Computer Sciences Commons^™

Mvmoe: Multi-Task Vehicle Routing Solver With Mixture-Of-Experts, Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu

Research Collection School Of Computing and Information Systems

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes …

Is There A Space In Landslide Susceptibility Modelling: A Case Study Of Valtellina Valley, Northern Italy, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Landslides pose significant and ever-threatening risks to human life and infrastructure worldwide. Landslide susceptibility modelling is an emerging field of research seeking to determine contributing factors of these events. Yet, previous studies rarely explored the spatial variation of different landslide factors. Hence, this study aims to demonstrate the potential contribution of spatial nonstationarity in landslide susceptibility modelling using Global Logistic Regression (GLR) and Geographically Weighted Logistic Regression (GWLR). The second objective of this study is to demonstrate the important role of data preparation, data sampling, variable sensing, and variable selections in landslide susceptibility modelling. Using Valtellina Valley in Northern Italy …

On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open-source software (OSS) projects. In this regard, we first investigate …

Privacy-Preserving Arbitrary Geometric Range Query In Mobile Internet Of Vehicles, Yinbin Miao, Lin Song, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. …

A Computational Aesthetic Design Science Study On Online Video Based On Triple-Dimensional Multimodal Analysis, Zhangguang Kang, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Computational video aesthetic prediction refers to using models that automatically evaluate the features of videos to produce their aesthetic scores. Current video aesthetic prediction models are designed based on bimodal frameworks. To address their limitations, we developed the Triple-Dimensional Multimodal Temporal Video Aesthetic neural network (TMTVA-net) model. The Long Short-Term Memory (LSTM) forms the conceptual foundation for the design framework. In the multimodal transformer layer, we employed two distinct transformers: the multimodal transformer and the feature transformer, enabling the acquisition of modality-specific patterns and representational features uniquely adapted to each modality. The fusion layer has also been redesigned to compute …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …

Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter (Extended Abstract), Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Texts are often interconnected in a network structure, e.g., academic papers via citations. On the one hand, though Graph Neural Networks (GNNs) have shown promising ability to derive effective embeddings for networked documents, they do not assume latent topics, resulting in uninterpretahle embeddings. On the other hand, topic models can infer interpretable document representations. However, most topic models focus on plain text and fail to leverage network structure across documents. In this paper, we propose a GNN-based topic model that both captures network connection and derives semantically interpretable text representations. For network modeling, we build our model with Optimal Transport …

Augmenting Decision With Hypothesis In Reinforcement Learning, Minh Quang Nguyen, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling efficiency. However, our study shows it suffers from low exploitation in early training period and bias sensitiveness. To address these issues, we propose to augment the decision-making process with hypothesis, a weak form of environment description. Our approach relies on prompting the learning agent with accurate hypotheses, and designing a ready-to-adapt policy through incremental learning. We propose the ALH algorithm, showing detailed analyses on a typical learning scheme and a diverse set of Mujoco benchmarks. Our algorithm produces a significant improvement over value-based learning algorithms and other strong baselines. …

Surfacing Text Changes In Archived Webpages, Lesley Frew

Computer Science Theses & Dissertations

Webpages change over time, and web archives hold copies of historical versions of webpages. Users of web archives, such as journalists, want to find and view changes on webpages over time. However, the current search interfaces for web archives do not adequately support this task. For the web archives that include a full-text search feature, multiple versions of the same webpage that match the search query are shown individually without enumerating changes, or are grouped together in a way that hides changes. We present a change text search engine that allows users to find changes in webpages. We describe the …

Streaminghub - A Realtime Biosignal Processing Framework For Lab Scale Experimentation, Yasith Jayawardana

Computer Science Theses & Dissertations

In human subjects research, biosignals such as eye movements, heart rate, and brain activity, are often collected and analyzed to find patterns with tangible real-world implications. Modern advancements in technology have sparked interest towards analyzing biosignals in realtime. When developing such algorithms, one may expect to find free, open-source tools that provide easy access to live, recorded, and simulated data streams. Yet, biosignal interfaces are often vendor-specific, making cross-vendor biosignal streaming non-trivial. Likewise, reading biosignal datasets is also non-trivial, as their content may be arranged quite differently.

To combat this divide, we provide the scientific community with a realtime biosignal …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Empowering Interprofessional Teams: Exploring Genai With The Health Sciences Library, Jess King, Teresa L. Hartman

Posters and Presentations: Leon S. McGoogan Health Sciences Library

The Leon S. McGoogan Health Sciences Library at the University of Nebraska Medical Center (UNMC) organized workshops to delve into Generative Artificial Intelligence (GenAI) applications in academic medical centers. These sessions, tailored for all skill levels, provided a safe forum for faculty and staff to engage with GenAI, increasing their digital literacy skills. Participants benefited from introductory sessions, hands-on activities, and reflective discussions, gaining practical insights into ethical GenAI use. These workshops form a vibrant GenAI community at UNMC, fostering collaboration and knowledge exchange among healthcare professionals and paving the way for continued technological integration in academic and clinical settings

Adopt: An Environmentally-Friendly System For Alerting Drivers To Occluded Pedestrians Traffic, Abrar Abdulrahman Alali

Computer Science Theses & Dissertations

The emergence of sensing technologies and vehicular communications has brought significant opportunities for enhancing pedestrian safety on city streets. However, existing solutions rely on costly technologies such as computer vision and trajectory prediction to detect crossing pedestrians, while they have limits in detecting pedestrians who are occluded by parked cars. Despite the presence of collaborative perception by surrounding vehicles and infrastructure, there is a notable absence of incorporating existing parked cars themselves due to their insufficiency in detecting pedestrians and communicating with other cars while they are turned off. Furthermore, accommodating pedestrians on streets has been linked to an additional …

Water Body Satellite Images Segmentation Using Maxwell Boltzmann Distribution, Lama Affara, Ali El-Zaart, Rabih Damaj

BAU Journal - Science and Technology

Images can exhibit diverse attributes and characteristics, because of variations in both the quantity of each intensity level and their respective positions, histograms display varying distributions. Some images feature symmetric histograms, while others exhibit asymmetry. In image segmentation tasks, traditional mean-based thresholding methods work well with symmetric histograms, relying on Gaussian distribution definitions. However, situations arise where asymmetric distributions must be considered. Threshold-based segmentation entails the partitioning of intensity levels into separate regions determined by the threshold value. Within this category of thresholding methods, Minimum Cross Entropy Thresholding (MCET) stands out as a mean-based thresholding technique with a unique self-contained …

Containerization On A Self-Supervised Active Foveated Approach To Computer Vision, Dario Dematties, Silvio Rizzi, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global …

Enhancing Tumor Classification Through Machine Learning Algorithms For Breast Cancer Diagnosis, Lawrence Agbota, Edmund F. Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei- Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, Augustina Adjei

School of Mathematical & Statistical Sciences Faculty Publications

In cancer diagnosis, machine learning helps improve cancer detection by providing doctors with a second perspective and allowing for faster and more accurate determination and decisions. Numerous studies have used both classic machine learning approaches and deep learning to address cancer classification. In this study, we examine the efficacy of five commonly used machine learning algorithms; both traditional and deep learning models namely, Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), Decision Tree and Deep Neural Networks (DNN). We analyze their ability to properly classify tumors as Benign or Malignant using the Wisconsin breast cancer dataset (WBCD). Random Forest …

Student Partners In Ai Literacy: A Library And Writing Center Collaboration, A. P. Anderson

Velma K. Waters Library Faculty Publications

Student voices are valuable but often overlooked in discussions surrounding the role of AI in higher education. AI Literacy education efforts that treat students only as a potential audience for instruction rather than as potential instructors themselves miss out on the passion, curiosity, and complex questions that students can bring to these conversations. If we center student voices in AI Literacy education discussions, and encourage both their enthusiasm and skepticism, students can become comfortable and confident in leading discussions about AI in the classroom and in their lives. In my proposed poster presentation, I will share insights from an AI …

Hierarchical Quantized Autoencoders: Using Hierarchical Models For Data Compression Across Multiple Domains, Armani Lorenzo Rodriguez

Theses and Dissertations

In the era of vast data processing and transmission, sending data over a channel for downstream operations is a very common occurrence. The bandwidth of this data channel acts as a limiting factor in this operation, capping the amount of data that can be sent over a time period. Therefore, in addition to pursuing advancements in networking technology, there exists a need for more efficient means of data compression. Learned compression is the application of machine learning models to the data compression problem, and in this study, we leverage the ability of neural networks to learn the underlying structure of …

Ai Literacy Innovations: Chatgpt's Integration Into A First-Year Information Literacy Program, Taylor J. Greene, Douglas R. Dechow

Library Presentations, Posters, and Audiovisual Materials

In the dynamic field of information technology, integration of Artificial Intelligence (AI) literacy into information literacy instruction is now essential to ensure the ethical and productive use of generative AI by our students. This poster demonstrates our innovative approach to embedding AI literacy within the first-year information literacy program at an R2 research university. We used a two-pronged strategy: an “AI Literacy” section in Canvas and practical demonstrations of applying ChatGPT in live library sessions. The Canvas module section equips students with foundational knowledge and critical thinking about using generative AI for research and learning activities. It covers AI fundamentals, …

Hyper-Dimensional Computing And Its Applications In Tinyml, Ellis A. Weglewski

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

As computing systems enter the realm of nano form levels, new fields of computational development have spawned, each posing their own set of challenges. Amongst these fields is Tiny Machine Learning (tinyML), which aims to install machine learning on tiny embedded systems. The restrictions imposed upon algorithms by the limited hardware of nano-scale tiny systems make contemporary approaches to machine learning non-contenders. Hyperdimensional computing is an approach to representing data as high-dimensional vectors which allows for one-pass encoding and quick all-encompassing comparison operations via an associative memory. This approach is power-efficient, robust, and can be done in-memory, all of which …

Enhancing Evolutionary Computation Through Phylogenetic Analysis, Chenfei Peng

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

In this paper, we will provide an overview of the paper “Phylogeny-informed fitness estimation for test-based parent selection” by Lalejini, et al. [7] Phylogenies, or ancestry trees, provide a detailed look into the evolutionary journey of a population. In evolutionary computation, a phylogeny can represent the progress of an evolutionary algorithm through a search space. Although phylogenetic analysis is mainly used to deepen the understanding of evolutionary algorithms after they have been run, this study explores its potential use in real-time to enhance parent selection during evolutionary searches. The research by Lalejini, et al. introduces the concept of phylogeny-informed fitness …

Simulation Of Rice Disease Recognition Based On Improved Attention Mechanism Embedded In Pr-Net Model, Yang Lu, Pengfei Liu, Siyuan Xu, Qiwang Liu, Fuqian Gu, Peng Wang

Journal of System Simulation

Abstract: Aiming at the low accuracy of existing CNN models in identifying rice leaf diseases, a hybrid convolutional neural network model PRC-Net (parallel residual with coordinate attention network) combining parallel structure and residual structure is proposed. A parallel structure is introduced to improve the receptive field of convolution, and the residual structure is combined to achieve the complete and continuous transmission of feature information. An improved spatial attention mechanism is embedded into the backbone model PR-Net to enhance the degree of aggregation of lesion feature information at different scales. In order to further improve the accuracy of disease identification and …

Just-In-Time Learning Energy Consumption Predictive Modeling Method In Multi-Condition Production Process, Sheng Wei, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the problem that the global energy consumption prediction model is only suitable for part of the prediction sample and the model is computationally intensive, the idea of just-in-time learning is introduced, and the local weighted partial least squares method combined with the energy consumption model is used to establish a temporary local energy consumption prediction model. The inertia weights of the particle swarm algorithm are improved, considering the effects of particle fitness, number of iterations and population size on the convergence speed and convergence accuracy of the particle swarm algorithm, a nonlinear change adaptive inertia weight strategy …

Thinking Of Aerospace Equipment Systematization Simulation Technology Development, Weimin Bao, Zhenqiang Qi

Journal of System Simulation

Abstract: The aerospace field is flourishing in the new era. Aerospace equipment presents new characteristics such as systematization, new quality, high efficiency and intelligence. Simulation technology plays a more important role in the digital aerospace era as a means of enhancing efficiency and empowerment covering all stages of the entire lifecycle, including project demonstration, research and development, testing, manufacturing, training, and maintenance. The conception of aerospace equipment systematization simulation technology is introduced, the current development status and practices at home and abroad are elaborated, and the future development trends and challenges of aerospace equipment systematization simulation technology are evaluated. Focusing …

Unsupervised Complex Condition Recognition Based On Stochastic Neighborhood Embedding, Lin Huang, Shanjun Liu, Wei Wang, Li Gong

Journal of System Simulation

Abstract: Modern industrial production equipment usually has a complex structure and runs alternately in different working conditions. Accurate working conditions identification based on monitoring data is the basis of health monitoring of the system, but the monitoring data of the system usually has a high dimension and a large data volume. To identify the complex equipment operating conditions, an unsupervised operating condition identification method based on stochastic neighborhood embedding is proposed. The stochastic neighborhood embedding algorithm can simultaneously preserve the local and global structural characteristics of the data, and also calculate the probability similarity of data points in high-dimensional and …

Completion Time Simulation Prediction Method For Aircraft Assembly Process With Batch And Sortie, Changjian Jiang, Hu Fan, Tao Luo, Wen Yuan, Zehao He

Journal of System Simulation

Abstract: Aiming at the product differentiation analysis limitation of traditional discrete event simulation method, a simulation prediction method for aircraft assembly process with batch and sortie is proposed. Around the aircraft sortie number, the formal definition of various basic elements and interactions in the assembly process with batch and sortie is studied, and the construction of station and whole line simulation model is carried out. The simulation promotion framework and execution mechanism supporting the product differentiation analysis are studied. Based on the simulation results, a method for predicting the completion time of sorties based on interval estimation method is proposed. …

Adaptive Pid Control Algorithm Based On Ppo, Zhiyong Zhou, Fei Mo, Kai Zhao, Yunbo Hao, Yufeng Qian

Journal of System Simulation

Abstract: A six-axis robotic arm is built and simulated in a complex control environment with disturbances by using MATLAB physics engine and Python, which provides a trial-and-error environment for the robotic arm training that could not be provided in reality. Proximal policy optimization(PPO) algorithm in reinforcement learning is proposed to improve the traditional PID control algorithm. By introducing the multi-agent idea and on the basis of the different effects of the three parameters of PID on control system and the characteristics of the six-axis robotic arm, the three parameters are separately trained as different intelligent individuals to achieve a new …

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 …

Design Of Real-Time Simulation & Test Software Based On Windows/Rtx, Yongbo Li, Runmei Tian, Hui Zhang, Shanpeng Guo, Qi Li

Journal of System Simulation

Abstract: Aiming at the limited real-time performance of traditional test software and the low generality of traditional simulation interface software, a real-time simulation software based on Windows/RTX is designed to meet the requirements of unit testing and control system simulation verification of semiphysical simulation software. Through modular design, GUI layer human-computer interface and RTX layer real-time operation program are developed. To ensure the real-time, the lock-free cyclic buffer plus dual-threading technology is used to solve the timeout problem of serial data transmission and reception when the simulation step size is 1 ms under RTX environment. A timeout detection algorithm is …

Cooperative Ant Colony Algorithm Combining Evaluation Reward And Punishment Mechanism And Neighborhood Dynamic Degradation, Yujie Wang, Xiaoming You, Sheng Liu

Journal of System Simulation

Abstract: To address the slow convergence and the tendency to fall into local optimality in solving TSP, a cooperative ant colony algorithm combining evaluation reward and punishment mechanism and neighborhood dynamic degradation (ENCACO) is proposed. The paths are classified into active and abandon paths according to the path evaluation value, and with the path evaluation value as the weight, the different pheromone reward and punishment strategies are adopted for the two types of paths to accelerate the convergence speed of the algorithm. Through the neighborhood dynamic degradation strategy, and the neighborhood radius is used to divide the set of cities …