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Articles 1 - 29 of 29
Full-Text Articles in Physical Sciences and Mathematics
Rating News Claims: Feature Selection And Evaluation, Izzat Alsmadi, Michael J. O'Brien
Rating News Claims: Feature Selection And Evaluation, Izzat Alsmadi, Michael J. O'Brien
Computer Science Faculty Publications
News claims that travel the Internet and online social networks (OSNs) originate from different, sometimes unknown sources, which raises issues related to the credibility of those claims and the drivers behind them. Fact-checking websites such as Snopes, FactCheck, and Emergent use human evaluators to investigate and label news claims, but the process is labor- and time-intensive. Driven by the need to use data analytics and algorithms in assessing the credibility of news claims, we focus on what can be generalized about evaluating human-labeled claims. We developed tools to extract claims from Snopes and Emergent and used public datasets collected by …
Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang
Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang
Computer Science Faculty Publications
Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated …
Generation Of Crowd Arrival And Destination Locations/Times In Complex Transit Facilities, Brian Ricks, Andrew Dobson, Athanasios Krontiris, Kostas Bekris, Mubbasir Kapadia, Fred Roberts
Generation Of Crowd Arrival And Destination Locations/Times In Complex Transit Facilities, Brian Ricks, Andrew Dobson, Athanasios Krontiris, Kostas Bekris, Mubbasir Kapadia, Fred Roberts
Computer Science Faculty Publications
In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm …
Ai Education Matters: Data Science And Machine Learning With Magic: The Gathering, Todd W. Neller
Ai Education Matters: Data Science And Machine Learning With Magic: The Gathering, Todd W. Neller
Computer Science Faculty Publications
In this column, we briefly describe a rich dataset with many opportunities for interesting data science and machine learning assignments and research projects, we take up a simple question, and we offer code illustrating use of the dataset in pursuit of answers to the question.
An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini
An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini
Computer Science Faculty Publications
We provide a brief introduction to CLIPS—a declarative/logic programming language for implementing expert systems—and PROLOG—a declarative/logic programming language based on first-order, predicate calculus. Unlike imperative languages in which the programmer specifies how to compute a solution to a problem, in a declarative language, the programmer specifies what they what to find, and the system uses a search strategy built into the language. We also briefly discuss applications of CLIPS and PROLOG.
Design And Pilot Testing Of Subgoal Labeled Worked Examples For Five Core Concepts In Cs1, Lauren E. Margulieux, Briana B. Morrison, Adrienne Decker
Design And Pilot Testing Of Subgoal Labeled Worked Examples For Five Core Concepts In Cs1, Lauren E. Margulieux, Briana B. Morrison, Adrienne Decker
Computer Science Faculty Publications
Subgoal learning has improved student problem-solving performance in programming, but it has been tested for only oneto-two hours of instruction at a time. Our work pioneers implementing subgoal learning throughout an entire introductory programming course. In this paper we discuss the protocol that we used to identify subgoals for core programming procedures, present the subgoal labels created for the course, and outline the subgoal-labeled instructional materials that were designed for a Java-based course. To examine the effect of subgoal labeled materials on student performance in the course, we compared quiz and exam grades between students who learned using subgoal labels …
Influence Spread In Two-Layer Interdependent Networks: Designed Single-Layer Or Random Two-Layer Initial Spreaders?, Hana Khamfroush, Nathaniel Hudson, Samuel Iloo, Mahshid R. Naeini
Influence Spread In Two-Layer Interdependent Networks: Designed Single-Layer Or Random Two-Layer Initial Spreaders?, Hana Khamfroush, Nathaniel Hudson, Samuel Iloo, Mahshid R. Naeini
Computer Science Faculty Publications
Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model …
Mementomap: An Archive Profile Dissemination Framework, Sawood Alam, Michele C. Weigle, Michael L. Nelson
Mementomap: An Archive Profile Dissemination Framework, Sawood Alam, Michele C. Weigle, Michael L. Nelson
Computer Science Faculty Publications
We introduce MementoMap, a framework to express and disseminate holdings of web archives (archive profiles) by themselves or third parties. The framework allows arbitrary, flexible, and dynamic levels of details in its entries that fit the needs of archives of different scales. This enables Memento aggregators to significantly reduce wasted traffic to web archives.
Impact Of Http Cookie Violations In Web Archives, Sawood Alam, Michele C. Weigle, Michael L. Nelson
Impact Of Http Cookie Violations In Web Archives, Sawood Alam, Michele C. Weigle, Michael L. Nelson
Computer Science Faculty Publications
Certain HTTP Cookies on certain sites can be a source of content bias in archival crawls. Accommodating Cookies at crawl time, but not utilizing them at replay time may cause cookie violations, resulting in defaced composite mementos that never existed on the live web. To address these issues, we propose that crawlers store Cookies with short expiration time and archival replay systems account for values in the Vary header along with URIs.
From Invisibility To Readability: Recovering The Ink Of Herculaneum, Clifford Seth Parker, Stephen Parsons, Jack Bandy, Christy Chapman, Frederik Coppens, William Brent Seales
From Invisibility To Readability: Recovering The Ink Of Herculaneum, Clifford Seth Parker, Stephen Parsons, Jack Bandy, Christy Chapman, Frederik Coppens, William Brent Seales
Computer Science Faculty Publications
The noninvasive digital restoration of ancient texts written in carbon black ink and hidden inside artifacts has proven elusive, even with advanced imaging techniques like x-ray-based micro-computed tomography (micro-CT). This paper identifies a crucial mistaken assumption: that micro-CT data fails to capture any information representing the presence of carbon ink. Instead, we show new experiments indicating a subtle but detectable signature from carbon ink in micro-CT. We demonstrate a new computational approach that captures, enhances, and makes visible the characteristic signature created by carbon ink in micro-CT. This previously "unseen" evidence of carbon inks, which can now successfully be made …
A Communication Architecture For Crowd Management In Emergency And Disruptive Scenarios, Alfredo J. Perez, Sherali Zeadally
A Communication Architecture For Crowd Management In Emergency And Disruptive Scenarios, Alfredo J. Perez, Sherali Zeadally
Computer Science Faculty Publications
Crowd management aims to develop support infrastructures that can effectively manage crowds at any time. In emergency and disruptive scenarios this concept can minimize the risk to human life and to the infrastructure. We propose the Communication Architecture for Crowd Management (CACROM), which can support crowd management under emergency and disruptive scenarios. We identify, describe, and discuss the various components of the proposed architecture, and we briefly discuss open challenges in the design of crowd management systems for emergency and disruptive scenarios.
A Generative Human-Robot Motion Retargeting Approach Using A Single Rgbd Sensor, Sen Wang, Xinxin Zuo, Runxiao Wang, Ruigang Yang
A Generative Human-Robot Motion Retargeting Approach Using A Single Rgbd Sensor, Sen Wang, Xinxin Zuo, Runxiao Wang, Ruigang Yang
Computer Science Faculty Publications
The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified …
Budget Magic: The Gathering For Beginners, Todd W. Neller
Budget Magic: The Gathering For Beginners, Todd W. Neller
Computer Science Faculty Publications
In this talk, Neller overviewed budget-friendly entry points to playing Magic: The Gathering (M:TG) after its first quarter-century of success. Noting the ways in which M:TG players have applied head-designer Mark Rosewater’s “restrictions breed creativity” lesson, he celebrated their creative formats that push back against expensive “pay to win” dynamics.
Developing A Contemporary And Innovative Operating Systems Course, Saverio Perugini, David J. Wright
Developing A Contemporary And Innovative Operating Systems Course, Saverio Perugini, David J. Wright
Computer Science Faculty Publications
This birds-of-a-feather provides a discussion forum to foster innovation in teaching operating systems (os) at the undergraduate level. This birds-of-a-feather seeks to generate discussion and ideas around pedagogy for os and, in particular, how we might develop a contemporary and innovative model, in both content and delivery, for an os course—that plays a central role in a cs curriculum—and addresses significant issues of misalignment between existing os courses and employee professional skills and knowledge requirements. We would like to exchange ideas regarding a re-conceptualized course model of os curriculum and related pedagogy, especially in the areas of mobile OSs and …
An Interactive, Graphical Simulator For Teaching Operating Systems, Joshua W. Buck, Saverio Perugini
An Interactive, Graphical Simulator For Teaching Operating Systems, Joshua W. Buck, Saverio Perugini
Computer Science Faculty Publications
We demonstrate a graphical simulation tool for visually and interactively exploring the processing of a variety of events handled by an operating system when running a program. Our graphical simulator is available for use on the web by both instructors and students for purposes of pedagogy. Instructors can use it for live demonstrations of course concepts in class, while students can use it outside of class to explore the concepts. The graphical simulation tool is implemented using the React library for the fancy ui elements of the Node.js framework and is available as a web application at https://cpudemo.azurewebsites.net. The goals …
Kaggle And Click-Through Rate Prediction, Todd W. Neller
Kaggle And Click-Through Rate Prediction, Todd W. Neller
Computer Science Faculty Publications
Neller presented a look at Kaggle.com, an online Data Science and Machine Learning learning community, as a place to seek rapid, experiential peer education for most any Data Science topic. Using the specific challenge of Click-Through Rate Prediction (CTRP), he focused on lessons learned from relevant Kaggle competitions on how to perform CTRP.
Overview Of The Biocreative Vi Precision Medicine Track: Mining Protein Interactions And Mutations For Precision Medicine, Rezarta Islamaj Doğan, Sun Kim, Andrew Chatr-Aryamontri, Chih-Hsuan Wei, Donald C. Comeau, Rui Antunes, Sérgio Matos, Qingyu Chen, Aparna Elangovan, Nagesh C. Panyam, Karin Verspoor, Hongfang Liu, Yanshan Wang, Zhuang Liu, Berna Altınel, Zehra Melce Hüsünbeyi, Arzucan Özgür, Aris Fergadis, Chen-Kai Wang, Hong-Jie Dai, Tung Tran, Ramakanth Kavuluru, Ling Luo, Albert Steppi, Jinfeng Zhang, Jinchan Qu, Zhiyong Lu
Overview Of The Biocreative Vi Precision Medicine Track: Mining Protein Interactions And Mutations For Precision Medicine, Rezarta Islamaj Doğan, Sun Kim, Andrew Chatr-Aryamontri, Chih-Hsuan Wei, Donald C. Comeau, Rui Antunes, Sérgio Matos, Qingyu Chen, Aparna Elangovan, Nagesh C. Panyam, Karin Verspoor, Hongfang Liu, Yanshan Wang, Zhuang Liu, Berna Altınel, Zehra Melce Hüsünbeyi, Arzucan Özgür, Aris Fergadis, Chen-Kai Wang, Hong-Jie Dai, Tung Tran, Ramakanth Kavuluru, Ling Luo, Albert Steppi, Jinfeng Zhang, Jinchan Qu, Zhiyong Lu
Computer Science Faculty Publications
The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and …
Getting Things Done For The Glory Of God, Todd W. Neller
Getting Things Done For The Glory Of God, Todd W. Neller
Computer Science Faculty Publications
The seminar covered a fusion of David Allen’s Getting Things Done; Covey, Merrill and Merrill’s First Things First; and Matt Perman’s What’s Best Next books on time management, with a view to being a good steward of time and effort for the glory of God. More information is available at http://cs.gettysburg.edu/~tneller/resources/gtd/index.html
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Computer Science Faculty Publications
Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library …
A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna
A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions …
Clinical Big Data And Deep Learning: Applications, Challenges, And Future Outlooks, Ying Yu, Liangliang Liu, Yaohang Li, Jianxin Wang
Clinical Big Data And Deep Learning: Applications, Challenges, And Future Outlooks, Ying Yu, Liangliang Liu, Yaohang Li, Jianxin Wang
Computer Science Faculty Publications
The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details …
Computational Thinking Bins: Outreach And More, Briana B. Morrison, Brian Dorn, Michelle Friend
Computational Thinking Bins: Outreach And More, Briana B. Morrison, Brian Dorn, Michelle Friend
Computer Science Faculty Publications
Computational Thinking Bins are stand alone, individual boxes, each containing an activity for groups of students that teaches a computing concept. We have a devised a system that has allowed us to create an initial set, test the set, continually improve and add to our set. We currently use these bins in outreach events for middle and high school students. As we have shared this resource with K-12 teachers, many have expressed an interest in acquiring their own set. In this paper we will share our experience throughout the process, introduce the bins, and explain how you can create your …
Ieee Access Special Section Editorial: Wirelessly Powered Networks, And Technologies, Theofanis P. Raptis, Nuno B. Carvalho, Diego Masotti, Lei Shu, Cong Wang, Yuanyuan Yang
Ieee Access Special Section Editorial: Wirelessly Powered Networks, And Technologies, Theofanis P. Raptis, Nuno B. Carvalho, Diego Masotti, Lei Shu, Cong Wang, Yuanyuan Yang
Computer Science Faculty Publications
Wireless Power Transfer (WPT) is, by definition, a process that occurs in any system where electrical energy is transmitted from a power source to a load without the connection of electrical conductors. WPT is the driving technology that will enable the next stage in the current consumer electronics revolution, including battery-less sensors, passive RF identification (RFID), passive wireless sensors, the Internet of Things and 5G, and machine-to-machine solutions. WPT-enabled devices can be powered by harvesting energy from the surroundings, including electromagnetic (EM) energy, leading to a new communication networks paradigm, the Wirelessly Powered Networks.
Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang
Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang
Computer Science Faculty Publications
Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, …
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Computer Science Faculty Publications
We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …
Web Archives At The Nexus Of Good Fakes And Flawed Originals, Michael L. Nelson
Web Archives At The Nexus Of Good Fakes And Flawed Originals, Michael L. Nelson
Computer Science Faculty Publications
[Summary] The authenticity, integrity, and provenance of resources we encounter on the web are increasingly in question. While many people are inured to the possibility of altered images, the easy accessibility of powerful software tools that synthesize audio and video will unleash a torrent of convincing “deepfakes” into our social discourse. Archives will no longer be monopolized by a countable number of institutions such as governments and publishers, but will become a competitive space filled with social engineers, propagandists, conspiracy theorists, and aspiring Hollywood directors. While the historical record has never been singular nor unmalleable, current technologies empower an unprecedented …
Eeg-Based Processing And Classification Methodologies For Autism Spectrum Disorder: A Review, Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna
Eeg-Based Processing And Classification Methodologies For Autism Spectrum Disorder: A Review, Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents …
Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya
Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya
Computer Science Faculty Publications
Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …
Multi-View Cluster Analysis With Incomplete Data To Understand Treatment Effects, Guoqing Chao, Jiangwen Sun, Jin Lu, An-Li Wang, Daniel D. Langleben, Chiang-Shan Li, Jinbo Bi
Multi-View Cluster Analysis With Incomplete Data To Understand Treatment Effects, Guoqing Chao, Jiangwen Sun, Jin Lu, An-Li Wang, Daniel D. Langleben, Chiang-Shan Li, Jinbo Bi
Computer Science Faculty Publications
Multi-view cluster analysis, as a popular granular computing method, aims to partition sample subjects into consistent clusters across different views in which the subjects are characterized. Frequently, data entries can be missing from some of the views. The latest multi-view co-clustering methods cannot effectively deal with incomplete data, especially when there are mixed patterns of missing values. We propose an enhanced formulation for a family of multi-view co-clustering methods to cope with the missing data problem by introducing an indicator matrix whose elements indicate which data entries are observed and assessing cluster validity only on observed entries. In comparison with …