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Full-Text Articles in Databases and Information Systems
Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon
Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon
All Dissertations
The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Research Collection School Of Computing and Information Systems
With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …
Predicting League Of Legends Ranked Games Outcome, Ngoc Linh Chi Nguyen
Predicting League Of Legends Ranked Games Outcome, Ngoc Linh Chi Nguyen
Senior Projects Spring 2022
League of Legends (LoL) is the one of most popular multiplayer online battle arena (MOBA) games in the world. For LoL, the most competitive way to evaluate a player’s skill level, below the professional Esports level, is competitive ranked games. These ranked games utilize a matchmaking system based on the player’s ranks to form a fair team for each game. However, a rank game's outcome cannot necessarily be predicted using just players’ ranks, there are a significant number of different variables impacting a rank game depending on how well each team plays. In this paper, I propose a method to …
Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu
Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu
Graduate Theses and Dissertations
Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously in …
Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo
Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …
Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin
Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers …
Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee
Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee
Research Collection School Of Computing and Information Systems
If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data-which include user "check-ins", types of business, and business locations-to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of …
Exploring Data Mining Techniques For Tree Species Classification Using Co-Registered Lidar And Hyperspectral Data, Julia K. Marrs
Exploring Data Mining Techniques For Tree Species Classification Using Co-Registered Lidar And Hyperspectral Data, Julia K. Marrs
Theses and Dissertations
NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level.
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Doctoral Dissertations
Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify …
On Predicting User Affiliations Using Social Features In Online Social Networks, Minh Thap Nguyen
On Predicting User Affiliations Using Social Features In Online Social Networks, Minh Thap Nguyen
Dissertations and Theses Collection (Open Access)
User profiling such as user affiliation prediction in online social network is a challenging task, with many important applications in targeted marketing and personalized recommendation. The research task here is to predict some user affiliation attributes that suggest user participation in different social groups.
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu, Mei Tao, Jiebo Luo
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu, Mei Tao, Jiebo Luo
Research Collection School Of Computing and Information Systems
Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top n similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to …
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation …
Dynamically Optimized Context In Recommender Systems, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Dynamically Optimized Context In Recommender Systems, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our …