Whole Ship Simulation Training Platform Based On Virtual Reality,
2021
Marine Dynamic Simulation and Control Lab, Dalian Maritime University, Dalian 116026, China;
Whole Ship Simulation Training Platform Based On Virtual Reality, Jiawen Sun, Hongxiang Ren, Fangbing Xiao, Xiaobin Jiang
Journal of System Simulation
Abstract: Under the current navigation education mode, it is common that trainees lack the overall navigation knowledge and the operation practice on onboard equipment and instruments. It is necessary to develop a whole ship simulation training platform that is not limited by time and sites. The six-degree freedom mathematical model of ship motion is established by using the separation modeling theory of ship maneuverability. The flexible object simulation technology, the modeling and rendering of ocean wave technology, and the reverse dynamics technology are adopted to realize the ship scene roaming and equipment interaction. The simulation results show that …
Evolutionary Simulation Of Medical Products Export Safety Supervision Considering Regulation Of Importing Country,
2021
1. School of Management, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; ;
Evolutionary Simulation Of Medical Products Export Safety Supervision Considering Regulation Of Importing Country, Xiaoli Li, Cejun Cao, Fanshun Zhang
Journal of System Simulation
Abstract: In order to investigate the impacts of the regulation of importing country on export safety supervision regarding medical products in the COVID-19 epidemic situation, an evolutionary game model considering the relation between export enterprise and supervision of government is constructed. Based on MATLAB simulation, the influences of different factors including regulation level of importing country, punishment mechanism on export safety supervision of medical products are analyzed. Results show that the illegal behaviors of the export enterprises of medical products can be restrained when the regulation level of importing country reaches a certain threshold. But that would fuel the passively …
Classification Of Flight Delay Based On Nonlinear Weighted Xgboost,
2021
Aeronautics Engineering College, Air Force Engineering University, X'an 710038, China;
Classification Of Flight Delay Based On Nonlinear Weighted Xgboost, Tang Hong, Wang Dong, Song Bo, Wenkui Chu, Linyuan He
Journal of System Simulation
Abstract: Aiming at the classification of flight delay under imbalance data, a novel method based on nonlinear weighted XGBoost (extreme gradient boosting) is proposed. The imbalance of flight delay data and the influence for classification performance caused by the data imbalance are analyzed. A heuristic nonlinear weighting method based on sample proportion is proposed, and the negative log likelihood loss function is optimized. The real flight delay dataset is used to validate the performance of the classification algorithm. The experiment results show that the proposed nonlinear weighted XGBoost algorithm can improve the classification accuracy of flight delay, while ensuing a …
Research On Benefits Of Mixed Traffic Flow Of Intelligent Connected Vehicles,
2021
1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China; ;2. Underground Polis Academy, Shenzhen University, Shenzhen 518060, China; ;3. Key Laboratory of Coastal Urban Resilient Infrastructures, Ministry of Education, Shenzhen University, Shenzhen 518060, China;
Research On Benefits Of Mixed Traffic Flow Of Intelligent Connected Vehicles, Mingwei Hu, Zhiming Zhang, Xiangsheng Chen
Journal of System Simulation
Abstract: In order to study the benefits of the mixed traffic flow Intelligent Connected Vehicles (ICV) and non-connected vehicles, aiming at the intelligent and connected characteristics of ICV. Through the microscopic traffic simulation software Vissim redevelopment, ICV having the function of intelligent connected vehicles traffic flow modeling is realized. The mixed traffic flow composed of intelligent connected vehicles and non-connected vehicles under different market penetration rates is simulated. By comparing the traffic benefits at different time periods and different penetration rates, it is found that with the increase of the market penetration rate of intelligent connected vehicles, the average speed …
Visual Analysis Method Of Tobacco Quality Data Based On Dimension Reduction,
2021
1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100190, China; ;
Visual Analysis Method Of Tobacco Quality Data Based On Dimension Reduction, Tian Dong, Guihua Shan, Xuebin Chi, Yanling Zhang, Weihua Feng, Jianwei Wang, Aiguo Wang, Wang Rui
Journal of System Simulation
Abstract: In order to meet the requirements of tobacco leaf matching across regions in tobacco material selection, a visual analysis method of tobacco leaf quality data that incorporating dimension reduction and correlation analysis methods is developed. Through the dimension reduction algorithm, the comparison algorithm and the visual interaction method based on the classification of aroma area for tobacco leaf quality data, a visual analysis method for exploring space division and correlation analysis of tobacco leaf quality data is provided. National tobacco leaf quality data analysis cases and expert demonstrations show that the method can carry out the tobacco leaf quality …
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data,
2021
InfiniteTactics, LLC
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Faculty Publications
The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …
Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice,
2021
American Institutes for Research
Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice, Robert Schwarzhaupt, Feng Liu, Joseph Wilson, Fanny Lee, Melissa Rasberry
Journal of Computer Science Integration
Prekindergarten to 12th-grade teachers of computer science (CS) face many challenges, including isolation, limited CS professional development resources, and low levels of CS teaching self-efficacy that could be mitigated through communities of practice (CoPs). This study used survey data from 420 PK–12 CS teacher members of a virtual CoP, CS for All Teachers, to examine the needs of these teachers and how CS teaching self-efficacy, community engagement, and sharing behaviors vary by teachers’ instructional experiences and school levels taught. Results show that CS teachers primarily join the CoP to gain high-quality pedagogical, assessment, and instructional resources. The study also found …
How ‘Human’ Should Robots Be?,
2021
Singapore Management University
How ‘Human’ Should Robots Be?, Singapore Management University
Perspectives@SMU
Hotel guests like interaction with devices that look and sound like them, but they can spark displeasure after service failures, new CUHK study shows
Reinforcement Learning Algorithms: An Overview And Classification,
2021
Western University
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms,
2021
Air Force Institute of Technology
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud
Theses and Dissertations
Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …
Exploiting Group Structures To Infer Social Interactions From Videos,
2021
Dartmouth College
Exploiting Group Structures To Infer Social Interactions From Videos, Maksim Bolonkin
Dartmouth College Ph.D Dissertations
In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features …
Adversarial Training For Skill Learning In A Mobile Robot,
2021
CUNY Graduate Center
Adversarial Training For Skill Learning In A Mobile Robot, Todd W. Flyr
Dissertations, Theses, and Capstone Projects
Machine Learning in mobile robotics is sometimes hampered by the difficulties associated with the creation of a large corpus of labeled data that most neural network based learning algorithms demand. In recent years, advances in the field of machine learning have been facilitated via the creation of large collaboratively-created labeled training datasets that researchers can use as the basis for experiments to validate and improve their candidate neural network architectures. For the field of robotics, however, tasks are so disparate and the physical devices so varied that in most cases the creation of collaborative benchmark datasets are impractical. Obtaining data …
Piecewise Linear Manifold Clustering,
2021
CUNY Graduate Center
Piecewise Linear Manifold Clustering, Artyom Diky
Dissertations, Theses, and Capstone Projects
This work studies the application of topological analysis to non-linear manifold clustering. A novel method, that exploits the data clustering structure, allows to generate a topological representation of the point dataset. An analysis of topological construction under different simulated conditions is performed to explore the capabilities and limitations of the method, and demonstrated statistically significant improvements in performance. Furthermore, we introduce a new information-theoretical validation measure for clustering, that exploits geometrical properties of clusters to estimate clustering compressibility, for evaluation of the clustering goodness-of-fit without any prior information about true class assignments. We show how the new validation measure, when …
Solving Multiple Inference In Graphical Models,
2021
CUNY Graduate Center
Solving Multiple Inference In Graphical Models, Cong Chen
Dissertations, Theses, and Capstone Projects
For inference problems in graphical models, much effort has been directed at algorithms for obtaining one single optimal prediction. In practice, the data is often noisy or incomplete, which makes one single optimal solution unreliable. To address this problem, multiple Inference is proposed to find several best solutions, M-Best, where multiple hypotheses are preferred for advanced reasoning. People use oracle accuracy as an evaluation criterion expecting one of the solutions has high accuracy with the ground truth. It has been shown that it is beneficial for the top solutions to be diverse. Approaches for solving diverse multiple inference are proposed …
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling,
2021
Air Force Institute of Technology
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Theses and Dissertations
This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process,
2021
Singapore Management University
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, while graphs in real-world scenarios are gradually generated with different-typed temporal events, containing abundant semantics and dynamics. Limited work has been done for embedding dynamic heterogeneous graphs since it is very challenging to model the complete formation process of heterogeneous events. In this paper, we propose a novel Heterogeneous Hawkes Process based dynamic Graph Embedding (HPGE) to handle this problem. HPGE effectively integrates the Hawkes process into …
Artificial Intelligence And Work: Two Perspectives,
2021
Singapore Management University
Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport
Research Collection School Of Computing and Information Systems
One of the most important issues in contemporary societies is the impact of intelligent technologies on human work. For an empirical perspective on the issue, we recently completed 30 case studies of people collaborating with AI-enabled smart machines. Twenty-four were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. We compare some of our observations to one of the broadest academic examinations of the issue. In particular, we focus on our case study observations with regard to key findings from the MIT Task Force on the Work of the Future report.
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages,
2021
Singapore Management University
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …
Orthogonal Inductive Matrix Completion,
2021
Singapore Management University
Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft
Research Collection School Of Computing and Information Systems
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …
Learning And Evaluating Chinese Idiom Embeddings,
2021
Singapore Management University
Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang
Research Collection School Of Computing and Information Systems
We study the task of learning and evaluating Chinese idiom embeddings. We first construct a new evaluation dataset that contains idiom synonyms and antonyms. Observing that existing Chinese word embedding methods may not be suitable for learning idiom embeddings, we further present a BERT-based method that directly learns embedding vectors for individual idioms. We empirically compare representative existing methods and our method. We find that our method substantially outperforms existing methods on the evaluation dataset we have constructed.
