Computer Sciences Commons^™

Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming C. Leu, Xiaoqing Frank Liu, Md Rakib Shahriar, S M Nahian Al Sunny

Computer Science and Computer Engineering Faculty Publications and Presentations

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model …

Towards A Physio-Cognitive Model Of Slow-Breathing, Chris Dancy

Faculty Conference Papers and Presentations

How may controlled breathing be beneficial, or detrimental to behavior? Computational process models are useful to specify the potential mechanisms that lead to behavioral adaptation during different breathing exercises. We present a physio-cognitive model of slow breathing implemented within a hybrid cognitive architecture, ACT-R/Φ. Comparisons to data from an experiment indicate that the physiological mechanisms are operating in a manner that is consistent with actual human function. The presented computational model provides predictions of ways that controlled breathing interacts with mechanisms of arousal to mediate cognitive behavior. The increasing use of breathing techniques to counteract effects of stressors makes it …

Towards A Physio-Cognitive Model Of The Exploration Exploitation Trade-Off., David M. Schwartz, Christopher L. Dancy

Faculty Conference Papers and Presentations

Managing the exploration vs exploitation trade-off is an important part of our everyday lives. It occurs in minor decisions such as choosing what music to listen to as well as major decisions, such as picking a research direction to pursue. The dilemma is the same despite the context: does one exploit the environment, using current knowledge to acquire a satisfactory solution, or explore other options and potentially find a better answer. An accurate cognitive model must be able to handle this trade-off because of the importance it plays in our lives. We are developing physio-cognitive models to better understand how …

Simulating Human-Ai Collaboration With Act-R And Project Malmo, Zachary M. Brill, Christopher L. Dancy

Faculty Conference Papers and Presentations

We use the ACT-R cognitive architecture (Anderson, 2007) to explore human-AI collaboration. Computational models of human and AI behavior, and their interaction, allow for more effective development of collaborative artificial intelligent agents. With these computational models and simulations, one may be better equipped to predict the situations in which certain classes of intelligent agents may be more suited to collaborate with people. One can more tractably understand and predict how different AI agents affect task behavior in these situations. To simulate human-AI collaboration, we are developing ACT-R models that work with more traditional AI agents to solve a task in …

Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth

Computer Science Faculty Publications

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller

Computer Science Faculty Publications

In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]

Motivations, Team Dynamics, Development Practices And How They Impact The Success Of Open Source Software: A Study Of Projects Of Code For America Brigades, Le Chang

Electronic Theses and Dissertations

Open data movement has nurtured the growth of civic open source software (OSS) in the recent decade. This emerging phenomenon has demonstrated a way that a community can collectively utilize technology to solve its problems.

This study is based on software projects in brigades of Code for America, which is a network of organizations that group volunteers to create digital solutions to community problems. In this study, we analyze the software engineering practices of current civic open source software development, participants' motivations and perceptions of the projects, and provide insights on the antecedents of success of the application development.

A …

Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller

Computer Science Faculty Publications

In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]

Towards High-Performance Big Data Processing Systems, Hong Zhang

Electronic Theses and Dissertations

The amount of generated and stored data has been growing rapidly, It is estimated that 2.5 quintillion bytes of data are generated every day, and 90% of the data in the world today has been created in the last two years. How to solve these big data issues has become a hot topic in both industry and academia. Due to the complex of big data platform, we stratify it into four layers: storage layer, resource management layer, computing layer, and methodology layer. This dissertation proposes brand-new approaches to address the performance of big data platforms like Hadoop and Spark on …

Assessment Of Information Security Culture In Higher Education, Henry Glaspie

Electronic Theses and Dissertations

Information security programs are instituted by organizations to provide guidance to their users who handle their data and systems. The main goal of these programs is to protect the organization's information assets through the creation and cultivation of a positive information security culture within the organization. As the collection and use of data expands in all economic sectors, the threat of data breach due to human error increases. Employee's behavior towards information security is influenced by the organizations information security programs and the overall information security culture. This study examines the human factors of an information security program and their …

Universality Of Egoless Behavior Of Software Engineering Students, Pradeep Waychal, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Software organizations have relied on process and technology initiatives to compete in a highly globalized world. Unfortunately, that has led to little or no success. We propose that the organizations start working on people initiatives, such as inspiring egoless behavior among software developers. This paper proposes a multi-stage approach to develop egoless behavior and discusses the universality of the egoless behavior by studying cohorts from three different countries, i.e., Japan, India, and Canada. The three stages in the approach are self-assessment, peer validation, and action plan development. The paper covers the first stage of self-assssment using an instrument based on …

Energy Slices: Benchmarking With Time Slicing, Katarina Grolinger, Hany F. Elyamany, Wilson Higashino, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. …

Evaluating Flexibility Metrics On Simple Temporal Networks With Reinforcement Learning, Hamzah I. Khan

HMC Senior Theses

Simple Temporal Networks (STNs) were introduced by Tsamardinos (2002) as a means of describing graphically the temporal constraints for scheduling problems. Since then, many variations on the concept have been used to develop and analyze algorithms for multi-agent robotic scheduling problems. Many of these algorithms for STNs utilize a flexibility metric, which measures the slack remaining in an STN under execution. Various metrics have been proposed by Hunsberger (2002); Wilson et al. (2014); Lloyd et al. (2018). This thesis explores how adequately these metrics convey the desired information by using them to build a reward function in a reinforcement learning …

Internet Censorship And Economic Impacts: A Case Study Of Internet Outages In India, Nivitha Raveendran, Christopher Leberknight

Department of Computer Science Faculty Scholarship and Creative Works

The objective of this research paper is to provide a methodology for measuring the financial impacts of Internet outages. The financial impacts are measured against a Nation's Gross Domestic Product (GDP) for several states in India to project the aftermath of Internet outage episodes. In addition historical trends are analyzed to help derive predictive logic for Internet outages in order to forecast Internet shutdown incidents based on antecedent events. Results demonstrate the proposed method for determining economic loss highlights several factors and may at times be influenced by the frequency of events compared to overall size of GDP. In addition, …

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare, Milad Zafar Nezhad

Wayne State University Dissertations

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. …

Smartphone-Based Self Rescue System For Disaster Rescue, Xitong Zhou

Theses, Dissertations and Capstones

Recent ubiquitous earthquakes have been leading to mass destruction of electrical power and cellular infrastructures, and deprive the innocent lives across the world. Due to the wide-area earthquake disaster, unavailable power and communication infrastructure, limited man-power and resources, traditional rescue operations and equipment are inefficient and time-consuming, leading to the golden hours missed. With the increasing proliferation of powerful wireless devices, like smartphones, they can be assumed to be abundantly available among the disaster victims and can act as valuable resources to coordinate disaster rescue operations. In this paper, we propose a smartphone-based self-rescue system, also referred to as RescueMe, …

Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher

Conference papers

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. …

Nupt St-Data Miner: An Spatio-Temporal Data Analysis And Visualization System, Zhiqiang Zou, Junjie Xiong, Xuerong He, Haihong Dai

Computer Science Faculty Research & Creative Works

Given the increasing popularity and availability of location tracking devices, large quantities of Spatio-Temporal data (ST-data) are available from many different sources. For the ST-data, reflecting the mobile characteristic of the world, it is essential to build a functional system to perform quickly interactive analysis. In this paper, we present an analysis and visualization system, NUPT ST-data Miner, which facilitates users to visualize and analyze ST-data. It (1) provides a flexible and extensible framework based on cloud computing platform, (2) is able to quickly retrieve specified ST-data, (3) integrated multiple functions for the ST-data. To demonstrate its efficiency, we validate …

An Uncertainty-Aware Visual System For Image Pre-Processing, Christina Gillmann, Pablo Arbelaez, Jose Tiberio Hernandez, Hans Hagen, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we …

Towards An Image-Based Indicator For Pad Classification And Localization, Christina Gillmann, Johh H. Matsuura, Hans Hagen, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Peripheral Artery Disease (PAD) is an often occurring problem caused by narrowed veins. With this type of disease, mostly the legs receive an insufficient supply of blood to sustain their functions. This can result in an amputation of extremities or strokes. In order to quantify the risks, doctors onsult a classification table which is based on the pain response of a patient. This classification is subjective and does not indicate the exact origin of the PAD symptoms. Resulting from this, complications can occur unprompted. We present the first results for an image-based indicator assisting medical doctors in estimating the stage …

Advances In Processing, Mining, And Learning Complex Data: From Foundations To Real-World Applications, Jia Wu, Shirui Pan, Chuan Zhou, Gang Li, Wu He, Chengqi Zhang

Information Technology & Decision Sciences Faculty Publications

Processing, mining, and learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning models from data with a complex structure (e.g., multirelational data, XML data, text data, image data, time series, sequences, graphs, streaming data, and trees) [1–5]. These kinds of data are commonly encountered in many social, economic, scientific, and engineering applications. Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for processing, mining, and learning them. Traditional …

The Influence Of Conversational Agents On Socially Desirable Responding, Ryan M. Schuetzler, G. Mark Grimes, Justin Scott Giboney, Jay F. Nunamaker Jr.

Information Systems and Quantitative Analysis Faculty Publications

Conversational agents (CAs) are becoming an increasingly common component in many information systems. The ubiquity of CAs in cell phones, entertainment systems, and messaging applications has led to a growing need to understand how design choices made when developing CAs influence user interactions. In this study, we explore the use case of CAs that gather potentially sensitive information from people—for example, in a medical interview. Using a laboratory experiment, we examine the influence of CA responsiveness and embodiment on the answers people give in response to sensitive and non-sensitive questions. The results show that for sensitive questions, the responsiveness of …

Expression Of The Microrna-143/145 Cluster Is Decreased In Hepatitis B Virus-Associated Hepatocellular Carcinoma And May Serve As A Biomarker For Tumorigenesis In Patients With Chronic Hepatitis B, Qi Zhao, Xiangfei Sun, Chao Liu, Tao Li, Juan Cui, Chengyong Qin

School of Computing: Faculty Publications

The aims of the present study were to identify the expression profile of microRNA (miR)‑143/145 in hepatitis B virus (HBV)‑associated hepatocellular carcinoma (HCC), explore its association with prognosis and investigate whether the serum miR‑143/145 expression levels may serve as a diagnostic indicator of HBV‑associated HCC. The microRNA (miRNA) chromatin immunoprecipitation dataset was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, and analyzed using the Wilcoxon signed‑rank test. It was observed that the expression of miR‑143 and miR‑145 was decreased 1.5‑fold in HBV‑associated HCC samples compared with non‑tumor tissue in the TCGA and the GSE22058 datasets …

The Rock 2018, School Of Engineering And Computer Science

The Rock

No abstract provided.

Towards Foundations Of Fuzzy Utility: Taking Fuzziness Into Account Naturally Leads To Intuitionistic Fuzzy Degrees, Christian Servin, Vladik Kreinovich

Departmental Technical Reports (CS)

The traditional utility-based decision making theory assumes that for every two alternatives, the user is either absolutely sure that the first alternative is better, or that the second alternative is better, or that the two alternatives are absolutely equivalent. In practice, when faced with alternatives of similar value, people are often not fully sure which of these alternatives is better. To describe different possible degrees of confidence, it is reasonable to use fuzzy logic techniques. In this paper, we show that, somewhat surprisingly, a reasonable fuzzy modification of the traditional utility elicitation procedure naturally leads to intuitionistic fuzzy degrees.

How Many Monte-Carlo Simulations Are Needed To Adequately Process Interval Uncertainty: An Explanation Of The Smart Electric Grid-Related Simulation Results, Afshin Gholamy, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the possible ways of dealing with interval uncertainty is to use Monte-Carlo simulations. A recent study of using this technique for the analysis of different smart electric grid-related algorithms shows that we need approximately 500 simulations to compute the corresponding interval range with 5% accuracy. In this paper, we provide a theoretical explanation for these empirical results.

Measures Of Specificity Used In The Principle Of Justifiable Granularity: A Theoretical Explanation Of Empirically Optimal Selections, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To process huge amounts of data, one possibility is to combine some data points into granules, and then process the resulting granules. For each group of data points, if we try to include all data points into a granule, the resulting granule often becomes too wide and thus rather useless; on the other case, if the granule is too narrow, it includes only a few of the corresponding point -- and is, thus, also rather useless. The need for the trade-off between coverage and specificity is formalized as the principle of justified granularity. The specific form of this principle …

A Novel Ieee 802.11 Power Save Mechanism For Energy Harvesting Motivated Networks, Yigitcan Celik

Theses, Dissertations and Capstones

The spread of wirelessly connected computing sensors and devices and hybrid networks are leading to the emergence of an Internet of Things (IoT), where a myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. However, the communication operations of wireless devices are often limited by the size and lifetime of the batteries because of the portability and mobility. To reduce energy consumption during wireless communication, the IEEE 802.11 standard specifies a power management scheme, called Power Saving Mechanism (PSM), for IEEE 802.11 devices. However, the PSM of IEEE 802.11 was originally designed for battery-supported devices …

A Formally Verified Heap Allocator, Arash Sahebolamri, Scott D. Constable, Steve J. Chapin

Electrical Engineering and Computer Science - Technical Reports

We present the formal verification of a heap allocator written in C. We use the Isabelle/HOL proof assistant to formally verify the correctness of the heap allocator at the source code level. The C source code of the heap allocator is imported into Isabelle/HOL using CParser and AutoCorres. In addition to providing the guarantee that the heap allocator is free of bugs and therefore is suitable for use in security critical projects, our work facilitates verification of other projects written in C that utilize Isabelle and AutoCorres.

An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas

Engineering Management & Systems Engineering Faculty Publications

Inter-rater reliability can be regarded as the degree of agreement among raters on a given item or a circumstance. Multiple approaches have been taken to estimate and improve inter-rater reliability of the United States Department of Defense Human Factors Analysis and Classification System used by trained accident investigators. In this study, three trained instructor pilots used the DoD-HFACS to classify 347 U.S. Air Force Accident Investigation Board (AIB) Class-A reports between the years of 2000 and 2013. The overall method consisted of four steps: (1) train on HFACS definitions, (2) verify rating reliability, (3) rate HFACS reports, and (4) random …