Digital Commons Network^™

An Empirical Study To Evaluate Aigc Detectors On Code Content, Jian Wang, Shangqing Liu, Xiaofei Xie, Yi Li

Research Collection School Of Computing and Information Systems

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with Large Language Models (LLMs), like ChatGPT, emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of LLMs, especially in security and safetycritical domains, such as academic integrity and answering questions on Stack Overflow, poses significant concerns. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by LLMs remains unexplored. To fill this gap, in this paper, we present an empirical study evaluating …

Risurconv : Rotation Invariant Surface Attention-Augmented Convolutions For 3d Point Cloud Classification And Segmentation, Zhiyuan Zhang, Licheng Yang, Xiang Zhiyu

Research Collection School Of Computing and Information Systems

Despite the progress on 3D point cloud deep learning, most prior works focus on learning features that are invariant to translation and point permutation, and very limited efforts have been devoted for rotation invariant property. Several recent studies achieve rotation invariance at the cost of lower accuracies. In this work, we close this gap by proposing a novel yet effective rotation invariant architecture for 3D point cloud classification and segmentation. Instead of traditional pointwise operations, we construct local triangle surfaces to capture more detailed surface structure, based on which we can extract highly expressive rotation invariant surface properties which are …

Collaborative Cross-Modal Fusion With Large Language Model For Recommendation, Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang

Research Collection School Of Computing and Information Systems

Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, …

Zero-Shot Object Counting With Good Exemplars, Huilin Zhu, Jingling Yuan, Zhengwei Yang, Yu Guo, Zheng Wang, Xian Zhong, Shengfeng He

Research Collection School Of Computing and Information Systems

Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lies in their inability to identify high-quality exemplars effectively. This deficiency hampers scalability across diverse classes and undermines the development of strong visual associations between the identified classes and image content. To this end, we propose the Visual Association-based Zero-shot Object Counting (VA-Count) framework. VACount consists of an Exemplar Enhancement Module (EEM) and a Noise Suppression Module (NSM) that synergistically refine the process of class exemplar identification …

Documenting Ethical Considerations In Open Source Ai Models, Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, Marc Cheong

Research Collection School Of Computing and Information Systems

Background: The development of AI-enabled software heavily depends on AI model documentation, such as model cards, due to different domain expertise between software engineers and model developers. From an ethical standpoint, AI model documentation conveys critical information on ethical considerations along with mitigation strategies for downstream developers to ensure the delivery of ethically compliant software. However, knowledge on such documentation practice remains scarce. Aims: The objective of our study is to investigate how developers document ethical aspects of open source AI models in practice, aiming at providing recommendations for future documentation endeavours. Method: We selected three sources of documentation on …

Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui

Research Collection School Of Computing and Information Systems

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time …

Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This article presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces …

Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, …

Robust And Generalizable Representations In The Hippocampus, Hung-Tu Chen

Dartmouth College Ph.D Dissertations

An intelligent system must balance generalizing across similar experiences with maintaining the distinctiveness of each experience. This thesis explores how the hippocampus manages this balance through its neural representations to support adaptive behavior. In Chapter 1, I provide an overview of key hippocampal phenomena that contribute to this process, including remapping, splitter signal, and replay. In Chapter 2, I challenge the concept of random remapping by showing that it is possible to predict, better than chance, how a given experience will be encoded in the hippocampus across different subjects. This suggests that encoding of related experiences, which was previously thought …

Privacy Risks And Regulatory Challenges In Smart Grids And Renewable Energy Systems, Mikołaj Rajca

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Smart grid technologies are central to the global shift towards a more efficient and sustainable energy infrastructure, integrating advanced digital systems with traditional power networks. While these technologies offer significant benefits, including enhanced energy management and the seamless integration of renewable energy sources, they also introduce complex privacy challenges. The extensive data collection and real-time communication capabilities inherent in smart grids raise concerns over consumer privacy, data breaches, and cybersecurity threats. This paper critically examines these privacy risks within the context of evolving regulatory frameworks such as the GDPR, NIS2 Directive, and the forthcoming EU AI Act. The discussion emphasizes …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

Architecting Standards: Leveraging Ia To Drive Emerging Technology Standards​, Noreen Y. Whysel

Publications and Research

In this talk for the annual meeting of the W3C Information Architecture Community Group, I discuss how standards bodies can learn from the Information Architecture field to improve accessibility and usability. By adopting user-centered design principles and engaging in iterative feedback loops, standards organizations can create more relevant and practical standards that align with real-world applications. Emphasizing collaboration with industry experts and end-users will help ensure that standards evolve along with technological advancements.

In the meantime, key standards are examined, such as ISO 9241, which provides guidelines for usability and user-centered design, and the W3C's Web Content Accessibility Guidelines (WCAG), …

The Limits Of Generative Ai In Administrative Law Research, Susan Azyndar

Journal Articles

The author recounts an administrative law classroom experience using generative AI. She considers the complexities of administrative law, AI training, professional responsibility, and traditional resources.

Revolutionizing Public Safety And Criminal Justice Through Ai, Alan Saquella

Publications

Artificial Intelligence (AI) is rapidly transforming public safety, criminal justice and security by fundamentally changing how crimes are committed, investigated and prevented. As AI tools become increasingly sophisticated, law enforcement and corporate security professionals are utilizing these advancements to enhance their capabilities. However, integrating AI into these sectors also brings significant challenges, including ethical concerns, recruitment difficulties, and the surge in crime rates. This article examines the transformative impact of AI, the ongoing efforts to unify AI applications across public safety and security sectors, and expert advice on overcoming the associated challenges.

Ai In The Health Professions, Heidi Monroe, Carrie Fry, Phillip Baker, Erika Busz

AI and the Future of Work

The aim of this track is to provide health professionals, and those interested in mental health and healthcare careers with an understanding of key aspects of AI use in healthcare. Participants will explore advantages of some recent AI developments and evaluate how they may be effectively leveraged to improve patient care, while addressing potential challenges, limitations, and ethical considerations.

Educating With Ai, Grace Seo, David Wicks

AI and the Future of Work

This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …

The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese

Undergraduate Research Symposium

A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …

Fostering Trust Through User Interface Design In Multi-Drone Search And Rescue, Johanna Ahlskog, Maria Theresa Bahodi, Artur Lugmayr, Timothy Merritt

Research outputs 2022 to 2026

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in search and rescue (SAR) missions, with pilots transitioning from manual control of single drones to more collaborative tasks orchestrating semi-autonomous fleets. Designing user interfaces to support UAV pilots effectively is crucial to improving the success of search missions. We developed two versions of a multi-drone SAR system prototype to simulate SAR missions and evaluated them with professional UAV SAR pilots in Sweden. Both versions showed the flight paths of the UAVs, yet in one version, a heatmap was overlayed to provide information from a lost person model. We evaluated situational …

Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia

Journal of System Simulation

Abstract: In order to solve the problem of low intelligence and digitization of the current mine hoisting system, a method based on DT for digital modeling, 3D visualization, and virtual real interaction of shaft hoisting machines is proposed. Aiming at the rockshaft hoist system, based on the digital twin five dimensional model framework, we analyze the operating mechanism of the equipment, and model the fully physical digital system of the rockshaft hoist. By constructing multidimensional multi-scale models and multidimensional heterogeneous data models, twin digital scenes are constructed, and virtual real mapping technology is combined to achieve dynamic mapping and virtual …

A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong

Journal of System Simulation

Abstract: Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions. Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals, a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed. Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution, which are converted into high-dimensional feature vectors; lightweight interaction of feature information flow is realized through time-frequency feature and spatiotemporal feature extraction modules; the feature enhancement fusion module fuses feature information to outputs sleep staging results. The results show that the model …

Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü

Journal of System Simulation

Abstract: The simulation test and evaluation of intelligent system of systems, systems and single equipment is a complex system engineering, which requires effective management of multi-source, heterogeneous and distributed massive simulation resources scattered in cloud test centers and test sites of various units; and good control of dynamically generated tasks, assumptions, configurations, results, evaluations and other data and files. The traditional way of managing and querying simulation resources by category is inefficient and difficult to meet the requirements of large-scale intelligent simulation test and evaluation activities. An overall framework for simulation resource management based on graph association organization, defines a …

Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy, Lixia Xu, Jilong Zhong, Shaoshi Wu, Yishan Ding, Xiaoyu Zhai, Shizhao Chen, Yizhe Wang, Xue Wen, Juanfang Zeng, Xinwen Hou

Journal of System Simulation

Abstract: In response to the problem of rare data samples in application experiment scenarios, this paper proposes an indicator transfer learning method based on cloud models and Maximum Mean Discrepancy (MMD), which transfers the indicator calculation model from typical simulation experiment scenarios to application experiment scenarios to meet the needs across platform and domain simulation evaluation. Using the maximum mean difference method to align the indicator distribution in the typical simulation experiment scenario to the indicator distribution in the application experiment scenario, thereby achieves indicator transfer, and by using cloud models based on a small number of examples for modeling …

Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization, Feixing Li, Lining Xing, Yu Zhou

Journal of System Simulation

Abstract: Machine learning typically mines underlying patterns and rules from data, making it susceptible to phenomena such as overfitting and underfitting, which in turn affects the generalization and robustness of learning models. This paper explores the potential fragility and instability of SVM from the perspective of adversarial simulation testing. The adversarial simulation strategy employed involves selectively contaminating training sample labels to simulate an attack on the SVM classifier, thereby degrading its performance and testing its dependency on training samples. To explore the ceiling of performance degradation of an SVM classifier under the combination attack of different samples, the contradictory objectives …

Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings, Qiankun Zhu, Jiwu Li, Yongfeng Du

Journal of System Simulation

Abstract: In order to improve the emergency evacuation capability of an old school building under fire scenarios, a fire evacuation model of an old school building is developed. The PyroSim software is used to build a fire dispersion model to simulate and analyse the changes of smoke visibility, temperature and CO at the safety exit of the fire floor in the school building under the condition of mechanical smoke exhaust, automatic sprinkler and whether the windows of the fire room are open or not. The simulation of the exit status and evacuation of people has been carried out in conjunction …

Research On Orb-Slam Algorithm Based On Windowed Matching Estimation, Wanye Yao, Zewei Pang, Peijie Sun, Zhu Wang

Journal of System Simulation

Abstract: To address unstability of location accuracy of ORB-SLAM system caused by randomness of camera pose solution method, an improved pose solution method based on feature point windowed matching and analytical ICP is proposed, and the mobile robot ORB-SLAM system is constructed. The extracted feature points are windowed to improve matching efficiency while ensuring good feature point matching, the analytical ICP algorithm is used to solve the camera pose for avoiding iteration, and the windowed pose solution with the smallest error is selected for bundle adjustment to reduce the pose errors caused by local information loss or mismatching. The results …

Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo

Journal of System Simulation

Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …

Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai

Journal of System Simulation

Abstract: Aiming at the problem that the inner space of underground pipeline cable is narrow and closed, which cannot be inspected by humans, and the existing pipeline robot cannot adapt to the special environment of pipeline cable, a miniaturized, compact pipeline cable inspection robot is designed. This robot is capable of operating within the underground pipeline where cables have already been laid to inspect the inner wall of the pipeline and the working condition of the cables. According to the requirements of the working conditions, the whole three-dimensional model of the robot has been established. The mapping relationship between the …

A New Model Predictive Current Controller Forac-Dc Matrix Converter In Unbalanced Grids, Wenlang Deng, Minghai Wu, Haipeng Xie, Yingjie Hu

Journal of System Simulation

Abstract: To reduce the fluctuation of active power on the grid side of AC-DC matrix converters under unbalanced input conditions and to address the issue of variable switching frequency in discrete model predictive control, this paper proposes a novel model predictive control method. This method selects effective vectors based on the phase angle of the grid current, thus avoiding the computational burden of evaluating the value function in traditional model predictive control. Additionally, a second-order extended complex Kalman filter is introduced, which achieves the computation accuracy of the secondorder term of the Taylor series expansion and enables the application of …