Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1121)
- TÜBİTAK (210)
- Wright State University (197)
- Technological University Dublin (192)
- University of New Mexico (165)
-
- Old Dominion University (143)
- Claremont Colleges (126)
- Missouri University of Science and Technology (125)
- University of Nebraska - Lincoln (125)
- San Jose State University (119)
- City University of New York (CUNY) (117)
- Brigham Young University (105)
- Neutrosophic Systems with Applications (92)
- University of Texas at El Paso (88)
- University of Northern Iowa (78)
- University of South Florida (78)
- Chulalongkorn University (73)
- Dartmouth College (71)
- MBZUAI (70)
- Kennesaw State University (66)
- Zayed University (62)
- Portland State University (61)
- Chapman University (60)
- New Jersey Institute of Technology (57)
- Boise State University (56)
- Edith Cowan University (56)
- University of Arkansas, Fayetteville (55)
- University of Nevada, Las Vegas (53)
- Purdue University (51)
- University of Texas Rio Grande Valley (51)
- Keyword
-
- Machine learning (211)
- Natural language processing (211)
- Artificial intelligence (157)
- Natural Language Processing (155)
- Machine Learning (147)
-
- Deep learning (132)
- Artificial Intelligence (101)
- Large language models (88)
- Computer Science (84)
- Social media (83)
- NLP (76)
- Twitter (70)
- Sentiment analysis (69)
- Mathematics (68)
- Deep Learning (67)
- Large Language Models (62)
- Text mining (59)
- Neural networks (58)
- Computational linguistics (54)
- Computer science (53)
- Department of Computer Science and Engineering (53)
- Education (49)
- Fuzzy logic (49)
- Generative AI (49)
- AI (48)
- Data mining (47)
- Linguistics (43)
- Classification (41)
- Social Media (39)
- Information retrieval (38)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1037)
- Theses and Dissertations (224)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Branch Mathematics and Statistics Faculty and Staff Publications (150)
- Master's Projects (99)
-
- Conference papers (96)
- Neutrosophic Systems with Applications (92)
- Kno.e.sis Publications (84)
- Dissertations (83)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (70)
- Computer Science Faculty Research & Creative Works (66)
- Electronic Theses and Dissertations (65)
- Journal of Humanistic Mathematics (63)
- All Works (62)
- Browse all Theses and Dissertations (62)
- Computer Science Faculty Publications (59)
- Faculty Publications (59)
- USF Tampa Graduate Theses and Dissertations (55)
- Publications and Research (51)
- Iowa Academy of Science Documents (50)
- Computer Science Faculty Publications and Presentations (45)
- Departmental Technical Reports (CS) (44)
- Dissertations, Theses, and Capstone Projects (44)
- Articles (42)
- Faculty, Staff and Student Publications (42)
- Open Access Theses & Dissertations (42)
- Journal of Informatics and Web Engineering (41)
- SMU Data Science Review (41)
- Walden Dissertations and Doctoral Studies (39)
- Computer Science and Engineering Faculty Publications (38)
- Publication Type
- File Type
Articles 2431 - 2460 of 6325
Full-Text Articles in Entire DC Network
The Impacts Of The Use Of Thematic & Chronologic Multi-Modal Information Representation On Sequential And Global Students’ Historical Understanding, Ling Weay Ang, Sellappan Palaniappan
The Impacts Of The Use Of Thematic & Chronologic Multi-Modal Information Representation On Sequential And Global Students’ Historical Understanding, Ling Weay Ang, Sellappan Palaniappan
Journal of Informatics and Web Engineering
This study examined the two different modes of multi-modes information presentations that affected sequential and global learners' history understanding: thematic and chronological. A total of 134 secondary schools’ students were enrolled (69 learning in chronological mode, 65 learning in thematic mode). Before the start of the treatment session, students were given a pre-test. The results showed that multimodal information presentation did not have a significantly great impact on historical learning or between pupils who learn in chronological and thematic ways. The chronological frame of reference technique, which reflected an interactive timeline, was reported to have supported students' sequential learning in …
Effective Statistical And Machine Learning Methods To Analyze Children's Vocabulary Learning, Houston T. Sanders
Effective Statistical And Machine Learning Methods To Analyze Children's Vocabulary Learning, Houston T. Sanders
USF Tampa Graduate Theses and Dissertations
Poor methodological and statistical practices can lead to unreliable results. The collaboration between statisticians and researchers can remedy this. Early education intervention research rarely uses advanced statistical techniques. Within early education, vocabulary instruction has been well-studied, yet outcomes continue to be underwhelming. The specialized knowledge and expertise statisticians possess has the potential to enhance word learning research by applying sophisticated analyses not commonly used. Choosing vocabulary words for instruction can be a daunting task and is highly subjective. In an effort to aid in the selection process, researchers use a word selection framework that groups words into three tiers. Even …
Using Intrinsically-Typed Definitional Interpreters To Verify Compiler Optimizations In A Monadic Intermediate Language, Dani Barrack
Using Intrinsically-Typed Definitional Interpreters To Verify Compiler Optimizations In A Monadic Intermediate Language, Dani Barrack
Dissertations and Theses
Compiler optimizations are critical to the efficiency of modern functional programs. At the same time, optimizations that unintentionally change the semantics of programs can systematically introduce errors into programs that pass through them. The question of how to best verify that optimizations and other program transformations preserve semantics is an important one, given the potential for error introduction. Dependent types allow us to prove that properties about our programs are correct, as well as to design data types and interpreters in such a way that they are correct-by-construction. In this thesis, we explore the use of dependent types and intrinsically-typed …
Digital Discrimination In The Sharing Economy: Evidence, Policy, And Feature Analysis, Miroslav Tushev
Digital Discrimination In The Sharing Economy: Evidence, Policy, And Feature Analysis, Miroslav Tushev
LSU Doctoral Dissertations
Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main facilitator of economic growth and shared prosperity in modern-day societies. However, recent research has revealed that the participation of minority groups in DSE activities is often hindered by different forms of bias and discrimination. Evidence of such behavior has been documented across almost all domains of DSE, including ridesharing, lodging, and freelancing. However, little is known about the under- lying design decisions of DSE systems which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge …
Why Menzerath's Law?, Julio Urenda, Vladik Kreinovich
Why Menzerath's Law?, Julio Urenda, Vladik Kreinovich
Departmental Technical Reports (CS)
In linguistics, there is a dependence between the length of the sentence and the average length of the word: the longer the sentence, the shorter the words. The corresponding empirical formula is known as the Menzerath's Law. A similar dependence can be observed in many other application areas, e.g., in the analysis of genomes. The fact that the same dependence is observed in many different application domains seems to indicate there should be a general domain-independent explanation for this law. In this paper, we show that indeed, this law can be derived from natural invariance requirements.
Deception-Based Knowledge In Indigenous And Scientific Societies American Indian Tricksters And Experimental Research Designs, Yancey Orr, Raymond Orr
Deception-Based Knowledge In Indigenous And Scientific Societies American Indian Tricksters And Experimental Research Designs, Yancey Orr, Raymond Orr
Environmental Science and Policy: Faculty Publications
The emerging movement to decolonize the sciences, social sciences, and humanities has emphasized the differences between Indigenous and Western scientific ways of knowing. Paradoxically, emphasizing the difference between these systems has also been the principle undergirding modern science’s claim to being a uniquely valid means of knowledge creation. Yet as each approach focused solely on contrasting Indigenous and scientific ways of knowing, potential similarities between these knowledge systems may have been ignored. One such oversight is the use of deception by each system, which is central to experimental research designs in the social and psychological sciences, and in American Indian …
A New Similarity-Based Multicriteria Recommendation Algorithm Based Onautoencoders, Zeynep Batmaz, Ci̇han Kaleli̇
A New Similarity-Based Multicriteria Recommendation Algorithm Based Onautoencoders, Zeynep Batmaz, Ci̇han Kaleli̇
Turkish Journal of Electrical Engineering and Computer Sciences
Recommender systems provide their users an efficient way to handle information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multicriteria recommender systems are an extension of traditional recommender systems that utilize multicriteria-based user preferences. Multicriteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be …
Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing
Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing
Research Collection School Of Computing and Information Systems
Recent studies have shown that strong Natural Language Understanding (NLU) models are prone to relying on annotation biases of the datasets as a shortcut, which goes against the underlying mechanisms of the task of interest. To reduce such biases, several recent works introduce debiasing methods to regularize the training process of targeted NLU models. In this paper, we provide a new perspective with causal inference to fnd out the bias. On the one hand, we show that there is an unobserved confounder for the natural language utterances and their respective classes, leading to spurious correlations from training data. To remove …
Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring …
Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt
Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt
Theses and Dissertations
Machine learning models that employ NLP techniques have become more widely accessible, making them an attractive solution for text and document classification tasks traditionally accomplished by humans. Two such use cases are matching the specialized experience required for a job to statements in applicant resumes, and finding and labelling clauses in legal contracts The AFMC has an immediate need for solutions to civilian hiring. However, there is currently no truth data to validate against. A similar task is contract understanding for which there is the CUAD, a recently published repository of 510 contracts manually labelled by legal experts. The presented …
Developing A Fake News Identification Model With Advanced Deep Languagetransformers For Turkish Covid-19 Misinformation Data, Mehmet Bozuyla, Akin Özçi̇ft
Developing A Fake News Identification Model With Advanced Deep Languagetransformers For Turkish Covid-19 Misinformation Data, Mehmet Bozuyla, Akin Özçi̇ft
Turkish Journal of Electrical Engineering and Computer Sciences
The massive use of social media causes rapid information dissemination that amplifies harmful messages such as fake news. Fake-news is misleading information presented as factual news that is generally used to manipulate public opinion. In particular, fake news related to COVID-19 is defined as 'infodemic' by World Health Organization. An infodemic is a misleading information that causes confusion which may harm health. There is a high volume of misinformation about COVID-19 that causes panic and high stress. Therefore, the importance of development of COVID-19 related fake news identification model is clear and it is particularly important for Turkish language from …
Event-Related Microblog Retrieval In Turkish, Çağri Toraman
Event-Related Microblog Retrieval In Turkish, Çağri Toraman
Turkish Journal of Electrical Engineering and Computer Sciences
Microblogs, such as tweets, are short messages in which users are able to share any opinion and information. Microblogs are mostly related to real-life events reported in news articles. Finding event-related microblogs is important to analyze online social networks and understand public opinion on events. However, finding such microblogs is a challenging task due to the dynamic nature of microblogs and their limited length. In this study, assuming that news articles are given as queries and microblogs as documents, we find event-related microblogs in Turkish. In order to represent news articles and microblogs, we examine encoding methods, namely traditional bag-of-words …
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Duplicate record, see https://ink.library.smu.edu.sg/sis_research/7680/. Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked solely on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple …
Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
Research Collection School Of Computing and Information Systems
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works propose diverse approaches to learn the program semantics for different tasks and these works have achieved state-of-the-art performance. However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed. From this starting point, in this paper, we conduct an empirical study to evaluate different program representation techniques. Specifically, we categorize current mainstream code representation techniques into four categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based program representation technique and …
Can Identifier Splitting Improve Open-Vocabulary Language Model Of Code?, Jieke Shi, Zhou Yang, Junda He, Bowen Xu, David Lo
Can Identifier Splitting Improve Open-Vocabulary Language Model Of Code?, Jieke Shi, Zhou Yang, Junda He, Bowen Xu, David Lo
Research Collection School Of Computing and Information Systems
Statistical language models on source code have successfully assisted software engineering tasks. However, developers can create or pick arbitrary identifiers when writing source code. Freely chosen identifiers lead to the notorious out-of-vocabulary (OOV) problem that negatively affects model performance. Recently, Karampatsis et al. showed that using the Byte Pair Encoding (BPE) algorithm to address the OOV problem can improve the language models’ predictive performance on source code. However, a drawback of BPE is that it cannot split the identifiers in a way that preserves the meaningful semantics. Prior researchers also show that splitting compound identifiers into sub-words that reflect the …
Cybersecurity Risk In U.S. Critical Infrastructure: An Analysis Of Publicly Available U.S. Government Alerts And Advisories, Zachary Lanz
Cybersecurity Risk In U.S. Critical Infrastructure: An Analysis Of Publicly Available U.S. Government Alerts And Advisories, Zachary Lanz
International Journal of Cybersecurity Intelligence & Cybercrime
As threat actor operations become increasingly sophisticated and emphasize the targeting of critical infrastructure and services, the need for cybersecurity information sharing will continue to grow. Escalating demand for cyber threat intelligence and information sharing across the cybersecurity community has resulted in the need to better understand the information produced by reputable sources such as U.S. CISA Alerts and ICS-CERT advisories. The text analysis program, Profiler Plus, is used to extract information from 1,574 U.S. government alerts and advisories to develop visualizations and generate enhanced insights into different cyber threat actor types, the tactics which can be used for cyber …
Directional Pairwise Class Confusion Bias And Its Mitigation, Sudhashree Sayenju, Ramazan Aygun Phd, Jonathan Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang Phd, Bill Franks, Sereres Johnston Phd, George Lee, Dan Sullivan, Girish Modgil Phd
Directional Pairwise Class Confusion Bias And Its Mitigation, Sudhashree Sayenju, Ramazan Aygun Phd, Jonathan Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang Phd, Bill Franks, Sereres Johnston Phd, George Lee, Dan Sullivan, Girish Modgil Phd
Published and Grey Literature from PhD Candidates
Recent advances in Natural Language Processing have led to powerful and sophisticated models like BERT (Bidirectional Encoder Representations from Transformers) that have bias. These models are mostly trained on text corpora that deviate in important ways from the text encountered by a chatbot in a problem-specific context. While a lot of research in the past has focused on measuring and mitigating bias with respect to protected attributes (stereotyping like gender, race, ethnicity, etc.), there is lack of research in model bias with respect to classification labels. We investigate whether a classification model hugely favors one class with respect to another. …
Classification And Keyword Identification Of Covid 19 Misinformation On Social Media: A Framework For Semantic Analysis, Grace Y. Smith
Classification And Keyword Identification Of Covid 19 Misinformation On Social Media: A Framework For Semantic Analysis, Grace Y. Smith
Theses and Dissertations
The growing surge of misinformation among COVID-19 communication can pose great hindrance to truth, magnify distrust in policy makers and/or degrade authorities’ credibility, and it can even harm public health. Classification of textual context on social media data relating to COVID-19 is an effective tool to combat misinformation on social media platforms. In this research, Twitter data was leveraged to 1) develop classification methods to detect misinformation and identify Tweet sentiment with respect to COVID-19 and 2) develop a human-in-the-loop interactive framework to enable identification of keywords associated with social context, here, being misinformation regarding COVID-19. 1) Six fusion-based classification …
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learning-based MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these …
Aspect-Based Api Review Classification: How Far Can Pre-Trained Transformer Model Go?, Chengran Yang, Bowen Xu, Junaed Younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, David Lo
Aspect-Based Api Review Classification: How Far Can Pre-Trained Transformer Model Go?, Chengran Yang, Bowen Xu, Junaed Younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, David Lo
Research Collection School Of Computing and Information Systems
APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the …
Women In Stem Fellowship: An Intersectional And Interdisciplinary Approach To Advancing Inclusion In The Sciences, Ina C. Seethaler, Gabriela Perez-Alvarado
Women In Stem Fellowship: An Intersectional And Interdisciplinary Approach To Advancing Inclusion In The Sciences, Ina C. Seethaler, Gabriela Perez-Alvarado
Journal of the South Carolina Academy of Science
At our university, women-identified individuals make up 23% of students in STEM fields; less than 15% of them graduate with a STEM degree. Nationally, more than 40% of women who enter a STEM job leave it within fewer than ten years. Gendered issues within STEM industries have been identified, yet we are far from equal opportunities for all genders. In 2018, we—the director of Women’s and Gender Studies (WGS) with colleagues in Math, Computing Sciences, and Chemistry—received a $45,000 grant to create a “Women in STEM Fellowship.” The inclusion of WGS made the fellowship interdisciplinary, intersectional, and informed our decision-making …
Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Publications
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating …
Learning Temporal Rules From Noisy Timeseries Data, Karan Samel, Zelin Zao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan A. Essa, Le Song
Learning Temporal Rules From Noisy Timeseries Data, Karan Samel, Zelin Zao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan A. Essa, Le Song
Machine Learning Faculty Publications
Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose higher level composite events. Examples of a composite event are a patient's medical symptom or a baseball player hitting a home run, caused distinct temporal orderings of patient vitals and player movements respectively. Such salient composite events are provided as labels in temporal datasets and most works optimize models to predict these composite event labels directly. We focus on uncovering the underlying atomic events and their relations that lead to the composite events within …
What Tourists Want, A Sustainable Paradise, Catherine Linnes, Jerome Agrusa, Giulio Ronzoni, Joseph Lema
What Tourists Want, A Sustainable Paradise, Catherine Linnes, Jerome Agrusa, Giulio Ronzoni, Joseph Lema
Hospitality Faculty Research
The increasing complexity of tourism and sustainability offers opportunities and challenges among diverse stakeholder perspectives. The need for sustainable and nature-based approaches exists throughout the growing body of literature from among a number of dimensions and measures. One of the overarching goals of the paper is to examine whether tourists will choose a destination or hotel that is actively working to improve the environment while examining how Hawaii’s tourism is nature-based as well as other measures of sustainability while enjoying a slice of paradise in the Hawaiian Islands. This study explores tourism sustainability concerns in Hawaii, such as the influence …
An Equity-First Approach To Climate Adaptation, San Diego Regional Climate Collaborative, San Diego Association Of Governments
An Equity-First Approach To Climate Adaptation, San Diego Regional Climate Collaborative, San Diego Association Of Governments
San Diego Regional Climate Collaborative
The Equity-First Approach to Climate Adaptation guidance document (Equity Guidance; document) serves as a general framework for local agency planners and transportation planning professionals. The Equity Guidance’s overall goal is to provide best practices for designing, planning, and implementing a climate adaptation process that supports equitable outcomes. This document identifies trends, assumptions, emerging practices, indicators, metrics, and implementation opportunities, that support and operationalize an equitable climate adaptation planning process.
Provably Weak Instances Of Plwe Revisited, Again, Katherine Mendel
Provably Weak Instances Of Plwe Revisited, Again, Katherine Mendel
CSB and SJU Distinguished Thesis
Learning with Errors has emerged as a promising possibility for postquantum cryptography. Variants known as RLWE and PLWE have been shown to be more efficient, but the increased structure can leave them vulnerable to attacks for certain instantiations. This work aims to identify specific cases where proposed cryptographic schemes based on PLWE work particularly poorly under a specific attack.
Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Context: In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. Objective: Towards tackling the challenge, we conduct a study on analyzing user concerns about in-app advertisement. Method: Specifically, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues …
Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan
Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan
Research Collection School Of Computing and Information Systems
The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. …
Wrong Way, Joseph Chaney
A Cognitive View Of "Pandemic Meditation" (A Mathematical Visual Poem), Kazmier Maslanka
A Cognitive View Of "Pandemic Meditation" (A Mathematical Visual Poem), Kazmier Maslanka
Journal of Humanistic Mathematics
Mathematical visual poetry is a poetic genre whereby metaphorical expressions are created using mathematical structures. Within the structure, the poetics are understood by the cross-mapping of numerous conceptual domains including visual, lexical, and mathematical. Here I focus on one particular mathematical visual poetic structure: what I call a Similar Triangles Poem or Proportional Poem. To illustrate the ideas discussed, I present "Pandemic Meditations," a mathematical visual poem; in particular I discuss how this mathematical poem uses the mechanisms of poetic metaphor in the context of the embodied mind. The intent of this paper is not to explain "Pandemic Meditation," …