couplers fed a set of …

A Framework-Based Cross-Institutional Cpd For Academic Staff In Gen-Ai Literacy, Critical Inquiry And Authentic Assessment, Roisin Donnelly, Ita Kennelly

Books/Book Chapters

Generative-Artificial Intelligence (Gen-AI) has emerged as a transformative force profoundly influencing, if not revolutionizing the way we now teach and how students learn in higher education (HE). Despite the initial flurry of early research studies following the raising of public awareness of Gen-AI (and in particular ChatGPT), enduring pragmatic questions remain for academic staff on how best to protect and promote student learning, how to meaningfully support assessment integrity from a curriculum perspective, and additionally how to effectively use Gen-AI technologies to aid learning and foster deeper critical thinking.

Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth

Publications

Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …

Classification Of Sow Postures Using Convolutional Neural Network And Depth Images, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Yeyin Shi

Department of Biological Systems Engineering: Papers and Publications

The United States swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …

Passive Physical Layer Distinct Native Attribute Cyber Security Monitor, Christopher M. Rondeau, Michael A. Temple, Juan Lopez Jr, J. Addison Betances

AFIT Patents

A method for cyber security monitor includes monitoring a network interface that is input-only configured to surreptitiously and covertly receive bit-level, physical layer communication between networked control and sensor field devices. During a training mode, a baseline distinct native attribute (DNA) fingerprint is generated for each networked field device. During a protection mode, a current DNA fingerprint is generated for each networked field device. The current DNA fingerprint is compared to the baseline DNA fingerprint for each networked field device. In response to detect at least one of RAA and PAA based on a change in the current DNA fingerprint …

Terahertz Permittivity Parameters Of Monoclinic Single Crystal Lutetium Oxyorthosilicate, Sean Knight, Steffen Richter, Alexis Papamichail, Megan Stokey, Rafał Korlacki, Vallery Stanishev, Philipp Kühne, Mathias Schubert, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

The anisotropic permittivity parameters of monoclinic single crystal lutetium oxyorthosilicate, Lu₂SiO₅ (LSO), have been determined in the terahertz spectral range. Using terahertz generalized spectroscopic ellipsometry (THz-GSE), we obtained the THz permittivities along the a, b, and c^⋆ crystal directions, which correspond to the ε_a; ε_b, and ε_c? on-diagonal tensor elements. The associated off diagonal tensor element ε_ac? was also determined experimentally, which is required to describe LSO’s optical response in the monoclinic a–c crystallographic plane. From the four tensor elements obtained in the model fit, we calculate the …

Survey Of Transfer Learning Approaches In The Machine Learning Of Digital Health Sensing Data, Lina Chato, Emma Regentova

Electrical & Computer Engineering Faculty Research

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these …

Data Science And The Ethics Of Private Information, Faria R. Promi

Publications and Research

This research focuses on the moral questions linked to modern data technologies like Big Data and the Internet of Things. These technologies can be, but they are also about privacy and keeping data secure. We've looked at what experts say about these concerns and discovered that most agree we need to safeguard people's privacy and data. Our research encourages everyone to use these technologies, finding a balance between technological progress and responsible, ethical behavior. This way, we can enjoy the benefits of these technologies while also respecting individuals' privacy and data rights. The study reviews what experts have said about …