Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (22)
- Portland State University (8)
- Technological University Dublin (6)
- University of Nebraska - Lincoln (3)
- Brigham Young University (2)
-
- Chapman University (2)
- Dordt University (2)
- University of Connecticut (2)
- Bentley University (1)
- Boise State University (1)
- Dominican University of California (1)
- Embry-Riddle Aeronautical University (1)
- James Madison University (1)
- Marshall University (1)
- Pace University (1)
- Purdue University (1)
- Sheridan College (1)
- University of New Mexico (1)
- University of Texas Rio Grande Valley (1)
- University of the Pacific (1)
- Utah State University (1)
- Washington University in St. Louis (1)
- West Chester University (1)
- West Virginia University (1)
- Western Washington University (1)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (22)
- Chemistry Faculty Publications and Presentations (6)
- Conference papers (6)
- CSE Conference and Workshop Papers (2)
- Faculty Publications (2)
-
- Faculty Work Comprehensive List (2)
- Mathematics, Physics, and Computer Science Faculty Articles and Research (2)
- Physics Faculty Publications and Presentations (2)
- Accountancy Faculty Publications (1)
- Collected Faculty and Staff Scholarship (1)
- College of the Pacific Faculty Articles (1)
- Cornerstone 3 Reports : Interdisciplinary Informatics (1)
- Department of Chemistry and Biochemistry - Faculty Scholarship (1)
- Department of Computer Science and Engineering: Dissertations, Theses, and Student Research (1)
- Faculty & Staff Scholarship (1)
- Generative AI Teaching Activities (1)
- Geosciences Faculty Publications and Presentations (1)
- Honors Scholar Theses (1)
- IMPACT Presentations (1)
- Mathematics Faculty Publications (1)
- Mathematics and Statistics Faculty Publications (1)
- Physics Faculty Research (1)
- Publications (1)
- Publications and Scholarship (1)
- STEM Gateway (1)
- School of Mathematical and Statistical Sciences Faculty Publications and Presentations (1)
- University Scholar Projects (1)
- WWU Honors College Senior Projects (1)
Articles 31 - 60 of 64
Full-Text Articles in Entire DC Network
Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin
Hierarchical Active Learning Application To Mitochondrial Disease Protein Dataset, James D. Duin
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
This study investigates an application of active machine learning to a protein dataset developed to identify the source of mutations which give rise to mitochondrial disease. The dataset is labeled according to the protein's location of origin in the cell; whether in the mitochondria or not, or a specific target location in the mitochondria's outer or inner membrane, its matrix, or its ribosomes. This dataset forms a labeling hierarchy. A new machine learning approach is investigated to learn the high-level classifier, i.e., whether the protein is a mitochondrion, by separately learning finer-grained target compartment concepts and combining the results. This …
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Research Collection School Of Computing and Information Systems
Program assertions are useful for many program analysis tasks. They are however often missing in practice. In this work, we develop a novel approach for generating likely assertions automatically based on active learning. Our target is complex Java programs which cannot be symbolically executed (yet). Our key idea is to generate candidate assertions based on test cases and then apply active learning techniques to iteratively improve them. The experiments show that active learning really helps to improve the generated assertions.
Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang
Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang
Research Collection School Of Computing and Information Systems
This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and suboptimal exploitation of available information when querying the labeled data. To overcome the limitations, …
Exploring Representativeness And Informativeness For Active Learning, Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, Dacheng Tao
Exploring Representativeness And Informativeness For Active Learning, Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, Dacheng Tao
Research Collection School Of Computing and Information Systems
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without …
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Research Collection School Of Computing and Information Systems
A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …
Online Passive-Aggressive Active Learning, Jing Lu, Peilin Zhao, Steven C. H. Hoi
Online Passive-Aggressive Active Learning, Jing Lu, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
We investigate online active learning techniques for online classification tasks. Unlike traditional supervised learning approaches, either batch or online learning, which often require to request class labels of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, aiming to maximize classification performance with minimal human labelling effort during the entire online learning task. In this paper, we present a new family of online active learning algorithms called Passive-Aggressive Active (PAA) learning algorithms by adapting the Passive-Aggressive algorithms in online active learning settings. Unlike conventional Perceptron-based approaches that employ only the …
Stem Gateway Course Redesign Teaching Professional Development: Resources For Teaching And Learning, Gary Smith, Audriana Stark
Stem Gateway Course Redesign Teaching Professional Development: Resources For Teaching And Learning, Gary Smith, Audriana Stark
STEM Gateway
The learning object is a collection of teaching professional development presentations and workbooks for guiding faculty in the re-design of lower-division college science and mathematics courses. The materials were designed and implemented during the University of New Mexico STEM Gateway Project, funded by the U.S. Department of Education Title V program during 2012-2016. The teaching professional development curriculum consisted of a 2.5-day course redesign institute followed by roughly monthly sessions on topics that include teaching diverse students; building learning strategies for students; obtaining student buy-in for active learning; evaluating alignment of learning objectives, activities, and assessment; peer observation of teaching, …
How Does The Structure Of A College Chemistry Examination Affect Pedagogy, Rajeev R. Pandey, John Mayberry, Jace Hargis
How Does The Structure Of A College Chemistry Examination Affect Pedagogy, Rajeev R. Pandey, John Mayberry, Jace Hargis
College of the Pacific Faculty Articles
This study examines variations of assessment and connections to active learning methods, which may enhance both the accuracy of assessment, engagement and retention. Correlation data relating instruction and assessment in a multiple dimensions are presented. Multiple choice (MC) and free response (FR) exams were provided and students were also given the option to provide FR answers to the MC items. This study suggests there is little overall difference in mean or median student scores on the MC vs. FR portions of the exam, but that there is some evidence to believe that student scores on MC portions are more variable …
Learning Hierarchically Decomposable Concepts With Active Over-Labeling, Yuji Mo, Stephen Scott, Doug Downey
Learning Hierarchically Decomposable Concepts With Active Over-Labeling, Yuji Mo, Stephen Scott, Doug Downey
CSE Conference and Workshop Papers
Many classification tasks target high-level concepts that can be decomposed into a hierarchy of finer-grained subconcepts. For example, some string entities that are Locations are also Attractions, some Attractions are Museums, etc. Such hierarchies are common in named entity recognition (NER), document classification, and biological sequence analysis. We present a new approach for learning hierarchically decomposable concepts. The approach learns a high-level classifier (e.g., location vs. non-location) by seperately learning multiple finer-grained classifiers (e.g., museum vs. non-museum), and then combining the results. Soliciting labels at a finer level of granularity than that of the target concept is a new approach …
Building Data And Information Literacy In The Undergraduate Chemistry Curriculum, Yasmeen Shorish, Barbara A. Reisner
Building Data And Information Literacy In The Undergraduate Chemistry Curriculum, Yasmeen Shorish, Barbara A. Reisner
Department of Chemistry and Biochemistry - Faculty Scholarship
The Literature and Seminar sequence at James Madison University has been used to develop the chemistry information literacy skills of chemistry majors for over four decades. These courses have been continually updated to emphasize information literacy skills for the twenty-first century. This chapter describes the methods that have been developed to improve chemical, data and general information literacy at a large, public, primarily undergraduate institution. The focus of the first semester course, described in this chapter, is on skill building rather than teaching specific resources. It is a model of integration and collaboration between chemistry faculty and chemistry librarians. Changes …
Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo
Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo
Research Collection School Of Computing and Information Systems
Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of …
Online Resource Platform For Mathematics Education, Marisa Llorens, Edmund Nevin, Eileen Mageean
Online Resource Platform For Mathematics Education, Marisa Llorens, Edmund Nevin, Eileen Mageean
Conference papers
Engineering education is facing many challenges: a decline in core mathematical skills; lowering entry requirements; and the diversity of the student cohort. One approach to confronting these challenges is to make subject content appropriate to the communication styles of today’s student. To achieve this, a pedagogical shift from the traditional hierarchical approach to learning to one that embraces the use of technology as a tool to enhance the student learning experience is required. By including the student as co-creator of course content, a greater sense of engagement is achieved and a change to one where students become agents of their …
Work In Progress: Online Resource Platform For Mathematics Education, Marisa Llorens, Edmund Nevin, Eileen Mageean
Work In Progress: Online Resource Platform For Mathematics Education, Marisa Llorens, Edmund Nevin, Eileen Mageean
Conference papers
Mathematics is intrinsic to engineering and as such plays an integral role in the education of engineers. New challenges are being faced in higher education particularly in the areas of student motivation, engagement and attainment. As a result mathematics is often the focus of engineering education research. Traditional methods of delivery such as lectures and tutorials need to evolve to counter these challenges with new pedagogical approaches explored including the use of new technologies. Today’s students are immersed in an increasingly technological world and are willing to adapt to new technological advances. This paper describes a study being undertaken in …
Teaching Tip: The Flipped Classroom, Heng Ngee Mok
Teaching Tip: The Flipped Classroom, Heng Ngee Mok
Research Collection School Of Computing and Information Systems
The flipped classroom has been gaining popularity in recent years. In theory, flipping the classroom appears sound: passive learning activities such as unidirectional lectures are pushed to outside class hours in the form of videos, and precious class time is spent on active learning activities. Yet the courses for information systems (IS) undergraduates at the university that the author is teaching at are still conducted in the traditional lecture-in-class, homework-after-class style. In order to increase students’ engagement with the course content and to improve their experience with the course, the author implemented a trial of the flipped classroom model for …
Representative Discovery Of Structure Cues For Weakly-Supervised Image Segmentation, Luming Zhang, Yue Gao, Yingjie Xia, Ke Lu, Jialie Shen, Rongrong Ji
Representative Discovery Of Structure Cues For Weakly-Supervised Image Segmentation, Luming Zhang, Yue Gao, Yingjie Xia, Ke Lu, Jialie Shen, Rongrong Ji
Research Collection School Of Computing and Information Systems
Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its imagelevel semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an ecient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not …
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Malicious Uniform Resource Locator (URL) detection is an important problem in web search and mining, which plays a critical role in internet security. In literature, many existing studies have attempted to formulate the problem as a regular supervised binary classification task, which typically aims to optimize the prediction accuracy. However, in a real-world malicious URL detection task, the ratio between the number of malicious URLs and legitimate URLs is highly imbalanced, making it very inappropriate for simply optimizing the prediction accuracy. Besides, another key limitation of the existing work is to assume a large amount of training data is available, …
Enhancing The Interdisciplinary Curriculum In Cybersecurity By Engaging High-Impact Educational Practices, Li-Chiou Chen, Andreea Cotoranu
Enhancing The Interdisciplinary Curriculum In Cybersecurity By Engaging High-Impact Educational Practices, Li-Chiou Chen, Andreea Cotoranu
Cornerstone 3 Reports : Interdisciplinary Informatics
No abstract provided.
Learning To Teach And Teaching To Learn, Maria Babiuc-Hamilton
Learning To Teach And Teaching To Learn, Maria Babiuc-Hamilton
Physics Faculty Research
New studies show that students do better in science classes that are taught interactively. We compare two different pedagogical approaches in teaching introductory physics: the lecture-based method, the active learning laboratories. We present the data on student performance on exams, homework, lab activities and tests, from 126 students taking the 200-level introductory physics courses at Marshall University, in Huntington, WV. We discuss the efficiency of each method in fostering the success of students in the introductory physics courses. We find that subtle differentiations can be implicitly detected in students’ exam grades, homework, participation, and choice of major.
Retention Of Statistical Concepts In A Preliminary Randomization-Based Introductory Statistics Curriculum, Nathan L. Tintle, Kylie Topliff, Jill Vanderstoep, Vicki-Lynn Holmes, Todd Swanson
Retention Of Statistical Concepts In A Preliminary Randomization-Based Introductory Statistics Curriculum, Nathan L. Tintle, Kylie Topliff, Jill Vanderstoep, Vicki-Lynn Holmes, Todd Swanson
Faculty Work Comprehensive List
Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76) to a cohort of students who used the consensus curriculum (n = 79). Overall, students taking the randomization-based curriculum showed higher conceptual retention in areas emphasized in the curriculum, with no significant decrease in conceptual retention in other areas. This study provides additional support for the use of randomization-methods in teaching introductory …
Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany
Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany
Conference papers
Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.
Egal: Exploration Guided Active Learning For Tcbr, Rong Hu, Sarah Jane Delany, Brian Mac Namee
Egal: Exploration Guided Active Learning For Tcbr, Rong Hu, Sarah Jane Delany, Brian Mac Namee
Conference papers
The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present …
Exploring The Frontier Of Uncertainty Space, Rong Hu, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee
Exploring The Frontier Of Uncertainty Space, Rong Hu, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee
Conference papers
We aim to investigate methods balancing exploitation with exploration in active learning to improve the performance of uncertainty sampling. Two exploration guided sampling methods are compared to uncertainty sampling on various real-life datasets from the 2010 Active Learning Challenge. Our initial experiments seems to indicate that combining exploration with uncertainty sampling improves performance on certain datasets but not all.
Off To A Good Start: Using Clustering To Select The Initial Training Set In Active Learning, Rong Hu, Brian Mac Namee, Sarah Jane Delany
Off To A Good Start: Using Clustering To Select The Initial Training Set In Active Learning, Rong Hu, Brian Mac Namee, Sarah Jane Delany
Conference papers
Active learning (AL) is used in textual classification to alleviate the cost of labelling documents for training. An important issue in AL is the selection of a representative sample of documents to label for the initial training set that seeds the process, and clustering techniques have been successfully used in this regard. However, the clustering techniques used are nondeterministic which causes inconsistent behaviour in the AL process. In this paper we first illustrate the problems associated with using non-deterministic clustering for initial training set selection in AL. We then examine the performance of three deterministic clustering techniques for this task …
Freedom Of Choice As A Motivational Factor In Active Learning, Atanas Radenski
Freedom Of Choice As A Motivational Factor In Active Learning, Atanas Radenski
Mathematics, Physics, and Computer Science Faculty Articles and Research
Freedom to choose what, when, and how to contribute in a learning process can motivate students to actively engage and achieve more in their studies. However, freedom of choice complicates course management and may deter instructors from allowing such freedom. Our approach is to utilize existing functionality of course management systems such as Moodle to automatically facilitate and coordinate free student choices and provide much needed relief for instructors at the same time. Using Moodle we have developed novel digital study packs that blend freedom of choice with guidance and control. Our survey shows that assisted freedom of choice is …
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Active Learning For Causal Bayesian Network Structure With Non-Symmetrical Entropy, Li G., Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with node selection. Our method admits a node selection criterion based on non-symmetrical entropy from the current data and a stop criterion based on structure entropy of the resulting networks. We examine the technical challenges and practical issues involved. Experimental results on a set of benchmark Bayesian networks …
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Empirical Usage Metadata In Learning Objects, Gwen Nugent, Kevin Kupzyk, S. A. Riley, L.D. Miller, Jesse Hostetler, Leen-Kiat Soh, Ashok Samal
Empirical Usage Metadata In Learning Objects, Gwen Nugent, Kevin Kupzyk, S. A. Riley, L.D. Miller, Jesse Hostetler, Leen-Kiat Soh, Ashok Samal
CSE Conference and Workshop Papers
The iLOG Project (Intelligent Learning Object Guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in …
Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Faculty Publications
Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longer than others. We show that the AL technique which performs best depends on how cost is measured. Applying an hourly cost model based on the results of an annotation user study, we approximate the amount of time necessary to annotate a given sentence. This model allows us to evaluate the effectiveness of AL sampling methods in terms of time spent in annotation. We acheive a 77% reduction in hours from a random …
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Accelerating Corpus Annotation Through Active Learning, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Accelerating Corpus Annotation Through Active Learning, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Faculty Publications
PDF of Powerpoint Presentation on accelerating corpus annotation through active learning. This presentation was given at the Conference of the American Association for Corpus Linguistics in 2008.