Digital Commons Network^™

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

News From The Front: How To Win The Ai War, Christine Haverington

Journal of the National Collegiate Honors Council Online Archive

While artificial intelligence is currently and justifiably a hot topic among scholars and university administrators, students are way ahead of the curve in terms of its use and application. Calling for educators to stop trying to catch AI “cheaters,” this essay provides evidence from honors and other classroom observations, student research on peer and faculty usage and attitudes, course evaluations, and external sources to demonstrate how and why generative AI can be creatively and effectively incorporated into teaching. Toward this end, practical pedagogical strategies are shared describing teaching modalities and innovative curricular design, avoiding the cognitive degradation of students, and …

Ragg: Retrieval-Augmented Grasp Generation Model, Zhenhua Tang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

Intent-based grasp generation inherently involves challenges such as manipulation ambiguity and modality gaps. To address these, we propose a novel Retrieval-Augmented Grasp Generation model (RAGG). Our key insight is that when humans manipulate new objects, they initially mimic the interaction patterns observed in similar objects, then progressively adjust hand-object contact. Consequently, we develop RAGG as a two-stage approach, encompassing retrieval-guided generation and structurally stable grasp refinement. In the first stage, we propose a Retrieval-Augmented Diffusion Model (ReDim), which identifies the most relevant interaction instance from a knowledge base to explicitly guide grasp generation, thereby mitigating ambiguity and bridging modality gaps …

Design And Simulation Evaluation Of Converged Toll Collection Schemes For Senegalese Highways, Lai Zengcheng, Zhang Ning, Yang Shichun, Ding Xueqi, Ji Yanjie

Journal of China & Foreign Highway

The Senegalese highway toll collection system is inefficient,and significant congestion and delays frequently occur at toll stations in the face of sudden traffic surges during holidays.This paper examined the limitations of the existing toll collection methods on Senegalese highways and combined the image detection algorithm and electronic license plate recognition technology with the existing MTC and ETC toll collection methods to construct a converged toll collection method based on vehicle information recognition.The paper aimed to optimize the Senegalese highway toll collection system,reduce its cost of use,and improve the efficiency of toll collection.The BPV Thiès toll station in Senegal was selected …

A Dehazing And Enhancement Algorithm For Heterogeneous Images Of Underground Mining Environments In Coal Mines, Zhang Xuhui, Xie Yanbin, Yang Wenjuan, Zhang Chao, Wan Jicheng, Dong Zheng, Wang Yanqun, Jiang Jie, Li Long

Coal Geology & Exploration

Objective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heterogeneous images of underground mining environments. Methods Initially, hazy images were segmented into zones with different brightness values, for which the average ambient light intensity of global dark channels was calculated. The calculation results were integrated through weighting with the ambient light of local bright channels, which was obtained using adaptive gamma correction and …

Using Satellite Image Segmentation To Detect Trails, Jeremy Reynolds

Theses and Dissertations

This masters thesis proposes an innovative approach to satellite image segmentation by focusing on the detection and mapping of walking, hiking, and biking trails. The motivation behind this project comes from the underexplored area in segmentation techniques for trail identification and offers potential benefits for urban planning, environmental monitoring, and public health. The problem statement addresses the need for a model that can differentiate between various trail types and other natural or man-made elements. The project aims for efficiency and scalability in processing satellite imagery across different compute hardware. The work details several stages: researching existing segmentation techniques, specifically road …

Performance Of Standard Medical Mllms On Ecg Image Data, Prisha Anil

Masters Theses

This work presents a structured benchmarking study of multimodal large language models (MLLMs) applied to electrocardiogram (ECG) interpretation tasks. We evaluate three representative architectures: MedGemma, HuatuoGPT-Vision, and LLaVA-Med, across progressive experimental stages involving text-only structured prompt normalization, text–image fusion with ECG plots, and full multimodal fusion incorporating time-series signals. A standardized five-section cardiology prompt was designed to enforce consistent output structure and SCP-code alignment, enabling reproducible metric computation across models. Quantitative evaluation using BERTScore, token-level F1, and diagnostic accuracy demonstrates that HuatuoGPT-Vision achieves the highest semantic and diagnostic alignment, while MedGemma exhibits superior formatting stability and reproducibility. In contrast, LLaVA-Med …

Enhancing Uk Electricity Price Forecasting Using Deep Learning., Stephen Cooke

ICT

Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.

New Insights Into Decapod Chemical Communication: A Focus On The Hydrothermal Vent Crab Xenograpsus Testudinatus, Jishnu Panamoly Ayyappan, Mark June S. Consigna, Li-Chun Tseng, Jiang-Shiou Hwang

Journal of Marine Science and Technology–Taiwan

Chemical communication in decapod crustaceans has been extensively studied for over 150 years, with most of the research focusing on sex pheromones. These crustaceans inhabit chemically complex environments and rely on diverse chemical signals for essential behaviors such as mate recognition, predator avoidance, and social interaction. This review synthesizes key findings from studies on shrimp, crayfish, lobsters, and crabs, the most well-documented taxa in crustacean chemical ecology. Various appendages, including the first and second antennae, mouthparts, and walking legs, are involved in chemosensory detection. Special emphasis is placed on chemical communication in extreme environments such as hydrothermal vents (HVs), with …

Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Impact Of Color, Shape, And Typeface On Visual Attention: An Eye Tracking Study On Brand Logo, Suzayana Rosidah, Fransiskus Xaverius Ivan, Suatmi Murnani, Hafzatin Nurlatifa, Kristian Adi Nugraha, Sunu Wibirama

ASEAN Journal on Science and Technology for Development

In numerous cases, companies undertake logo redesigns to enhance brand perception. However, little attention has been paid to the impact of each element of the redesigned logo on visual attention and brand perception. To address this research gap, we collected data from eye tracking and self-report questionnaires of 30 participants during exposure to the old and new logos of a prominent bookstore in Indonesia. The results of the questionnaires revealed a significant relationship between the responses concerning color, shape, typeface, and those pertaining to visual attention (p < 0.05). Most participants were able to grasp the value of creativity, flexibility, progress, change, and strength in the new logo shape. The results of eye tracking show that color was the most influential factor that attracted visual attention in old (F(1.5, 43.4) = 14.905, p < 0.05) and new logos (F(1.7, 50) = 34.757, p < 0.05). This study suggests that companies should selectively choose a color scheme of a logo that better attracts the attention of consumers. In addition, our finding is promising as a practical guide for similar research, as well as a case study on how logo redesign affects brand perception and visual attention.

A Dynamically Adapting Forecast Cone Based On Ensemble Spread, Michael N. Barletta

Electronic Theses & Dissertations (2024 - present)

Dynamically based ensemble prediction systems have gained considerable attention because they can provide a greater range of possible forecast outcomes and quantify the uncertainty in forecasts. In turn, forecasters can convey clearer messages to the public on the range of forecast scenarios and display inherent uncertainty in weather forecasts, which can be difficult to do with deterministic forecasts. Although global ensemble prediction systems have demonstrated skill in their probabilistic track predictions, the information contained within them is not always fully utilized beyond the mean forecast and standard deviation (i.e., spread). One way that these ensembles could be better employed is …

Explainable Ai In Medical Imaging: An Interdisciplinary Translational Approach, Caitlyn Chavez

Computational and Data Sciences (PhD) Dissertations

Advances in computer vision and image processing have made a clear impact on many fields, from healthcare diagnostics to autonomous driving. However, as these models become more complex, understanding their decision-making processes has grown increasingly challenging, making explainable AI (XAI) a crucial component of modern AI systems. The focus of this work is to integrate these new technologies alongside foundational methods of image processing to create tools that can be used by domain experts who are not programmers. Prior to delving into the projects which investigate these concepts, the methodologies, background, and the overall frameworks are discussed. In the first …

Unlocking Potential: Analyzing The Content, Style, Structure, And Interactivity Of Mesonets As Operational Dashboards, Savannah Olivas, Jeannette Sutton, Michele K. Olson

Emergency Preparedness, Homeland Security, and Cybersecurity Faculty Scholarship

Emergency managers need data and information to make life-saving decisions on behalf of the public. Operational dashboards, if designed appropriately, can provide this information in a central location and reduce cognitive demands during decision-making. Mesonet websites can serve as a type of operational dashboard that has the potential to provide the meteorological data necessary for emergency managers to make decisions. In this study, we use quantitative content analysis to examine the content, style, structure, and interactivity of 18 Mesonet websites from across the contiguous United States. We find that Mesonet websites vary in the type and amount of content they …

13th International Conference On Business, Technology And Innovation 2024, University For Business And Technology - Ubt

UBT International Conference

Welcome to IC – UBT 2024

UBT Annual International Conference is the 13th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make …

"The Words We Do Not Yet Have." A Creative Inquiry Into Human-Plant Relationships, Maliheh Ghajargar

Art Faculty Articles and Research

Climate change, loss of plant biodiversity, and ocean pollution signal the drastic changes in our ecology that call us to attend to the needs of more than human forms of life on Earth. Sustainable design and HCI research are responding to this call by offering methods and approaches to design more sustainable products and systems and recently, more than human design is building momentum. This agenda seeks to reform traditional design processes by decentering the creative agency of the dominant socio-economical group of humans and foregrounding those of diverse Others. In this paper, I focus on plants as a nonhuman …

Collaborative Practices In Virtual Group Work On Dynamic Geometry Tasks, Younggon Bae, V. Rani Satyam, Zareen G. Aga

School of Mathematical & Statistical Sciences Faculty Publications

The goal of this study is to explore productive ways to engage students in groupwork using dynamic geometry tasks in online synchronous classroom environments. In particular, we aim to understand the social, mathematical, and technological aspects of student collaboration in virtual spaces. We analyzed how three online groups of students collaboratively worked on dynamic geometry tasks of exploring interactive kaleidoscope applets in Desmos and producing visual representations and written descriptions of geometric transformations used in the applets. The students shared their screens in Zoom as they shared their findings and discussed how to draw and write to represent the kaleidoscopes. …

Section Point Cloud Denoising Method Based On Enhanced Dbscan And Distance Consensus Evaluation, Chengpeng Ge, Dong Zhao, Rui Wang, Qinghua Ma

Journal of System Simulation

Abstract: A denoising method based on the improved DBSCAN(density-based spatial clustering of applications with noise) algorithm is proposed to address the problem of removing noise points in point cloud data. The statistical filtering method is applied to pre-screen isolated outliers and remove largescale noise from the point cloud. The DBSCAN algorithm is optimized to reduce computational time complexity and achieve adaptive parameter adjustment, thereby dividing the point cloud into normal clusters, suspected clusters and abnormal clusters, and immediately removing abnormal clusters. Distance consensus assessment is applied, and suspect clusters are further evaluated. By calculating the distance between the suspected point …

Fusing Rotation Angle Coding In Spherical Space For Human Action Recognition, Benyue Su, Bangguo Zhu, Mengjuan Guo, Min Sheng

Journal of System Simulation

Abstract: The existing human action recognition methods focus more on the translation information such as the coordinates and displacements of skeleton structure, and pay less attention to the motion trend of skeleton structure and the rotation information representing the motion direction of joints and bones. A spatio-temporal convolutional neural network method combining the rotation angle coding in spherical space is introduced. The angle information with scale invariance is obtained by mapping the human action in three-dimensional spherical space, and the dynamic angular velocity information is extracted as the angle code to represent the rotation information of joints and bones in …

Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali

Journal of Soft Computing and Computer Applications

Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …

Hidden Scientists/Hidden Creatives, Con Kennedy

Conference Presentations

This presentation discussed how the UN Sustainable Development Goals (SDGs) were integrated into an existing graphic design module delivered on the BA in Visual Communication Design in a joint project with the TU Dublin School of Art and Design, School of Biological, Health and Sports Sciences
 Sustainability and Research Hub
Research, the Ethics and Integrity Office. The project explored underrepresented women in science and graphic design.

D3still : Decoupled Differential Distillation For Asymmetric Image Retrieval, Yi Xie, Yihong Lin, Wenjie Cai, Xuemiao Xu, Huaidong Zhang, Yong Du, Shengfeng He

Research Collection School Of Computing and Information Systems

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these oneto-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is …

"I Feel Like He's Looking In The Computer World To Be Social, But I Can't Trust His Judgement": Reimagining Parental Control For Children With Asd, Prakriti Dumaru, Bryson D. Hackler, Audrey Flood, Mahdi Nasrullah Al-Ameen

Computer Science Faculty and Staff Publications

Children with Autism Spectrum Disorder (ASD) often seek comfort from devices (e.g., smartphones) to deal with social overstimulation. However, such reliance exposes them to inappropriate digital content and increases susceptibility to mimicry and social vulnerability. Thus, parents having children with ASD encounter unique challenges in regulating their device usage, which are little addressed in the existing literature on parental mediation. As we begin to address this gap, we designed low-fidelity prototypes centered around open communication and self-regulation, which we refined based on the feedback from six ASD experts in two focus groups. We evaluated updated designs (presented in the form …

Key Benefits Of Small Group Instruction For Diverse Learners, Lydia Mcevoy

Master's Theses

Utilizing a mixed method approach this research study investigated the effects of small group instruction on the learning of diverse learners. Informed by a preliminary literature review that supports the use of small-group instruction, the researcher conducted a small-scale action research project to focus on three diverse learners in a 1st-grade classroom over four weeks. One of the findings of this project shows that small group instruction helps promote social and emotional skills as students feel more comfortable interacting with peers in a small group rather than in a whole group. Another finding indicates that students feel more encouraged by …

The Water That Will Be: A Qualitative Look Into The Effects Of Visual Imagery On Participation In Water Conservation Initiatives, Shane Martin White

Graduate Theses and Dissertations

This dissertation explored the role of visual imagery in water conservation initiatives, focusing on its impact on public understanding, emotional connection, and willingness to participate. Drawing from the Theory of Planned Behavior and Affective Disposition Theory, the study investigated how art interventions can enhance comprehension and engagement with water conservation easements. Utilizing qualitative methods, including interviews, focus groups and PhotoVoice, data was collected from stakeholders in the Illinois River Watershed region. The research examined current levels of understanding regarding conservation easements, evaluated the effectiveness of visual imagery interventions in enhancing public awareness, and identified the potential for collaborative approaches between …

Automated Cinematographer For Vr Viewing Experiences, Zihan Wu

Dartmouth College Master’s Theses

As the virtual reality (VR) industry continues to evolve, the question of how to effectively capture VR experiences for an audience remains a challenge. The predominant method of showcasing VR applications through first-person recordings lacks cinematic interest, failing to capture other viewpoints and the essence of the moment. Meanwhile, manually setting up cameras and editing videos requires technical expertise on behalf of the user. In this paper, we propose the use of machine learning (ML) to automatically select the most compelling predefined viewpoint in a VR environment, at any given moment. Our models, trained on actor motion and voice volume, …

Emojis And Miscommunication In Text-Based Interactions Among Nigerian Youths, Uduak Udoudom, Godwin William, Anthony Igiri, Ememobong Okon, Kalita Aruku

Journal of Informatics and Web Engineering

This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and …

The Problem Of Communication Of The Sign And Symbol In The Design Of The Brand In Society: The Differences Resulting From Forgery And Imitation (Analytical Study), Sattar Al-Juboori

Middle East Journal of Communication Studies

The research problem focused on studying the consequences of communicating through signs and symbols in the context of the current world, given the huge amount of diverse industrial and commercial products and the challenges related to distinguishing those products. The research aims to find out the problem of perception of the sign and symbol in the designs of logos and trademarks for the consumer, the role that the sign or symbol plays in the symbolic representation of the consumer, and the desires and motivations that the brand raises for the consumer in the purchasing process. The research community is limited …

Progression Magazine, 2023-2024, Coastal Carolina University

Progression Magazine

Magazine of the Gupta College of Science at Coastal Carolina University.