24 research outputs found
TransParsCit: A Transformer-Based Citation Parser Trained on Large-Scale Synthesized Data
Accurately parsing citation strings is key to automatically building large-scale citation graphs, so a robust citation parser is an essential module in academic search engines. One limitation of the state-of-the-art models (such as ParsCit and Neural-ParsCit) is the lack of a large-scale training corpus. Manually annotating hundreds of thousands of citation strings is laborious and time-consuming. This thesis presents a novel transformer-based citation parser by leveraging the GIANT dataset, consisting of 1 billion synthesized citation strings covering over 1500 citation styles. As opposed to handcrafted features, our model benefits from word embeddings and character-based embeddings by combining the bidirectional long shortterm memory (BiLSTM) with the Transformer and Conditional Random Forest (CRF). We varied the training data size from 500 to 1M and investigated the impact of training size on the performance. We evaluated our models on standard CORA benchmark and observed an increase in F1-score as the training size increased. The best performance happened when the training size was around 220K, achieving an F1-score of up to 100% on key citation fields. To our best knowledge, this is the first citation parser trained on a largescale synthesized dataset. Project codes and documentation can be found on this GitHub repository: https://github.com/lamps-lab/Citation-Parser
Improving Multi-Touch Interactions Using Hands as Landmarks
Efficient command selection is just as important for multi-touch devices as it is for traditional interfaces that follow the Windows-Icons-Menus-Pointers (WIMP) model, but rapid selection in touch interfaces can be difficult because these systems often lack the mechanisms that have been used for expert shortcuts in desktop systems (such as keyboards shortcuts). Although interaction techniques based on spatial memory can improve the situation by allowing fast revisitation from memory, the lack of landmarks often makes it hard to remember command locations in a large set. One potential landmark that could be used in touch interfaces, however, is peopleâs hands and fingers: these provide an external reference frame that is well known and always present when interacting with a touch display. To explore the use of hands as landmarks for improving command selection, we designed hand-centric techniques called HandMark menus. We implemented HandMark menus for two platforms â one version that allows bimanual operation for digital tables and another that uses single-handed serial operation for handheld tablets; in addition, we developed variants for both platforms that support different numbers of commands. We tested the new techniques against standard selection methods including tabbed menus and popup toolbars. The results of the studies show that HandMark menus perform well (in several cases significantly faster than standard methods), and that they support the development of spatial memory. Overall, this thesis demonstrates that peopleâs intimate knowledge of their hands can be the basis for fast interaction techniques that improve performance and usability of multi-touch systems
Use of Landmarks to Improve Spatial Learning and Revisitation in Computer Interfaces
Efficient spatial location learning and remembering are just as important for two-dimensional Graphical User Interfaces (GUI) as they are for real environments where locations are revisited multiple times. Rapid spatial memory development in GUIs, however, can be difficult because these interfaces often lack adequate landmarks that have been predominantly used by people to learn and recall real-life locations. In the absence of sufficient landmarks in GUIs, artificially created visual objects (i.e., artificial landmarks) could be used as landmarks to support spatial memory development of spatial locations. In order to understand how spatial memory development occurs in GUIs and explore ways to assist usersâ efficient location learning and recalling in GUIs, I carried out five studies exploring the use of landmarks in GUIs â one study that investigated interfaces of four standard desktop applications: Microsoft Word, Facebook, Adobe Photoshop, and Adobe Reader, and other four that tested artificial landmarks augmented two prototype desktop GUIs against non-landmarked versions: command selection interfaces and linear document viewers; in addition, I tested landmarksâ use in variants of these interfaces that varied in the number of command sets (small, medium, and large) and types of linear documents (textual and video). Results indicate that GUIsâ existing features and design elements can be reliable landmarks in GUIs that provide spatial benefits similar to real environments. I also show that artificial landmarks can significantly improve spatial memory development of GUIs, allowing support for rapid spatial location learning and remembering in GUIs. Overall, this dissertation reveals that landmarks can be a valuable addition to graphical systems to improve the memorability and usability of GUIs
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City
In recent years, bikesharing systems have become increasingly popular as
affordable and sustainable micromobility solutions. Advanced mathematical
models such as machine learning are required to generate good forecasts for
bikeshare demand. To this end, this study proposes a machine learning modeling
framework to estimate hourly demand in a large-scale bikesharing system. Two
Extreme Gradient Boosting models were developed: one using data from before the
COVID-19 pandemic (March 2019 to February 2020) and the other using data from
during the pandemic (March 2020 to February 2021). Furthermore, a model
interpretation framework based on SHapley Additive exPlanations was
implemented. Based on the relative importance of the explanatory variables
considered in this study, share of female users and hour of day were the two
most important explanatory variables in both models. However, the month
variable had higher importance in the pandemic model than in the pre-pandemic
model
Characterizing User Behaviors in Open-Source Software User Forums: An Empirical Study
User forums of Open Source Software (OSS) enable end-users to collaboratively
discuss problems concerning the OSS applications. Despite decades of research
on OSS, we know very little about how end-users engage with OSS communities on
these forums, in particular, the challenges that hinder their continuous and
meaningful participation in the OSS community. Many previous works are
developer-centric and overlook the importance of end-user forums. As a result,
end-users' expectations are seldom reflected in OSS development. To better
understand user behaviors in OSS user forums, we carried out an empirical study
analyzing about 1.3 million posts from user forums of four popular OSS
applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the
contribution patterns of three common user types (end-users, developers, and
organizers), we observed that end-users not only initiated most of the threads
(above 96% of threads in three projects, 86% in the other), but also acted as
the significant contributors for responding to other users' posts, even though
they tended to lack confidence in their activities as indicated by
psycho-linguistic analyses. Moreover, we found end-users more open, reflecting
a more positive emotion in communication than organizers and developers in the
forums. Our work contributes new knowledge about end-users' activities and
behaviors in OSS user forums that the vital OSS stakeholders can leverage to
improve end-user engagement in the OSS development process.Comment: 15th International Conference on Cooperative and Human Aspects of
Softare Engineerin
ToSHI - Towards Secure Heterogeneous Integration: Security Risks, Threat Assessment, and Assurance
The semiconductor industry is entering a new age in which device scaling and cost reduction will no longer follow the decades-long pattern. Packing more transistors on a monolithic IC at each node becomes more difficult and expensive. Companies in the semiconductor industry are increasingly seeking technological solutions to close the gap and enhance cost-performance while providing more functionality through integration. Putting all of the operations on a single chip (known as a system on a chip, or SoC) presents several issues, including increased prices and greater design complexity. Heterogeneous integration (HI), which uses advanced packaging technology to merge components that might be designed and manufactured independently using the best process technology, is an attractive alternative. However, although the industry is motivated to move towards HI, many design and security challenges must be addressed. This paper presents a three-tier security approach for secure heterogeneous integration by investigating supply chain security risks, threats, and vulnerabilities at the chiplet, interposer, and system-in-package levels. Furthermore, various possible trust validation methods and attack mitigation were proposed for every level of heterogeneous integration. Finally, we shared our vision as a roadmap toward developing security solutions for a secure heterogeneous integration
f(R) theories
Over the past decade, f(R) theories have been extensively studied as one of
the simplest modifications to General Relativity. In this article we review
various applications of f(R) theories to cosmology and gravity - such as
inflation, dark energy, local gravity constraints, cosmological perturbations,
and spherically symmetric solutions in weak and strong gravitational
backgrounds. We present a number of ways to distinguish those theories from
General Relativity observationally and experimentally. We also discuss the
extension to other modified gravity theories such as Brans-Dicke theory and
Gauss-Bonnet gravity, and address models that can satisfy both cosmological and
local gravity constraints.Comment: 156 pages, 14 figures, Invited review article in Living Reviews in
Relativity, Published version, Comments are welcom
Biochemical and computational approach of selected phytocompounds from Tinospora crispa in the management of COVID-19
A pandemic caused by the novel coronavirus (SARS-CoV-2 or COVID-19) began in December 2019 in Wuhan, China, and the number of newly reported cases continues to increase. More than 19.7 million cases have been reported globally and about 728,000 have died as of this writing (10 August 2020). Recently, it has been confirmed that the SARS-CoV-2 main protease (Mpro) enzyme is responsible not only for viral reproduction but also impedes host immune responses. The Mpro provides a highly favorable pharmacological target for the discovery and design of inhibitors. Currently, no specific therapies are available, and investigations into the treatment of COVID-19 are lacking. Therefore, herein, we analyzed the bioactive phytocompounds isolated by gas chromatographyâmass spectroscopy (GC-MS) from Tinospora crispa as potential COVID-19 Mpro inhibitors, using molecular docking study. Our analyses unveiled that the top nine hits might serve as potential anti-SARS-CoV-2 lead molecules, with three of them exerting biological activity and warranting further optimization and drug development to combat COVID-19
A comprehensive review on global contributions and recognition of pharmacy professionals amidst COVID-19 pandemic: moving from present to future
Abstract Background COVID-19, a respiratory tract infection caused by SARS-CoV-2, is a burning question worldwide as it gives rise to a pandemic situation. No specific medications are still recommended for COVID-19; however, healthcare support is crucial for ameliorating the disease condition. Pharmacists are the frontline fighters who are responsible for providing healthcare support to the COVID-19 infected patients around the world. This review endeavored to briefly rationalize the contributions of several pharmacy professionals in diverse fields along with their collaborative efforts and dedication regarding their limitations during the COVID-19 situation and view the prospects of pharmaceutical care services in the post-pandemic period. Main body of the abstract Online databases were utilized to search for scholarly articles and organizational websites, to sum up the information about the contemporary and expanded role of pharmacists. Key articles were retrieved from Google Scholar, PubMed, and Science Direct databases using terms: âCOVID-19,â ânovel coronavirus,â âcommunity,â âindustrial,â âhospital,â âclinical,â ârecognition,â âobstacles,â âcollaboration,â âSARS-CoV-2,â âhealthcare,â and âoutbreakâ in combination with âpharmacist.â The articles were included from the inception of the pandemic to January 25, 2021. The current review found pharmacistâs global contributions and involvements with other professionals to provide healthcare services amidst COVID-19. This included testing of suspects, providing medical information, psycho-social support, debunking myths, mitigating drug shortage events, telemedicine, e-prescription, infection control, and controlling the drug supply chain. In many countries, pharmacistsâ activities were much appreciated but in some countries, they were not properly acknowledged for their contributions amidst COVID-19 outbreak. They played additional roles such as participating in the antimicrobial stewardship team, improving value-added services, conducting clinical data analysis to suppress the outspread of the SARS-CoV-2. Short conclusion During the COVID-19 pandemic while the whole world is fighting against an invisible virus, the pharmacists are the earnest hero to serve their responsibilities along with additional activities. They need to be prepared and collaborate with other healthcare professionals further to meet the challenges of post-pandemic circumstances
An Effective Ensemble Machine Learning Approach to Classify Breast Cancer Based on Feature Selection and Lesion Segmentation Using Preprocessed Mammograms
Background: Breast cancer, behind skin cancer, is the second most frequent malignancy among women, initiated by an unregulated cell division in breast tissues. Although early mammogram screening and treatment result in decreased mortality, differentiating cancer cells from surrounding tissues are often fallible, resulting in fallacious diagnosis. Method: The mammography dataset is used to categorize breast cancer into four classes with low computational complexity, introducing a feature extraction-based approach with machine learning (ML) algorithms. After artefact removal and the preprocessing of the mammograms, the dataset is augmented with seven augmentation techniques. The region of interest (ROI) is extracted by employing several algorithms including a dynamic thresholding method. Sixteen geometrical features are extracted from the ROI while eleven ML algorithms are investigated with these features. Three ensemble models are generated from these ML models employing the stacking method where the first ensemble model is built by stacking ML models with an accuracy of over 90% and the accuracy thresholds for generating the rest of the ensemble models are >95% and >96. Five feature selection methods with fourteen configurations are applied to notch up the performance. Results: The Random Forest Importance algorithm, with a threshold of 0.045, produces 10 features that acquired the highest performance with 98.05% test accuracy by stacking Random Forest and XGB classifier, having a higher than >96% accuracy. Furthermore, with K-fold cross-validation, consistent performance is observed across all K values ranging from 3–30. Moreover, the proposed strategy combining image processing, feature extraction and ML has a proven high accuracy in classifying breast cancer