Physical Sciences and Mathematics Commons^™

Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang

Journal of Scientific Information Research

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …

Spatiotemporal Evolution Of Surface Water Quality And Driving Factors Across Varying Levels Of Human Interference In A Major Subbasin Of The Yellow River Basin, China, Longmei Xie, Ruizhong Gao, Xixi Wang, Limin Duan, Lijing Fang, Hui Tong, Chang Yue, Tingxi Liu

Civil & Environmental Engineering Faculty Publications

Study region

The Dahei River Basin, a sub-basin of the Yellow River Basin, was selected for this study. Encompassing population centers and villages in Inner Mongolia, it supports vital ecosystems and underscores the need to address its unique challenges in water management and protection.

Study focus

This study examines surface water quality dynamics in the Dahei River Basin, a key aspect of sustainable water management. Predicting spatiotemporal variations and driving factors remains challenging. Using 2013–2023 datasets on water quality, hydrology, and meteorology, the composite pollution index (CPI), ANOVA, and generalized additive models were applied to provide insights for effective management. …

Wildful (2024) By Kengo Kurimoto, Maughn Gregory

Middle Grades Books

Kengo Kurimoto’s graphic novel Wildful explores the transformative power of nature and deep attention. Poppy is struggling with her mother’s depression after her grandmother’s passing. Initially absorbed in digital distractions, Poppy is drawn into the wilderness when her dog, Pepper, chases a fox. She meets Rob, a boy who teaches her to observe nature with curiosity and respect—tracking animals, noticing patterns, and sitting in silence. Poppy learns that being in nature can assuage generational grief through a renewed sense of wonder and connection.

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

Examining The Presence And Effects Of Coherence And Fragmentation In The Gulf Of Maine Fishery Management Network, Derek A. Katznelson, Antonia Sohns, Dongkyu Kim, Evelyn Roozee, William Donner, Andrew M. Song, Jasper R. De Vries, Owen Temby, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Natural resource management networks cohere due to mutual dependencies and fragment, in part, due to the perceived risks of interaction. However, research on these networks has tended to accept coherence a priori rather than problematizing dependence, and few studies exist on interorganizational risk perception. This article presents the results of a study operationalizing these concepts and measuring the distribution of three types of dependence (capital, legitimacy, and regulatory) and two types of perceived risk (performance and sanction) among nearly fifty stakeholder groups and organizations participating in the management of fisheries in the binational Gulf of Maine. The analysis reveals an …

The Chemical Nature Of Colors, Bryant Corbin, Bryant Corbin

Longwood Senior Thesis Proposal

No abstract provided.

Towards Robust Multimodal Land Use Classification: A Convolutional Embedded Transformer, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ulhaq, David Blake, Naeem Janjua

Research outputs 2022 to 2026

Multisource remote sensing data has gained significant attention in land use classification. However, effectively extracting both local and global features from various modalities and fusing them to leverage their complementary information remains a substantial challenge. In this paper, we address this by exploring the use of transformers for simultaneous local and global feature extraction while enabling cross-modality learning to improve the integration of complementary information from HSI and LiDAR data modalities. We propose a spatial feature enhancer module (SFEM) that efficiently captures features across spectral bands while preserving spatial integrity for downstream learning tasks. Building on this, we introduce a …

Panoscu: A Simulation-Based Dataset For Panoramic Indoor Scene Understanding, Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn

Research outputs 2022 to 2026

Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions—making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene Change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU …

Robust Trend Estimation From Temporally Irregular Recreational Fisheries Surveys: A Panel Modeling Framework For Sparse Time Series, Ebenezer Afrifa-Yamoah, S. M. Taylor, Ute A. Mueller

Research outputs 2022 to 2026

Monitoring of recreational fisheries faces ongoing challenges due to irregular data collection and sampling gaps. We used a robust statistical approach combining cross-sectional panel modeling with generalized linear models to analyze discontinuous time series data. We modeled recreational boating patterns across four distinct Western Australian locations using camera monitoring data (2011–2014), and integrated weather conditions and temporal factors to improve trend estimation. Environmental conditions were strongly related to boating activity, particularly wind effects of distinct north–south patterns. Recreational activity decreased at northern locations during easterly winds and at southern locations during northerly winds. Cross-validation demonstrated how environmental factors can be …

A Mathematical Model On The Temporal Dynamics Of Aviation Competitive Pricing, Tichaona Chikore,, Farai Nyabadza,

Journal of Aviation/Aerospace Education & Research

This study investigates the competitive dynamics of airport pricing using U.S. airport data to validate the findings. It employs linear and nonlinear ordinary differential equation models to analyze the influence of competitive interactions and internal factors on pricing decisions. The methodology involves parameter estimation via optimization techniques and quantile regression to capture heterogeneity across market segments. Mathematical analysis and simulation results show that if competitive coupling coefficients are low then there is weak competitive influence on pricing, with airports’ pricing largely driven by internal factors. Also, if the adjustment rates exhibit consistency across airports then internal dynamics are dominant in …

Generalizing Classification Of Pilot Workload: Transfer Learning Versus A Jepa-Inspired Transformer Architecture, Naim Barnett, Shivani Nagrecha, Morgan Glover, Clayton Harper, Justin Wilson, James Maher, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

Within the context of learning, there poses difficulty when objectively measuring human performance. In this work, we investigate the evaluation of human performance via its relation to the individual's mental capacity by classification of cognitive load within the domain of aviation. By utilizing a mixed virtual and physical flight simulation environment in conjunction with biometric sensing, we create and evaluate the predictive capabilities of a Joint-Embedding Predictive Architecture (JEPA) and compare the architecture and results to traditional methods for transfer learning and domain adaptation. We find that our JEPA inspired architecture can achieve more than 70% accuracy of cognitive workload, …

Gamified Mhealth System For Evaluating Upper Limb Motor Performance In Children: Cross-Sectional Feasibility Study, Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

Computer Science Faculty Research and Publications

Background: Approximately 17% of children in the United States have been diagnosed with a developmental or neurological disorder that affects upper limb (UL) movements needed for completing activities of daily living. Gold-standard laboratory assessments of the UL are objective and precise but may not be portable, while clinical assessments can be time-intensive. We developed MoEvGame, a mobile health (mHealth) gamification software system for the iPad, as a potential advanced technology to assess UL motor functions.

Objective: This feasibility study examines whether MoEvGame can assess children’s whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination. The specific aims were to …

A Comparative Analysis Of Hedging And Safe-Haven Properties Of Cryptocurrencies And Commodities, Tari M. Karimo, Ochoche Abraham

CBN Journal of Applied Statistics (JAS)

No abstract provided.

Bayesian Networks For Safety-Critical Systems, Joseph Mietkiewicz

Theses

This thesis addresses a operational challenge in modern industrial operations: the increasing complexity of systems and the consequent cognitive burden on operators. As industrial technologies advance, the human-computer interface has become the primary conduit for information flow, playing a pivotal role in operational decision-making. However, the proliferation of data often leads to information overload, potentially compromising rather than enhancing operator performance. This research explores an approach to this pressing issue through the application of Bayesian networks as decision support systems in safety- critical scenarios. Our study employs a multi-faceted approach, combining theoretical modeling with empirical testing. Through collaboration with industry …

The Reliability Response To Patent Law’S Ai Challenges, Arti K. Rai

Faculty Scholarship

Pervasive AI use adds newfound importance to longstanding debates over patent timing and reliability. Patent claims on speculative ideas generated by AI, or even the infusion of speculative AI-generated ideas into the public domain, may defeat patent incentives for more careful research. Although challenges that AI use poses for patent validity requirements like human inventorship and nonobviousness have received more attention, reliability is equally important.

Indeed, as this Article argues, the issues are linked. If requirements for inventorship and nonobviousness were adjusted to emphasize reliability, a human role could be preserved, and AI use would not necessarily threaten patents. Currently, …

Embscu, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. …

Saom, Mariia Khan

Research Datasets

The SAOM dataset is created for the evaluation of the whole-object semantic segmentation in embodied AI indoor environments. The SAOM dataset is tailored for segmentation in dynamic embodied environments, focusing on interactable objects. It includes 54 object classes, all of which are either `pickupable’, `openable’, or `receptacles`. Unlike static-object datasets, the objects in SAOM can undergo transformations, such as being opened, closed, or moved.

M3t, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

For embodied agents, such as robots, tracking objects in their surroundings through visual observation is essential — a task, referred to as Visual Object Tracking (VOT). For instance, during a rearrangement task, a robot may need to track objects, as part of the scene change understanding process, to accurately restore them to their original states. Classic Multiple Object Tracking (MOT) datasets typically focus on tracking moving, single-class object instances in a video from a fixed viewpoint, limiting their applicability to embodied AI tasks. In embodied AI tasks, objects belong to multiple classes, are often static, and are observed from continuously …

Experimental Investigation On Hydrogen-Rich Syngas Production Via Gasification Of Common Wood Pellet In Bangladesh: Optimization, Mathematical Modeling, And Techno-Econo-Environmental Feasibility Studies, Md Sanowar Hossain, Mujahidul Islam Riad, Showmitro Bhowmik, Barun K. Das

Research outputs 2022 to 2026

Since hydrogen produces no emissions, there is increasing interest in its production throughout the world as the need for clean and sustainable energy grows. Bangladesh has an abundance of biomass, particularly wood pellets, which presents a huge opportunity for gasification to produce hydrogen. Gasification of mahogany (Swietenia mahagoni-SM) and mango (Mangifera indica-MI) wood is performed in a downdraft gasifier to evaluate the impact of particle size, equivalence ratio, and temperature on hydrogen gas composition and gasifier performance. Under the optimal conditions determined by central composite design-response surface methodology (CCD-RSM) optimization, gasification of SM and MI wood can greatly increase hydrogen …

A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani

Mesopotamian Journal of Computer Science

Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection …

Anila: Adaptive Neuro-Inspired Learning Algorithm For Efficient Machine Learning, Ai Optimization, And Healthcare Enhancement, Ismael Khaleel, Wijdan Noaman Marzoog, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data …

Enhancing Cybersecurity In Smart Education With Deep Learning And Computer Vision: A Survey, Guma Ali, Aziku Samuel, Maad M. Mijwil, Kholoud Al-Mahzoum, Malik Sallam, Ioannis Adamopoulos, Ayodeji Olalekan Salau, Indu Bala, Klodian Dhoska, Engin Melekoglu

Mesopotamian Journal of Computer Science

The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, …

Self-Attention Enhanced Dual Bigru For Arabic Fake News Detection, Baqer M. Merzah, Jafar Razmara, Jaber Karimpour

Mesopotamian Journal of Computer Science

The rapid proliferation of social media platforms has greatly amplified the dissemination of fake news, representing significant obstacles to public trust and evidence-based decision-making, particularly for the Arabic-speaking population. Meeting the challenge of Arabic fake news detection is a problem compounded by the complex morphological nature of the language, as well as limited resources. This study presents a hybrid deep learning framework that integrates two Bidirectional Gated Recurrent Units (BiGRUs) along with an attention mechanism for efficiently detecting misinformation in Arabic news. The method leverages FastText word embeddings for disambiguating the intricate semantics of the Arabic language. The model is …

Roi – Enhancing Detection Of Citrus Disease Based On Yolov10, Raya N. Ismail, Armaneesa Naaman Hasoon, Israa Rafaa Abdulqader, Salwa Khalid Abdulateef

Mesopotamian Journal of Computer Science

One of the most important fruit crops in the world is citrus. However, some citrus diseases spread rapidly, which is why early detection at an accurate stage is important for timely intervention. YOLO-based object detection models, such as the latest YOLOv10, where small lesions are difficult to identify among noisy backgrounds, have recently been developed, yet their accuracy tends to degrade. Therefore, we proposed a citrus disease detection model by integrating the region of interest (ROI) for object segmentation with the YOLOv10 model, thus addressing the issues of low detection accuracy and slow inference time. The proposed model was trained …

End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab

Mesopotamian Journal of Computer Science

Automatic License Plate Recognition (ALPR), DEtection TRansformer (DETR), Deep Learning for Object Detection, Optical Character Recognition (OCR), Region-Specific Vehicle Identification

Dgen: A Dynamic Generative Encryption Network For Adaptive And Secure Image Processing, Mohammed Rajih Jassim, Qusay M. Salih, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public …

A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri

Mesopotamian Journal of Computer Science

Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Assessing Iot Intrusion Detection Computational Costs When Using A Convolutional Neural Network, Mathew Nicho, Brian Cusack, Christopher D. Mcdermott, Shini Girija

All Works

IoT systems face vulnerabilities due to their data processing requirements and resource constraints. With 13 billion connected devices globally, this research investigates the economic viability of AI-based intrusion detection systems (IDSs), specifically analyzing the automation costs of implementing a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) for classifying malicious sensor traffic. This study introduces an innovative framework that evaluates six distinct architectural components of CNN and LSTM: image input processing, convolutional layer operations, max pooling layer functionality, fully connected layer characteristics, softmax output activation, and class determination mechanisms. The framework employs six metrics: matrix size, feature vector number, …

On The Validity Of Traditional Vulnerability Scoring Systems For Adversarial Attacks Against Llms, Atmane Ayoub Mansour Bahar, Ahmad Samer Wazan

All Works

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks on Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of different metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. Approach - This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by …