Computer Sciences Commons^™

Acknowledgement Entity Recognition In Cord-19 Papers, Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, Christopher Griffin

Computer Science Faculty Publications

Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F₁=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to …

Opening Books And The National Corpus Of Graduate Research, William A. Ingram, Edward A. Fox, Jian Wu

Computer Science Faculty Publications

Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. …

Blockchain Based Access Control For Enterprise Blockchain Applications, Lei Xu, Isaac Markus, Subhod I, Nikhil Nayab

Computer Science Faculty Publications

Access control is one of the fundamental security mechanisms of IT systems. Most existing access control schemes rely on a centralized party to manage and enforce access control policies. As blockchain technologies, especially permissioned networks, find more applicability beyond cryptocurrencies in enterprise solutions, it is expected that the security requirements will increase. Therefore, it is necessary to develop an access control system that works in a decentralized environment without compromising the unique features of a blockchain. A straightforward method to support access control is to deploy a firewall in front of the enterprise blockchain application. However, this approach does not …

Kcrs: A Blockchain-Based Key Compromise Resilient Signature System, Lei Xu, Lin Chen, Zhimin Gao, Xinxin Fan, Kimberly Doan, Shouhuai Xu, Weidong Shi

Computer Science Faculty Publications

Digital signatures are widely used to assure authenticity and integrity of messages (including blockchain transactions). This assurance is based on assumption that the private signing key is kept secret, which may be exposed or compromised without being detected in the real world. Many schemes have been proposed to mitigate this problem, but most schemes are not compatible with widely used digital signature standards and do not help detect private key exposures. In this paper, we propose a Key Compromise Resilient Signature (KCRS) system, which leverages blockchain to detect key compromises and mitigate the consequences. Our solution keeps a log of …

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

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 …

An Accelerated Hierarchical Approach For Object Shape Extraction And Recognition, Mahmoud K. Quweider, Bassam Arshad, Hansheng Lei, Liyu Zhang, Fitratullah Khan

Computer Science Faculty Publications

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and …

Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

Computer Science Faculty Publications

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for …

Comparing Physical And Simulated Performance Of A Deterministic And A Bio-Inspired Stochastic Foraging Strategy For Robot Swarms, Qi Lu, Antonio D. Griego, G. Matthew Fricke, Melanie E. Moses

Computer Science Faculty Publications

Designing resource-collection algorithms for relatively simple robots that are effective given the noise and uncertainty of the real world is a challenge in swarm robotics. This paper describes the performance of two algorithms for collective robot foraging: the stochastic central-place foraging algorithm (CPFA) and the distributed deterministic spiral algorithm (DDSA). With the CPFA, robots mimic the foraging behaviors of ants; they stochastically search for targets and share information to recruit other robots to locations where they detect multiple targets. With the DDSA, robots travel along pre-planned spiral paths; robots detect the nearest targets first and, in theory, guarantee eventual complete …

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.

Safedb: Spark Acceleration On Fpga Clouds With Enclaved Data Processing And Bitstream Protection, Han-Yee Kim, Rohyoung Myung, Boeui Hong, Heonchang Yu, Taeweon Suh, Lei Xu, Weidong Shi

Computer Science Faculty Publications

This paper proposes SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. SafeDB provides a comprehensive and systematic hardware-based security framework from the bitstream protection to data confidentiality, especially for the cloud environment. The AES key shared between FPGA and client for the bitstream encryption is generated in hard-wired logic using PKI and ECC. The data security is assured by the enclaved processing with encrypted data, meaning that the encrypted data is processed inside the FPGA fabric. Thus, no one in the system is able to look into clients' data because plaintext data are not exposed …

A Novel Iot Switching Model Based On Cloud-Centric Rtdbs, Sheikh Muhammad Farjad

Computer Science Faculty Publications

INTRODUCTION: Over the past decade, a significant rise in the number of internet-connected devices was observed. The number of smart devices is continuously increasing. The expanding network of interconnected devices provided an impetus for rapid advancement in the domain of Internet-of-Things (IoT). IoT has recently become a centre of attraction for research community. The development of microprocessors and microcontrollers has supplemented advancement in IoT paradigm. The rapidly emerging paradigm of IoT is facing challenges which render significant impact on its performance and cost. OBJECTIVES: In this paper, a novel architecture based on Real-Time Database System (RTDBS) coupled with cloud functionalities …

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.

A Novel Method For Intelligent Single Fault Detection Of Bearings Using Sae And Improved D–S Evidence Theory, Jianguang Lu, Huan Zhang, Xianghong Tang

Computer Science Faculty Publications

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, …

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

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 …

A Holistic Approach For Enhancing Distributed Education With Multi-Campus Course Delivery Methods, Mahmoud K. Quweider, Ala Qubbaj, Liyu Zhang, Fitratullah Khan, Hansheng Lei

Computer Science Faculty Publications

To create an emerging research institution, a regional university was created that spans multiple campuses within a radius of more than one hundred miles by merging at least three current institutions. The merge allowed the university to pool its human and technical resources. Students can now pursue new degrees that were not available before at one campus or another, take a newly available technical or specialty courses, and even select their own preferred professor when a course is offered by many faculty. In order to serve students at multiple campuses that are geographically far a part, the university instituted policies …

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

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.

Applying The Fahp To Improve The Performance Evaluation Reliability Of Software Defect Classifiers, Hussam Ghunaim Ph.D., Julius Dichter

Computer Science Faculty Publications

Today’s software complexity makes developing defect-free software almost impossible. Consequently, developing classifiers to classify software modules into defective and non-defective before software releases have attracted great interest in academia and software industry alike. Although many classifiers have been proposed, no one has been proven superior over others. The major reason is that while a research shows that classifier A is better than classifier B, we can find other research that shows the opposite. These conflicts are usually triggered when researchers report results using their preferable performance evaluation measures such as, recall and precision. Although this approach is valid, it does …

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 …

Optimal Staged Self-Assembly Of Linear Assemblies, Cameron Chalk, Eric M. Martinez, Robert Schweller, Luis Vega, Andrew Winslow, Tim Wylie

Computer Science Faculty Publications

We analyze the complexity of building linear assemblies, sets of linear assemblies, and O(1)-scale general shapes in the staged tile assembly model. For systems with at most b bins and t tile types, we prove that the minimum number of stages to uniquely assemble a 1 n line is (logt n + logb n t + 1). Generalizing to O(1) n lines, we prove the minimum number of stages is O( log n tb t log t b2 + log log b log t ) and

( log n tb t log t b2 ). Next, we consider assembling sets …

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

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

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

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

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 …

A New Way To Detect Cyberattacks Extracting Changes In Register Values From Radio-Frequency Side Channels, Ronald A. Riley, James T. Graham, Ryan M. Fuller, Rusty O. Baldwin, Ashwin Fisher

Computer Science Faculty Publications

The Internet of Things (IoT) and the Internet of Everything (IoE) have driven processors into nearly every powered de- vice, from thermostats to refrigerators to light bulbs. From a security perspective, the IoT and IoE create a new layer of sig- nals and systems that can provide insight into the internal opera- tions of a device via analog side channels. Our research focuses on leveraging these analog side channels in IoT/IoE processors to detect intrusions. Our goal is to defend against cyberattacks that insert malware into IoT devices by detecting deviations in the code running on their processors from known …

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.

Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica Wiredja, Daniela Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, …