Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions,
2024
Singapore Management University
Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …
Reinforcement Learning For Strategic Airport Slot Scheduling: Analysis Of State Observations And Reward Designs,
2024
Nanyang Technological University
Reinforcement Learning For Strategic Airport Slot Scheduling: Analysis Of State Observations And Reward Designs, Anh Nguyen-Duy, Duc-Thinh Pham, Jian-Yi Lye, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Due to the NP-hard nature, the strategic airport slot scheduling problem is calling for exploring sub-optimal approaches, such as heuristics and learning-based approaches. Moreover, the continuous increase in air traffic demand requires approaches that can work well in new scenarios. While heuristics rely on a fixed set of rules, which limits the ability to explore new solutions, Reinforcement Learning offers a versatile framework to automate the search and generalize to unseen scenarios. Finding a suitable state observation and reward structure design is essential in using Reinforcement Learning. In this paper, we investigate the impact of providing the Reinforcement Learning agent …
Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem,
2024
Singapore Management University
Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem, Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun
Research Collection School Of Computing and Information Systems
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based …
Adaptive Stabilization Based On Machine Learning For Column Generation,
2024
Singapore Management University
Adaptive Stabilization Based On Machine Learning For Column Generation, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Eberhard Andrew, Guangquan Zhang
Research Collection School Of Computing and Information Systems
Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. …
Mvmoe: Multi-Task Vehicle Routing Solver With Mixture-Of-Experts,
2024
Singapore Management University
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,
2024
Singapore Management University
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 …
Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter (Extended Abstract),
2024
Singapore Management University
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,
2024
Singapore Management University
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. …
Unified Training Of Universal Time Series Forecasting Transformers,
2024
Singapore Management University
Unified Training Of Universal Time Series Forecasting Transformers, Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Research Collection School Of Computing and Information Systems
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel …
Diffusion Models For Generative Outfit Recommendation,
2024
Singapore Management University
Diffusion Models For Generative Outfit Recommendation, Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He
Research Collection School Of Computing and Information Systems
Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are constrained by existing fashion products, limiting their effectiveness in addressing users' diverse fashion needs. Recently, the advent of AI-generated content provides the opportunity for OR to transcend these limitations, showcasing the potential for personalized outfit generation and recommendation.To this end, we introduce a novel task called Generative Outfit Recommendation (GOR), aiming to generate a set of fashion images and compose them into a visually compatible outfit tailored to specific users. The key objectives of GOR lie in …
Enhancing Tumor Classification Through Machine Learning Algorithms For Breast Cancer Diagnosis,
2024
The University of Texas Rio Grande Valley
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 …
Containerization On A Self-Supervised Active Foveated Approach To Computer Vision,
2024
Northwestern Argonne Institute of Science and Engineering
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 …
Student Partners In Ai Literacy: A Library And Writing Center Collaboration,
2024
East Texas A&M University
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 …
Ai Literacy Innovations: Chatgpt's Integration Into A First-Year Information Literacy Program,
2024
Chapman University
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,
2024
University of Minnesota Morris
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 …
Simulation Of Rice Disease Recognition Based On Improved Attention Mechanism Embedded In Pr-Net Model,
2024
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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,
2024
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
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 …
Completion Time Simulation Prediction Method For Aircraft Assembly Process With Batch And Sortie,
2024
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
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,
2024
School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China
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,
2024
School of Mathematics and Computer, Tongling University, Tongling 244061, China; School of Computer and Information, Anqing Normal University, Anqing 246133, China
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 …
