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Upgrade Internet Explorer Upgrade Chrome Upgrade Firefox Upgrade Safari Dismiss Penn State Penn StateHigherof Information Sciences and TechnologyHigherof Information Sciences and Technology Managing Information, Powering Intelligence Top Navigation Menu   DonateSenseSearch IST StudentsUndergraduate Undergraduate Online Degrees Graduate HonorsWonkIntegrity Career Solutions & Corporate Engagement Student Engagement Opportunities Prospective StudentsUndergraduate Undergraduate Graduate Online Transfer StudentsTranspirationof MajorTranspirationof Campus AlumniAlumni Society Board DirectoryFacultySenseStaff Visitors Advisory Board Postdoctoral Scholars Graduate Students Offices & Centers Search Main Menu The CollegeAbout News Calendar of EventsSenseSearch Student Spotlight IST Publications Campuses Plan a Visit Offices & Centers The EducationDegree Programs Careers & InternshipsWonkEnvironmentWonkOpportunities The ResearchCurrent Projects Research Areas Partners Centers and Labs Student Research Research Administration The ApplicationUndergraduate Graduate Online Search Options This Site Penn State People Departments Home The CollegeWell-nighAbout theHigherMission & Objectives IST History Diversity Dean's Welcome News Calendar of EventsSenseSearch Student Spotlight Student Blogs IST Publications Campuses Plan a Visit Offices & Centers The EducationStratumPrograms Bachelor of Science IUG ProgramSocializeof Science M.S.StratumPh.D.StratumOnline Degrees Honors Careers & InternshipsWonkEnvironment Student Life Clubs & Orgs Undergrad Advising Centers & LabsWonkOpportunities Undergrad Research Scholarships Education Abroad Outreach The Research Main Research Areas Partners Centers & Labs Student Research Research Admin TheUsingUndergraduate Graduate Online DonateSenseSearch IST Students Undergraduate Main IST Advising Center Majors Minors Transfer Credit Process Concurrent MajorsTranspirationof Campus Graduating Students Special Topics & IST 402 Independent Study Certificates IUG Program Online Graduate Main Forms Exam Scheduling Independent Study Policies Graduating Students Graduates in IST (GIST) HonorsWonkIntegrity Career Solutions Main CareerMinutiaeProcess Resumes, Letters, and Online Profiles Internship & Job Search Interviews & Offers Compass Career Resources Library Employers Student Engagement Student Life Clubs & Org Centers & Labs Scholarships Undergrad Research Education Abroad Outreach Prospective Students Main Undergraduate Graduate Online Transfer StudentsTranspirationof MajorTranspirationof Campus Alumni Main Update Your Info Alumni Society Board Alumni Spotlight Outstanding AlumniRibbonGivingWhenPSSA PSSA Events Directory FacultyUnitedFaculty GraduateSenseStaff Visitors IST Advisory Board Postdoc Scholars Graduate Students Research Associates Office & Centers Sidebar Navigation Menu Current Projects Research Areas Partners Centers and Labs Student Research Research Administration Current Projects You are hereHome » The Research » Current Projects Topic - Any -Artificial IntelligenceBig DataCognitive ScienceEnterprise ArchitectureHealth InformaticsHuman-Centered InformaticsPrivacy and SecurityProgrammingSocial InformaticsSenseResearcher - Any -Andrea TapiaAnna SquicciariniC. Lee GilesCarleen MaitlandDavid FuscoDavid ReitterDinghao WuDongwon LeeFrank RitterGuoray CaiJacob GrahamJames WangJohn M. CarrollJohn YenLynette (Kvasny) YargerMary Beth RossonNicklaus A. GiacobePeng LiuPeter ForsterPrasenjit MitraSencun ZhuVasant HonavarXiang ZhangXiaolong ZhangXinyu XingYasser El-ManzalawyZhenhui (Jessie) LiZihan Zhou Search Terms A New Direction for Software Reverse Engineering and BinaryLawmakingRetrofittingSenseResearcher: Dinghao Wu Sponsoring Agency: Office of Naval Research A major obstacle in binary lawmaking based retrofitting is the immaturity of the reverse engineering tools. Current approaches, mostly binary lawmaking patching based, to retrofit legacy software systems have a number of drawbacks including performance overhead and security issues. To the weightier of our knowledge, there are no binary reverse engineering tools that can disassemble a binary executable into turnout lawmaking which can be reassembled when in a fully streamlined manner. This limitation has severely restricted the using of reverse engineering techniques in legacy software retrofitting. Further, the wringer and transformation tools and ecosystems are shredded and fragmented. Connecting the dots between the tools, infrastructures, and ecosystems will have unconfined impact on software wringer and retrofitting. To fill in the gap, we are considering a radically variegated tideway by placing the recompilability as the first and topmost goal. We will remoter develop our preliminary study on Reassembleable Disassembling, with the similar diamond goal to preserve the recompilability while lifting the lawmaking to higher level languages or intermediate representations. The proposed reverse engineering technology can help plicate legacy software systems with modern security mechanisms, permitting us to write a problem space that was previously intractable. Research Tags: Security and Privacy, Reverse Engineering, BinaryLawmakingAnalysis, Software Retrofitting, Software Analysis, Software Security, Program Analysis, Legacy Code, Legacy SystemsWell-judgedPrediction of User Demographics from Social Media via Markov Logic Network and Deep LearningSenseResearcher: Dongwon Lee Sponsoring Agency: SamsungWideInstitute of Technology The Social User Mining (SUM) project aims to develop novel algorithmic solutions, working prototypes, and innovative applications for mining "publicly-available" social media data from user profiles. (No illegally crawled or scraped data violating users' privacy or terms-of-service of social network sites will be used.) We aim to mine and automatically discover interesting information well-nigh social users including demographics, profile, temporal pattern, and spatial pattern, which withstand important and practical implications in real settings. In the SUM project, we will work to understand the pursuit questions: (1) What is the technical landscape and solution space of the problem in general? (2) How can we create good quality ground truth data set to various profile information? (3) Which social media data is the most useful one to discover user profile information? (4) How can we combine variegated social media data to modernize the performance of overall solutions? (5) What is the holistic framework? (6) What are the constructive and scalable data mining solutions to mine such social user information?WorriednessSpace and Measuring Environmental Exposure in Behavioral ResearchSenseResearcher: Zhenhui (Jessie) Li Sponsoring Agency: University of Illinois Research suggests that the environment influences lifestyle behaviors and contributes to racial and socioeconomic inequities in health. Overall, however, results are inconsistent and effect sizes are small. One potential subtitle is measurement error in environmental exposures. Despite that many individuals spend considerable time outside their firsthand neighborhood, most research has solely measured environmental exposures in a respondent’s residential space and failed to take into worth daily mobility. This research will examine the joint spatial and temporal stability of worriedness spaces (AS) derived from GPS data in a racially, ethnically, and socioeconomically diverse sample of adults. Specifically, we aim to (1) determine the optimal number and combination of days of GPS tracking needed to represent an individual’s AS by testing the temporal stability within a person wideness days, types of days, and between weeks; (2) determine the sufficiency of using only one time period for GPS tracking by examining the temporal stability of an individual’s AS within and between seasons; and (3) compare participant AS as derived from GPS tracking with those derived from questionnaires, with comparisons based on AS overlap, size, and environmental attributes. Research Tags: Big DataStructuringin Web-Forum Discourse: Computational Models ofVersionand LanguageTranspirationFaculty Researcher: David Reitter Sponsoring Agency: National Science Foundation Language use in real-world dialogue happens in context; linguistic choices depend on previous ones. For example, chosen words and sentence structures tend to mirror what was used previously by a conversation partner, a process known as "alignment."Structuringappears to help people understand each other in dialogue, and it seems to proffer to human-computer interfaces, too. The touchable functions of structuring in dialogue, however, are unclear. The project will devise computational models that describe and quantify structuring and language transpiration in natural-language dialogue. With these, one can snift them in very language use, such as in web-forums. The computational models will explain and predict processes in a way that makes them exploitable in modern social networks as well as for data science. The outcomes of the project may point to novel methods of prioritizing and filtering the most helpful content and can write quality of life and well-being of patients such as those of the peer-support polity whose conversations were studied in the investigator's work motivating the proposal. AutomatingStrainedIntelligence: Empowering Analysts with Intelligent,VoluntarySoftwareTeachersFaculty Researcher: Vasant Honavar Sponsoring Agency: Concurrent Technologies Corporation The principle investigator of this project will provide subject matter expertise in strained intelligence and thought leadership as related to minutiae of the Analyst Virtual Assistant (AVA) and CognitiveTeacherscapabilities for the Boosting Innovation GEOINT Topic 2:StrainedIntelligence AutomationWholesaleAgency Announcement. The project will provide research support in strained intelligence, machine learning and intelligent agents, including, but not limited to: constructive methods for classifying actors in heterogeneous and dynamic social networks (node labeling), and link prediction. Automating Relevance and Trust Detection in Social Media Data for Emergency ResponseSenseResearcher: Andrea Tapia, Anna Squicciarini Sponsoring Agency: National Science Foundation The goal of this project is to develop ways to modernize information quality and use in emergency response, increasing the value of using messaging and microblogged data from crowds of non-professional participants during disasters. Despite the vestige of strong value to those experiencing the disaster and those seeking information concerning the disaster, there has been very little effort in detecting the relevance and veracity of messages in social media streams.  The problem of data verification is one of the largest problems confronting emergency-response organizations contemplating using social media data. This research directly addresses this known problem by methods to measure relevant and verifiable information. The results of this research will have a uncontrived pipeline to organizations involved in emergency response.  Therefore the research has the potential to help organizations, which respond to emergencies, make use of large amounts of citizen-produced data, which in turn may modernize the speed, quality, and efficiency of emergency response leading to largest support to those who need them, and increasingly lives saved. This research will contribute to the field of Emergency and Disaster Studies by mapping the key decisions made during an emergency response, the information needs, type, form and spritz during those visualization points, and most importantly, assessing data quality and verifiable standards for each. It will moreover investigate relevant and verifiable identifiers (or features), provide weights, incorporate these into an tampering framework, and use the results of the wringer as input to scalable computational models. The work will diamond algorithms that can estimate the relevance and veracity of messages in a high-volume streaming text comprised of short messages. Given the diverse backgrounds of the team, it will contribute to the use and minutiae of socio-technical systems theory to unriddle the integration of technical and social systems. The output of the models will match the organizational needs of responding organizations. Big Data Social Science - An Integrative Education and Research Program in Social Data AnalyticsSenseResearcher: C. Lee Giles, Prasenjit Mitra Sponsoring Agency: National Science Foundation This project draws together a diverse interdisciplinary team of researchers to create a new training program in Social Data Analytics, aimed at producing a new type of scientist capable of meeting emerging big data challenges. In response to massive new sources of data, "data science" and "analytics" are emerging as new fields of inquiry, merging statistics, computer science, and visualization. The greatest challenges and opportunities upspring from socially-generated big data, observed as a result of human interactions that are increasingly recorded via web, mobile device, and distributed sensors, or revealed through the digitization of historical records. Society faces a transformative data deluge, from which new scientific, economic, and social value can be extracted. This project includes a new curriculum, training in wide technologies of data science and analytics, a series of research rotations in both wonk and non-academic settings, and a rencontre mechanism under which interdisciplinary teams compete to innovate solutions to real social data analytics problems. Further, this project expands the participation of underrepresented groups in data science, by combining an heady new field with a focus on diversity as a research theme. BinaryLawmakingReverse Engineering and RetrofittingSenseResearcher: Dinghao Wu Sponsoring Agency: National Science Foundation and Office of Naval Research Reverse engineering has many important applications in computer security, one of which is retrofitting software for safety and security hardening when source lawmaking is not available. However, no existing tool is worldly-wise to disassemble executable binaries into turnout lawmaking that can be correctly reassembled in a fully streamlined manner. People have tried to overcome it by patching or duplicating new lawmaking sections for retrofitting of executables, which is not only inefficient but moreover cumbersome and restrictive on what retrofitting techniques can be unromantic to. Our research is working toward a tool that can disassemble executables to the extent that the generated lawmaking can be assembled when to working binaries without transmission effort. Research Tags: Security and Privacy, Reverse Engineering, BinaryLawmakingAnalysis, Software Retrofitting, Software Analysis, Software Security, Program Analysis, Legacy Code, Legacy Systems Biologically Inspired Algorithms for Knowledge Representation, Memory, Language Processing and LearningSenseResearcher: Vasant Honavar Sponsoring Agency: National Science FoundationStrainedneural networks, considering of their potential for massive parallelism and fault and noise tolerance, offer an lulu tideway to the diamond of associative memories, language processors, and trainable pattern classifiers. Evolutionary algorithms offer a powerful ways of exploring large search spaces for solutions that optimize multiple objectives.Versusthis background, we explored several closely related topics in biologically inspired algorithms and architectures for knowledge representation and language processing. Research Tags:StrainedIntelligence, Big Data, Health Informatics and BioinformaticsSmart-assImage DataWringerof Intrusion Detection Cyber AnalystsSenseResearcher: John Yen, Peng Liu, Vasant Honavar Sponsoring Agency:Higherof Information Sciences and Technology Cyberanalysts perform ramified tasks in analyzing data regarding potential cyberattacks; however, our understanding of their fine-grained cognitive process and how these processes evolve through training are rather limited. The goals of this research project are twofold. First, we aim to diamond cognitive tasks that not only reflect some of the complexity of real-world intrusion detection tasks but moreover are suitable for fMRI studies. Second, we aim to wield smart-ass network wringer and machine learning methods to unriddle smart-ass images data of cyberanalysts from fMRI and EEG for predictive modeling regarding the performance of intrusion detection by cyberanalysts. The result of this research can provide an initial vestige of larger-scale smart-ass studies for improving our understanding well-nigh the cognitive processes of ramified visualization making tasks. Research Tags: Neural Science,Smart-assNetwork, Cybersecurity, Cognitive Science, Big Data, Machine LearningTowersa Big Data Analytics Workforce in iSchoolsSenseResearcher: Dongwon Lee Sponsoring Agency: National Science Foundation The significance and importance of this project resides in the introduction of big data analytics into the education landscape. There is increasing demand for skilled personnel in big data industries, but existing big data curricula at the university level focus primarily on students with a strong computational background, ignoring a large segment of students who might otherwise pursue education and training in this vital area, but who will be faced with big data issues in the workplace. This project aims to write the national demand for professionals with knowledge in big data and broadening the pool for a big data analytics workforce. Part of this effort will involve research as to whether the newly ripened learning modules are increasingly constructive at increasing students' big data competencies, skills, and analysis. Specifically, this project aims to develop three innovative learning modules, which will be designed to (1) utilize both group-based and contextualized learning methods and (2) be workable and wieldy to students majoring in disciplines outside, but related to main-stream computer science.TowersanZippyCyber Defense Toolkit based onViolatingImpactWringerin deject ComputingSenseResearcher: Peng Liu Sponsoring Agency: National Institute of Standards and TechnologyDejectcomputing has been widening the once existing gap between mission impact towage and zippy cyber defense in defending versus cyber-attacks. If we do not underpass this gap, there would be two very undesirable consequences: (1) mission impact towage results cannot be automatically used to make recommendations on taking zippy cyber defense actions; (2) existing zippy cyber defense techniques cannot be mission aware. That is, these techniques won’t be worldly-wise to directly state their effectiveness in terms of the mission requirements such as how much or what (mission tasks) will be workaday by when. Through our recent NIST-funded research we have ripened an innovative model to theoretically underpass the gap between mission impact towage and zippy cyber defense. However, a substantial value of research is still required surpassing a mission-aware zippy cyber defense toolkit can be built and evaluated. The model is still too utopian in terms of how mission tasks are modeled. As a result, the model cannot be directly implemented. To solve this problem, a daunting research rencontre is to discover new wresting layers to underpass the semantic gap between the model and the inspect log data used by the defense toolkit. In this project, we propose to (a) write the new research challenges; (b) build a mission-aware zippy cyber defense toolkit in deject computing context, and (c) self-mastery touchable specimen studies to evaluate the effectiveness of this new defense toolkit. In addition, we will construct a website through which the tool can be remotely demonstrated and tested by the government and industry users.TowersSecureVoluntaryVehiclesSenseResearcher: Peng Liu Sponsoring Agency:Higherof Information Sciences and Technology This project aims to defend versus various security threats from voluntary vehicles through new security architectures and system designs. Research Tags: voluntary vehicles, cyberattacks, defenses CAREER:WideTrace-Oriented BinaryLawmakingAnalysisSenseResearcher: Dinghao Wu Sponsoring Agency: National Science Foundation Binary lawmaking wringer is very lulu from a security viewpoint. First, in many tasks such as malware analysis, software plagiarism detection, and vulnerability exploration, the source lawmaking of the program under viewing is often absent, and the wringer has to be washed-up on binary lawmaking initially. Second, plane if the source lawmaking is available, binary wringer allows us to reason the real instructions executed on hardware and stave the well-known “What You See Is Not What You Execute” problem. Third, some program behaviors such as enshroud wangle only walkout in the low-level code. Binary lawmaking wringer is faced with an increasing rencontre caused by emerging, readily misogynist lawmaking obfuscation techniques. Traditional signature-based malware detection is often problematic as it relies on file hashes and bye (or instruction) signatures which are not very resilient to obfuscation. This project tackles the rencontre by proposing several wide methods that combine techniques from policies and semantics perspectives. The proposed methods leverage formal program semantics, symbolic execution, streamlined constraint solving, and algorithmic memorization of lawmaking semantics that form solid foundations with rigorous resilience properties to latest attacks. Research Tags: Binary Diffing, Malware Analysis, Metamorphic Malware, Symbolic Execution, Weakest Pre-condition, Constraint Solving, Taint Analysis, Side-channel, Software Analysis, Software Security, ProgramWringerCAREER: Cross-Domain Urban Data MiningSenseResearcher: Zhenhui (Jessie) Li Sponsoring Agency: National Science FoundationEqualto U.S. 2010 Census, well-nigh 80.7% of the U.S. population live in urban area. Urbanization has modernized people's lives but moreover generated many urban issues such as traffic congestion, air pollution, health, education, and life quality. In the meantime, with the rapid progress in sensing technologies and widely-used digital documentation, increasing value of urban data are stuff piled in the digital form, including human traces, traffic, air quality, local events, vehicle collisions, noise reports, and many more. Many cities in the U.S. (e.g., New York City, Chicago, and Los Angeles) have joined the unshut data initiative and created websites to release the municipality data to the public. Such big data implies rich knowledge well-nigh a municipality and could empower us to write many hair-trigger urban challenges. This project develops novel data mining techniques to help people uncover the complicated correlations in the big urban data. Research Tags: Big Data Collaborative: Intelligent Context-Aware Peer-to-Peer Transaction BrokeringSenseResearcher: John M. Carroll Sponsoring Agency: National Science Foundation This is a collaborative project moreover involving researchers from Carnegie-Mellon University and Xerox Palo Alto Research Center; it expands a previous NSF ribbon in which we ripened and investigated the first mobile computing infrastructure for timebanking. Timebanking is an objector movement and unselfish service mart system in which time is voluntarily exchanged for services among local polity members. This project investigates subtracting computational support for context sensation to timebanking infrastructures, that is, helping people take into worth their own location, navigational trajectories, preferences, and current plans as they engage in time-based exchanges. We moreover are investigating coproduction-based systems in which services are mutually, interdependently and often reciprocally produced, producing “free” social goods. CollectiveSustentationThreats: Models and DeterrenceSenseResearcher: Anna Squicciarini Sponsoring Agency: Texas A&M University Social media enables the rapid harnessing and unfurling of user interest, captivating the sustentation of huge numbers of users. However, knowing that interest may quickly solidify and then collectively focus virtually a particular phenomenon, new threats are emerging which have potentially far reaching consequences. These threats, moreover referred to as joint sustentation threats involve users’ opinion manipulations, fast spread of various forms of malware, or false information sharing, inferential via manipulation of joint attention. In comparison with tradition Internet and Computer threats (such as malware) – in which users are targeted by teachers and lured into taking some deportment – users themselves are unwitting accomplices to the spread, infection rate, and success of these joint sustentation threats. This project aims to develop the framework, algorithms, and systems for detecting, analyzing, modeling, and defending versus emergent joint sustentation threats in large-scale social systems by (1) creating new explanatory socio-behavioral models of the dynamics of joint sustentation in tandem with the inherent threats versus joint attention; (2) operationalizing our descriptive data-drive models to provide predictive capabilities of emerging joint sustentation threats, rigorous stress-testing, parameter sensitivity analysis, and what-if analysis, all grounded in social-behavioral theories of joint attention; and (3) developing, deploying, testing, and refining a suite of threat sensation analytics to serve as a prototype for a joint sustentation early-warning system. Combating Radicalization and the Foreign Terrorist Fighter ThreatSenseResearcher: Peter Forster Sponsoring Agency: This project focuses on engaging government entities and starchy society through a series of tabletop exercises to build topics in countering terrorism and extremism. Some uncontrived expenses are covered by the US Department of Defense but there is no funding to imbricate personnel time. Research Tags: Counterterrorism, Counter Violent Extremism, Foreign Terrorist Fighters, NATO, Returning Terrorist Fighters, Tabletop Exercises CompCog: Inference of the Syntactic and Semantic Relationships Between Words from an Untagged Corpus Using a Distributional Model of Semantics Derived from Human Memory TheorySenseResearcher: David Reitter Sponsoring Agency: National Science Foundation Within the human mind, there is something like a wordlist that tells people what words midpoint (semantics) and how words are combined to make grammatical sentences (syntax). How does the mind learn this wordlist from wits with a language? Computer simulations can help science largest understand this learning process which can, in turn, help teach languages in the classroom and aid in the early detection of language deficits. Improving the worthiness of computers to simulate language learning processes can moreover lead to the minutiae of largest technology such as machine translation, web search, and virtual assistants. This project considers how a largest understanding of language learning can help us stave worldwide pitfalls of memory unfluctuating to the use of language through the diamond of a new model of human memory, the Hierarchical Holographic Model. This computational model helps explain unrepealable aspects of how words and languages are learned, and will indulge us to investigate the question of whether human memory has the worthiness to snift summarily indirect associations between concepts. The researchers consider vestige that sensitivity to utopian relations between words improves the worthiness of the computer model to learn syntax, such as parts-of-speech, and to use words thus to construct grammatical sentences. This work will be assessed versus human language data and competing computational models. The success of the computational model should provide vestige that (1) language vanquishment depends on indirect associations, and (2) human memory must be worldly-wise to form indirect associations to facilitate it. CPS-Security: Towards Provably Correct Distributed Attack-resilientTenancyof Unmanned-vehicle-operator NetworksSenseResearcher: Peng Liu Sponsoring Agency: National Science Foundation Inherent vulnerabilities of information and liaison technology systems to cyberattacks (e.g., malware) impose significant security risks to Cyber-Physical Systems (CPS), which is evidenced by a number of recent accidents. Noticeably, current distributed tenancy of CPS is not really attack-resilient. Although provable resilience would significantly lift the trustworthiness of CPS, existing defenses are rather ad-hoc and mainly focus on wade detection. In addition, while network attacks have been extensively studied, resilient-to-malware distributed tenancy has been rarely investigated. This research aims to underpass the gap by investigating provably correct distributed attack-resilient tenancy of CPS. The project will focus on a representative matriculation of CPS, namely unmanned-vehicle-operator networks. Its four main research thrusts are (1) the minutiae of a distributed attack-resilient tenancy framework to ensure task completion of multiple vehicles despite network attacks and malware attacks, (2) the synthesis of novel distributed attack-resilient tenancy algorithms to deal with network attacks, (3) the diamond of interpretation algorithms to snift malware attacks on vehicles, and computationally efficient algorithms which indulge wipe vehicles to stave the standoff with the vehicles compromised by malware, and (4) the validation of the cost-effectiveness of the proposed distributed attack-resilient tenancy framework via a principled systematic evaluation plan. The research findings profoundly impact CPS security of a variety of engineering disciplines vastitude unmanned-vehicle-operator networks, including smart grid, smart buildings and intelligent transportation systems. Research Tags: Attack-resilient Control, Unmanned-vehicle-operator Networks Cramless Phase 2SenseResearcher: Frank Ritter Sponsoring Agency: Charles River Analytics, Inc. This research will explore algorithms to help schedule new jobs, using strained intelligence and industrial-organizational psychology. Cross-Organization Big Data Cyber AttackSensationFaculty Researcher: John Yen, Peng Liu Sponsoring Agency: National Science Foundation Cyberattacks, expressly those involvingWidePersistent Threats (APTs), have targeted organizations of all types. A key opportunity to counter large-scale cyberattacks is to initiate the establishment of a wholesale partnership regarding the ultimate goal of cross-organization protected sharing of relevant cybersecurity data for enhanced operation, workforce development, and research. The impacts of sharing cybersecurity data are immense, such as an institution's snooping well-nigh potential risks involved in sharing such data. This project will modernize our understanding well-nigh these ramified issues related to barriers for protected sharing of cybersecurity data through collaborative activities and a planned workshop. An improved understanding regarding these issues and their relationships in a holistic way provides a hair-trigger wiring on which possible weightier practices for agreements, frameworks, and cyberinfrastructures for sharing relevant cybersecurity data can be established. In addition, the workshop will moreover identify options and uncover their tradeoffs for addressing the ramified issues for cross-organization sharing of cybersecurity data. It is likely that this tacit knowledge will enhance the formal knowledge regarding cybersecurity analysis, management, and tool development, expressly for achieving cross-organization big data cyberattack awareness. Research Tags: Cyberattack Awareness, cross-organization information sharing Crowdsourcing Human-Centered Data Analytics toModernizethe use ofResiderScience by Local StakeholdersSenseResearcher: Andrea Tapia Sponsoring Agency: National Science Foundation The problem of verifying data is one of the largest problems confronting emergency-response organizations contemplating using social media data (Tapia, Bajpai, Jansen, Yen, & Giles, 2011). Our research directly addresses this problem by designing both social and computational interventions that can estimate the relevance and veracity of resider contributions in a way that largest meets the needs of responders during a crisis. Through the support of this award, our research will proceeds remoter understanding of data analytics that support human processing of emergency management data, the types of information necessary to help polity members understand their own resilience capacity, and participatory processes to modernize resider science projects. This research has potential to help organizations responding to emergencies make use of large amounts of citizen-produced data, which in turn may modernize the speed, quality, and efficiency of emergency response, leading to largest support to those who need them, and even, increasingly lives saved. This research proposes a method of quality tenancy by introducing an iterative system of data curation that encourages resider scientists to wilt stakeholders in the quality of the information that they create by utilizing human-centered data analytics. The system begins with indirect data aggregated from social media that is later filtered and enhanced with uncontrived contributions from resider scientists engaged in the participatory diamond of a polity zestful system. We will use the results to diamond social and technical infrastructure for polity resilience operational picture (CROP) that would indulge local polity leaders to monitor the resilience of their polity at various levels. This CROP prototype would yank in data from physical sensors, network sensors, and human sensors monitoring resilience indicators. The scientific tideway to this work is measurement of the engagement of the polity in its own slipperiness response by observing the spritz of information and indicators of polity resilience during and without a crisis. If successful, this project will take a significant step forward in our understanding of methods to measure relevant and verifiable information in a process that 1) utilizes polity participation in enhanced event detection and pre-response to crises, and 2) supports emergency response organizations leverage new layers of spatial, temporal, and social infrastructure. Developing 3-D ImageWringerMethods for Maize Root StructuresSenseResearcher: James Wang, Zihan Zhou Sponsoring Agency:Higherof Information Sciences and Technology This project aims to develop 3-D image wringer methods for root structures. The work is a collaboration with Penn State’s Department of Plant Science. Research Tags: 3-D Image Analysis, Plant Root Discrimination in the IT Labor Market?InspectStudy of Race and Institution in the Employment Seeking ProcessSenseResearcher: Lynette (Kvasny) Yarger Sponsoring Agency:Higherof Information Sciences and Technology Creating a diverse IT workforce has been a rencontre for many major technology companies, including Apple, Facebook, and Yahoo. At these Silicon Valley companies, only 2-3% of the technology workforce is Black, while Blacks make up 12% of the US workforce. Technology firms often oppose that the rationalization of this disparity is due to the severe shortage of Blacks with degrees in computing. However, U.S. universities turn out Black computer science and computer engineering graduates at twice the rate that leading technology companies rent them. In addition, corporations, universities and U.S. government agencies protract to invest in programs to build a diverse pipeline of technology talent, but the representation of Blacks in tech remains stagnant. The purpose of this study is to examine the role of implicit bias by employers in the hiring process of entry-level IT professionals. Research Tags: IT Workforce, Diversity, Inclusion, Hiring, Implicit Bias DomainVersionApproaches for ClassifyingSlipperinessRelated Data in Social MediaSenseResearcher: Andrea Tapia Sponsoring Agency: National Science Foundation The project investigates the use of big-data wringer techniques for classifying crisis-related data in social media with respect to situational sensation categories, such as caution, advice, fatality, injury, support, with the goal of helping emergency response teams identify useful information. A major rencontre is the scale of the data, where millions of short messages are continuously posted during a disaster, and need to be analyzed. The use of current technologies based on streamlined machine learning is limited due to the lack of labeled data for an emergent target disaster, and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved. To tackle the whilom challenges, domain version techniques that make use of existing labeled data from prior disasters and unlabeled data from a current disaster are designed. The resulting models are continuously updated and improved based on feedback from crowdsourcing volunteers. The research will provide real, usable solutions to emergency response organizations and will enable these organizations to modernize the speed, quality and efficiency of their response. Educationally, this research will involve the integration of domain version and emergency-related research into courses taught by the PIs, and training of undergraduate and graduate students, including underrepresented groups. The goal of this research is to perform big data nomenclature for an emergent disaster, tabbed target disaster, using domain version algorithms that employ unlabeled data from the target disaster, in wing to label data from prior source disasters. The research provides solutions based on deep neural networks to tackle the unique challenges in applying machine learning for crisis-related data analysis, specifically the volume and velocity challenges of big slipperiness data. The main contributions to the state-of-the-art in both the computer and information sciences and the social science are as follows: (1) Enhancements to the field of Emergency/Disaster Studies by mapping the key decisions well-nigh transferring situational sensation knowledge, and the information needs, type, form and spritz during those visualization points. This is an essential step to providig data to response organizations at a time and in a form that is verifiable, violating and appropriate; (2) Novel domain version approaches based on deep neural networks, for transferring information from source crises to a target crisis. Deep learning approaches will make it possible to employ large amounts of labeled source data and unlabeled target data, and to incrementally update the models as increasingly labeled target data becomes available; (3)Remoteruse and minutiae of socio-technical systems theory to unriddle the integration of technical and social systems in the context of knowledge transfer from prior source crises to an emergent target crisis. Technical and social solutions to the problem of transferring situational sensation knowledge will be composite together for use in emergency response. DomainVersionApproaches for ClassifyingSlipperinessRelated Data in Social MediaSenseResearcher: Andrea Tapia Sponsoring Agency: National Science Foundation The research will investigate domain version approaches for classifying crisis-related social media data. The main objective of the research is to write the volume and velocity challenges of big slipperiness data in emergence response, where millions of short messages are continuously posted during a disaster and need to be analyzed to identify those that siphon situational sensation information useful for the response teams. Given the lack of labeled data for an emergent target slipperiness event and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved, the goal of this research is to perform big data nomenclature for a target slipperiness using unsupervised domain version algorithms that employ unlabeled data from the target slipperiness itself, in wing to label data from prior source crises. The research will provide real, usable solutions based on deep learning to tackle the unique challenges in applying machine learning for crisis-related data analytics. Research Tags: Big Data, Social Informatics EAGER: Toward Transparency in Public Policy via Privacy-Enhanced Social FlowWringerwith Applications to Ecological Networks and CrimeSenseResearcher: Zhenhui (Jessie) Li Sponsoring Agency: National Science Foundation Recent improvements in computing capabilities, data collection, and data science have enabled tremendous advances in scientific data analysis. However, the relevant data are often highly sensitive (e.g., Census records, tax records, medical records). This project addresses an emerging and hair-trigger scientific problem: Privacy concerns limit wangle to raw data that might reveal information well-nigh individuals. Techniques to "sanitize" such data (e.g., anonymization) could have negative impacts on the quality of the scientific results that use the data. How can we provide data that protects the privacy of individuals but moreover virtuously support scientific analyses? Research Tags: Big Data Exploring Global Structures in Visual Data for Robust 3D VisionSenseResearcher: Zihan Zhou Sponsoring Agency:Higherof Information Sciences and Technology Although significant progress has been made well-nigh the imaging devices and 3D reconstruction techniques over the past few decades, obtaining well-judged 3D models and the associated camera positions from images still remains a challenging problem in computer vision. In this project, we develop novel, data-driven approaches to robust and efficient 3D reconstruction of man-made environments by harnessing various forms of global spatial relationships among multiple points, lines, or patches in the images, such as parallel lines, planes, and repetitive patterns. We remoter study how these structures can facilitate various emerging commercial and consumer applications of 3D vision, such as virtual reality, robotics, and voluntary driving. Research Tags: Computer Vision, Virtual Reality, Big Data Geodeliberation: Enabling Democratic Decision-Making in Local Communities through Place-based Deliberative DialoguesSenseResearcher: Guoray Cai, John M. Carroll Sponsoring Agency: National Science Foundation This project seeks to discover new knowledge required to support geodeliberation in polity geospatial decision-making contexts. Geodeliberation refers to democratic deliberation (within local communities) on ramified and controversial geographically-defined problems and involves the use of geographical information and online, asynchronous deliberative technologies. This research addresses two key knowledge gaps. One is the lack of understanding well-nigh human information and interaction policies while engaging online asynchronous geodeliberation, and the methodological challenges of supporting community-scale deliberation of ramified geospatial problems over sustained engagements. A increasingly formidable gap is between the desirable level of public involvement and the practical level of participation that can be supported by the current social-technical solutions. To write these gaps, the research applies an ethnographically-guided participatory research tideway to: (1) identify opportunities and barriers in using geodeliberation to empower communities; and (2) investigate visual-computational methods to enable human participation and facilitation of geodeliberation processes. Research activities include: developing cognitively-motivated diamond of visual representations and interfaces; models of deliberative spiel and decision-making in communities; and zippy facilitation of large-scale geodeliberation towards largest coherence and effectiveness. The research addresses broader impacts of three kinds. First, this project demonstrates the potential of using information technology to modernize societal engagement in community-level. Second, the diamond research investigation of socio-technical support for geodeliberation will provide a touchable model for local governments wideness the nation. Third, this project will prepare a generation of undergraduate and graduate students with consciousness and career potentials in applying social-technical solutions in the practice of democratic decision-making. HowVariegatedAre Your Social Media Personae?SenseResearcher: Dongwon Lee Sponsoring Agency: National Science Foundation and Economic and Social Sciences Research Council We are researching how individuals present themselves wideness variegated social media platforms. That is, when users create profiles in variegated social networks, are they redundant expressions of the same persona or are they well-timed to each platform? Our team is reviewing profile images and shared information to take a first squint at how user profiles vary in the volume wideness variegated social networks. Research Tags: Big Data,Polityand Social Informatics, Social Network, Privacy Model, Privacy Enforcement, Internet Security Identifying and Informing Strategies for Disrupting Drug Distribution Networks: AnUsingto Opiate Flows in PennsylvaniaSenseResearcher: Peter Forster Sponsoring Agency: National Institute of Justice The primary purpose of this project is to interreact with the Pennsylvania State Police (PSP) and polity organizations to identify and describe opiate distribution networks, discern ways to disrupt them in Pennsylvania, and develop a model for implementation wideness the United States. Research Tags: Abuse, Prescription Pain Relievers, Heroin, Social Networking Analysis, Distribution Networks, Hubs, Prevention, Enforcement, Treatment Inferring Software Specifications fromUnshutSource Repositories by Leveraging Data and CollectivePolityExpertiseSenseResearcher: Vasant Honavar Sponsoring Agency: National Science Foundation Today individuals, society, and the United States critically depend on software to manage infrastructures for power, financial and finance, air traffic control, telecommunication, transportation, national defense, and healthcare. Specifications are hair-trigger for communicating the intended policies of software systems to software developers and users and to make it possible for streamlined tools to verify whether a given piece of software behaves as intended. Safety hair-trigger applications have traditionally enjoyed the benefits of such specifications, but at a unconfined cost. The sparsity of precise, comprehensible, and efficiently verifiable specifications is a major hurdle to developing software systems that are reliable, secure, and easy to maintain and reuse. This project brings together an interdisciplinary team of researchers with complementary expertise in formal methods, software engineering, machine learning, and big data analytics to develop streamlined or semi-automated methods for inferring the specifications from code. The resulting methods and tools combine analytics over large unshut source lawmaking repositories to plicate and modernize upon specifications by program analysis-based specification inference through synergistic advances wideness both these areas. Information andLiaisonTechnology (ICT)-based incubators: Institutional Foundations in RwandaSenseResearcher: Carleen Maitland Sponsoring Agency: U.S. Fulbright Program ICT-based technology incubators have wilt an important component of entrepreneurship and economic growth in the U.S. Yet, it is unclear whether or not these models can transfer to emerging economies. Many studies have identified factors influencing incubators' success in launching businesses. However, these studies take only a narrow view of success as well as the factors involved. This research identifies a novel set of successful outcomes for incubators, taking into worth the realities of less ripened economies. It moreover develops a theory of purlieus institutions that identifies organizational and governmental institutions necessary for success. Research Tags: ICT Incubators, Entrepreneurship, Rwanda Innovation in anWhite-hairedSocietySenseResearcher: C. Lee Giles Sponsoring Agency: The National Bureau of Economic Research This research will merge disambiguated and linked MEDLINE ProQuest data into UMETRICS and National Institute of Health (NIH)Usingdata all at scale. For the NIH, we will undergo a Public TrustPreliminariesCheck and obtain clearance. Once approved, it will provide wangle to NIH data via an NIH laptop. We will moreover compare the disambiguated and linked MEDLINE ProQuest data to the Smalheiser-Torvik Authority tragedian data. INSPIRE: A Data-DrivenTidewaytoward Exploring Natural and Anthropogenic Methane Emissions in Regions of Shale GasMinutiaeFaculty Researcher: Zhenhui (Jessie) Li Sponsoring Agency: National Science Foundation This INSPIRE project addresses the issue of upper volume hydraulic fracturing, moreover tabbed fracking, and its effects on ground water resources. Fracking allows drillers to pericope natural gas from shale deep within the earth. Methane gas sometimes escapes from shale gas wells and can contaminate water resources or leak into the undercurrent where it contributes to greenhouse gas emissions. Monitoring for these potential leaks is difficult considering methane is moreover released into aquifers naturally, and considering monitoring is time- and resource-intensive. Such subsurface leakage may moreover be relatively rare. This project seeks to modernize overall understanding of the impacts of natural gas drilling using both advances in computer science and geoscience, and to teach the public well-nigh such impacts. The project will elucidate both the effects of human activities such as shale gas minutiae as well as natural processes which release methane into natural waters. Results of the proposed research will lead to a largest understanding of water quality in areas of shale-gas minutiae and will highlight problems and potentially problematic management practices. The research will whop both the fields of geoscience and computer science, will train interdisciplinary graduate students, and involve resider scientists in collecting data and understanding environmental data analysis. The project combines new hydro-geochemical strategies and data mining approaches to study the release of methane into streams and ground waters. Research Tags: Big Data Integrated Molecular, Dynamic Imaging, and ModelingWringerof Stomatal GuardLaminaWallsSenseResearcher: James Wang Sponsoring Agency: National Science Foundation This project seeks to determine how the carbohydrate-based lamina walls of baby-sit cells dynamically transpiration shape to tenancy stomatal pore size, thus permitting plants to tenancy stat dioxide (CO2) uptake and water loss. Stomata are small openings in the surfaces of plants that regulate the photosynthetic conversion of CO2 into plant biomass, which serves as a renewable source of food, materials, and bioenergy. A deeper understanding of lamina wall structure, mechanics, and dynamics in stomatal baby-sit cells will help identify plants that can increasingly efficiently use water, a major limiting factor in global agricultural production. The computational image wringer and modeling tools that will be ripened in this project will provide scientists with new ways of interpreting and understanding experimental data.Consideringstomatal baby-sit cells are an wondrous example of cellular engineering by plants and are wieldy and observable by scientists of all ages, a learning module will be ripened and deployed that allows 4th through 8th graders to observe stomatal dynamics first-hand and challenges them to construct and optimize functioning macro-scale models of stomatal baby-sit cells, helping to inspire future scientists and engineers. This project will moreover train two PhD students and a research socialize in interdisciplinary research skills that navigate the boundaries of biology, computer and information science, and engineering. Research Tags: Image Segmentation, 3-D, Plant Biology Integration of Environmental Factors and Causal Reasoning Approaches for Large-Scale Observational Health ResearchSenseResearcher: Vasant Honavar Sponsoring Agency: National Science Foundation Vast quantities of health, environmental, and behavioral data are stuff generated today, yet they remain locked in digital silos. For example, data from health superintendency providers, such as hospitals, provide a dynamic view of the health of individuals and populations from lineage to death. At the same time, government institutions and industry have released troves of economic, environmental, and behavioral datasets, such as indicators of income/poverty, wrongheaded exposure (e.g., air pollution), and ecological factors (e.g., climate) to the public domain. How are economic, environmental, and behavioral factors linked with health? This project will put together numerous sources of large environmental and clinical data streams to enable the scientific polity to write this question. By breaking current data silos, the broader scientific impacts will be wide. First, this effort will foster new routes of biomedical investigation for the big data community. Second, the project will enable discoveries that will have behavioral, economic, environmental, and public health relevance. It moreover aims to hoke a first-ever data warehouse containing numerous health/clinical, environmental, behavioral, and economic data streams to ultimately enable causal discovery between these data sources. The ultimate goal of the project is to facilitate community-led and collaborative causal discovery through dissemination of integrated and unshut big data and analytics tools. Interdisciplinary Research on Emotion Understanding for Smart CitiesSenseResearcher: James Wang Sponsoring Agency:Higherof Information Sciences and Technology This project aims to develop emotion-understanding capabilities for smart cities. The project is highly interdisciplinary, involving theHigherof Information Sciences and Technology, psychology, and statistics. Research Tags: Emotion, Computer Vision Interdisciplinary Workshop on theMinutiaeof a National Broadband ResearchVoucherFaculty Researcher: Carleen Maitland Sponsoring Agency: National Science Foundation This project includes organizing a workshop designed to promote a three-cornered dialog between policymakers, researchers and data managers (implementation agencies), to collectively pinpoint a broadband research voucher that will ultimately provide answers to questions relevant to each of these stakeholders. The organizers will seek to secure the participation of broadband experts from multiple stakeholders, including universities and university united research institutes, regulatory agencies, policy makers, industry bodies, and implementation agencies. A publicly misogynist workshop report will be created to disseminate workshop findings. Maintenance Training Under Uncertainty: Expanding A Smart Tutoring System to SupportVanquishmentand Retention of SkillsSenseResearcher: Frank Ritter Sponsoring Agency: Office of Naval Research Training is important for the Navy. In this research program, we will explore the implications of the KRK learning theory, particularly how it can help warfighters not only to initially learn but moreover to modernize retention of skills. We will examine how variegated learning schedules influence learning retention, and how a range of learning schedules stupefy the learning and retention of variegated skills. We will moreover protract to develop a tutoring system to remoter test and to wield this theory. We will use maintenance of Navy platforms as a domain of study and analysis, particularly for the tutor.Increasinglygeneralized skills and increasingly robust learning is necessary considering Navy platforms increasingly vary as for a given platform its components are less uniform, making the maintenance and undertone problem solving increasingly uncertain, requiring increasingly unstipulated and increasingly robust skill to maintain. Some of the key research areas and questions this program will struggle to wordplay include (1) identifying the learning theories and associated training protocols that promote minutiae of robust and unstipulated maintenance skills, (2) understanding which training architectures will indulge efficient practice of ramified maintenance procedures to proceduralize and generalize knowledge to modernize its availability and retention, and (3) investigating how such an tracery realizes tutors that can unquestionably do the training and whether or not they are effective. Moving Backwards: From mobile phones to wall cards in the Rwandan Cash-based Assistance ProgramsSenseResearcher: Carleen Maitland Sponsoring Agency:Higherof Information Sciences and Technology, Schreyer HonorsHigherICT-based technology incubators have wilt an important component of entrepreneurship and economic growth in the U.S. Yet, it is unclear whether or not these models can transfer to emerging economies. Many studies have identified factors influencing incubators' success in launching businesses. However, these studies take only a narrow view of success as well as the factors involved. This research identifies a novel set of successful outcomes for incubators, taking into worth the realities of less ripened economies. It moreover develops a theory of purlieus institutions that identifies organizational and governmental institutions necessary for success. Research Tags: Cash-based Assistance, Humanitarian Aid, Socio-technical Systems, Rwanda MRI:Vanquishmentof a Nikon SIM & STORM Capable Super-resolution Fluorescent Microscope as a Shared Instrument for the PSU ResearchPolityFaculty Researcher: James Wang Sponsoring Agency: National Science Foundation An ribbon is made to The Pennsylvania State University, University Park campus to purchase a super-resolution microscope that will enable the capture of images of plant and unprepossessing cells, as well as ramified chemical samples, at the scale of single molecules. This microscope will reveal new insights into how living and chemical systems are organized and work. The project will moreover generate new image wringer tools for the scientific community. The microscope will enable interdisciplinary research training and enhance education through coursework and outreach to other Penn State campuses and other institutions. Integration of this microscope into a personnel microscopy facility will make it misogynist to undergraduate, graduate and postdoctoral trainees, and regular imaging workshops will be offered by Penn State. New teaching modules for K-12 and undergraduate educators demonstrating the science of size and the potential of super-resolution microscopy will be developed.Wangleand training will be unpreventable for underrepresented students through programs including the SummerWitsin the EberlyHigherof Science, McNair Scholars, Women in Science and Engineering Research, and Minority Undergraduate Research Experience. Public understanding of super-resolution microscopy and its advantages will be catalyzed by multiple outreach activities and venues, including The Franklin Institute (science museum) and Penn State's Ag Progress Days, which together will expose this cutting-edge imaging technology to tens of thousands of people. The discoveries enabled by this microscope will whop the study of plant and unprepossessing development, sustainable threshing and energy production, and the chemical interactions that pinpoint our physical environment. Research Tags: Image Analysis, 3-D, Plant Biology Multi-Agent Sustainable WaterVisualizationTheory (MUST): Nexus of Water, Road, and Hierarchic Social Contractual SystemsSenseResearcher: Xiang Zhang Sponsoring Agency: National Science Foundation Providing reliable wipe water is essential for health and prosperity. However, our society wontedly faces the disastrous consequences of decisions made without considering their socio-economic context and inadequately considering the interdependency of the hair-trigger infrastructure systems and services. Examples include the incidents of poisonous scum in the water supply in Toledo, Ohio, in the summer of 2014, and increasingly recently, the lead-contaminated water in Flint, Michigan, which lead to a declaration of a State of Emergency by the federal government. A worldwide observation is that decisions that are primarily based on short-term forfeit considerations, such as those made by many municipal and municipality offices when facing resource constraints, can exacerbate water problems rather than modernize them.Hair-triggerwater-related decisions are wontedly faced by decision-makers at variegated levels, i.e. the municipal, polity and individual resident. This CRISP team believes that a sound water infrastructure investment visualization support (WIIDS) model, calibrated with a rich repository of data, will have a transformative impact in a wholesale and long-term socioeconomic context. Such decisions will properly intrust the resources, risks, and responsibilities to unzip long-term economic, societal, and environmental benefits. The goal of this research is to formulate an wide visualization model and use data to support water-related decisions. Particularly, it will consider the interdependency of water systems to the road system (which provides mobility of people and economy while generating loads on the water pipe deterioration). The research activities will be based on a unique testbed of water service provided by the Cleveland Water Department, which supplies water to the municipality of Cleveland and 77 diversified municipalities. This model will describe the interdependency of the water system, road system, and the hierarchic contractual system that defines the terms of services. The model will be calibrated with a repository of data; it will be extended to compare and quantify the benefits of reactive versus predictive maintenance policies. Research Tags: Big Data, Health Informatics, Social Informatics MURI: Adversarial and Uncertain Reasoning for Adaptive Cyber Defense:Towersthe Scientific FoundationSenseResearcher: Peng Liu Sponsoring Agency: George Mason University Today’s cyberdefenses are largely static. They are governed by slow deliberative processes involving testing, security patch deployment, and human-in-the-loop monitoring. As a result, adversaries can systematically probe target networks, pre-plan their attacks, and ultimately persist for long times inside compromised networks and hosts. A new matriculation of technologies, tabbed Adaptive Cyber Defense (ACD), is stuff ripened that presents adversaries with optimally waffly wade surfaces and system configurations, forcing adversaries to continually re-assess and re-plan their cyberoperations. Although these approaches (e.g., moving target defense, dynamic diversity, and bio-inspired defense) are promising, they seem stationary and stochastic, but non-adversarial, environments. This research aims to build the scientific foundations so that system resiliency and robustness in adversarial settings can be thoroughly defined, quantified, measured, and extrapolated in a rigorous and reliable manner. Research Tags: Adaptive Cyber Defense, Adversarial Reasoning Novel Approaches for Mining Large andRamifiedNetworksSenseResearcher: Xiang Zhang Sponsoring Agency: National Science Foundation This project includes an integrated research, education, and outreach program that focuses on the minutiae of novel methods for mining large, ramified networks. Networks (graphs) are ubiquitous in real-world applications. Although successful, the methodology minutiae for network analytics is still in its early stage. This project addresses fundamental questions essential to the urging of large and ramified network analytics. These challenges are driven by real-world applications in social, biological, and medical domains. The research plan is complemented by a comprehensive education and outreach plan focused on (1) the minutiae of new interdisciplinary courses, (2) uncontrived undergraduate involvement in the research projects, and (3) outreach activities including the STEM program targeting K-12 schools. This research aims to proffer the reliability and efficiency of large network wringer by (1) developing novel memory-based random walk proximity measures that can powerfully capture the similarity between nodes, (2) studying the dual-network model and its applications, and (3) designing robust and flexible multi-network algorithms for clustering and ranking. Old is Gold - Co-Production of Healthy Living for the Elderly Through TimeFinancialFaculty Researcher: John M. Carroll, Mary Beth Rosson Sponsoring Agency: National Science Foundation There are and will be too many elderly for society to protract custodial superintendency regimes; and such regimes waste huge potential contributions of healthy elderly whose retirements can last 40 years.  In collaboration with public and private retirement communities, “aging in place” elderly, and key local nonprofits, we are investigating the practices of older adults and their interest in and utilization of technology to facilitate peer-based coproduction of health and wellbeing. Our project takes a pervasive participation tideway to the recognition and facilitation of white-haired as a social resource in modern society. Our original plan was to specifically employ timebanking, but we have x-rated that for increasingly radical approaches we invented. Penn State Biomedical Big Data to Knowledge (B2D2K) Training ProgramSenseResearcher: Vasant Honavar Sponsoring Agency: National Library of Medicine The Biomedical Big Data to Knowledge (B2D2K) Training Program at the Pennsylvania State University will bring together data science researchers and educators from the EberlyHigherof Science, theHigherof Engineering, theHigherof Health and Human Development, theHigherof Engineering, theHigherof Information Sciences and Technology, theHigherof Medicine, and the Geisinger Institute for Genomic Medicine to create a truly transformative multi-disciplinary predoctoral training environment. The B2D2K program aims to train a diverse personnel of next-generation biomedical data scientists with the deep knowledge of Data Science to develop novel algorithmic and statistical methods for towers predictive, explanatory, and causal models through integrative analyses of disparate types of biomedical data (including Electronic Health Records, genomics, behavioral, socio-economic, and environmental data) to whop science and modernize health. Penn State Clinical and Translational Science InstituteSenseResearcher: Vasant Honavar Sponsoring Agency: National Center forUp-and-comingTranslational Sciences Over the past decade, Penn State’s Clinical and Translational Science Institute (CTSI) has ripened into an zippy and visible entity within the University and the institutional home for clinical and translational research. The University has cutting-edge capabilities in a vast variety of vital and unromantic disciplines that are relevant to health and hair-trigger to the discovery and minutiae of innovative tools. This project will catalyze multidisciplinary clinical and translational Team Science by engaging researchers, professionals, and communities wideness and outside the traditional boundaries of biomedicine, from within Penn State and beyond. It will moreover strengthen and slide research values and concerns for the health and healthcare needs of an increasingly diverse population. Last, it will powerfully share resources and expertise with other CTSI Hubs, the CTSA Consortium, and increasingly widely with health practitioners and the public at large. The project aims to dramatically modernize clinical trials processes, progressive the rate at which discoveries are translated into clinical care. It will moreover educate a new generation of health professionals and investigators to successfully write upstanding issues that upspring when technological capabilities and societal imperatives meet with economic and practical constraints. Penn State's CyberCorps; Scholarship for Service ProgramSenseResearcher: Anna Squicciarini, Peter Forster, Nicklaus A. Giacobe, Dongwon Lee Sponsoring Agency: National Science Foundation This project will expand the sufficiency and involvement of Penn State students State in cyber-relevant disciplines. To support student needs, we have implemented a flexible and strong Scholarship for Service (SFS) program, based on customized mentoring for each student. Through the program, we will (1) provide federal employers with unrenowned entry-level professionals with the skills and nature required to meet the Nation’s cybersecurity challenges and rise to the top of their field, (2) enhance our relationship with federal entities to ensure the success of the SFS program, and (3) ignite interest in IA careers and IA programs at Penn State and protract to demonstrate our support to the Nation’s cybersecurity enterprise. Practical Logic of STEM Career Choice: AHair-triggerInterpretiveTidewayto Profiling IT Career Pathways of African American Males at HBCUsSenseResearcher: Lynette (Kvasny) Yarger Sponsoring Agency: National Science Foundation This study traces the career pathways of successful African American male higher students who have opted to pursue IT-related careers. Grounded in the theory of power and practice posited by Pierre Bourdieu, the study uncovers the practical logics that African American men employ when making career choices. The sample consists of 100 African American male students who have expressed interest in IT careers and who are currently enrolled at Historically Black Colleges and Universities (HBCUs). A small sample of African American men at Predominantly White Institutions is moreover included. The research includes conducting person-centered wringer that is well grounded in theory and employs rigorous qualitative approaches. The proposed work contributes to the limited literature on African American men's wonk success and helps sieve some of the mixed and contradictory findings well-nigh their career choices that exist in the current literature. The results of this research reveal how and which social structures enable and constrain African American men's IT-related career choices; information which may be useful to policy makers, teachers and school counselors, and that may inform the megacosm of innovative interventions. Findings may moreover contribute to the increase in the STEM workforce while expanding the career options for African American men who are historically unduly unauthentic by economic downturns. The research continues a collaboration between Washington State University and Pennsylvania State University as well as strengthens ongoing partnerships with the four participating HBCUs. Privacy Protection in Social Networks: Bridging the Gap Between User Perception and Privacy EnforcementSenseResearcher: Dongwon Lee, Peng Liu, Mary Beth Rosson Sponsoring Agency: National Science Foundation With the increasing participation in online social networks, it is hair-trigger to preserve users’ privacy, without preventing them from socialization and information sharing. Unfortunately, existing approaches fall short meeting such requirements. In general, security and privacy problems in social networks can be viewed from two perspectives: human-oriented and technology-centered. These two camps, however, are largely isolated in the literature, and their findings are not integrated with each other. This research will yacky a unifying framework that bridges the gap between the perspectives through which user security and privacy problems in social networks can be viewed. We will (1) study the threats and vulnerabilities of social networks and existing protection approaches; (2) snift the discrepancies between user expectations and very information disclosure; (3) yacky a user-centered yet computationally-efficient formal model of user privacy in social networks, and (4) implement a mechanism to powerfully enforce privacy policies in the proposed model. The solutions will protect user privacy in a way that reconciles users' desire for freely sharing sensitive information in social networks. Research Tags: Social Network, Privacy Model, Privacy Enforcement, Internet Security Proto-Institutionalization of Collaborative Data Analytics in Humanitarian ReliefSenseResearcher: Carleen Maitland Sponsoring Agency: Harnessing data for rapid response in humanitarian crises is a pressing rencontre as well as opportunity. Humanitarian relief organizations work to share data during response, but these efforts must overcome organizational, work, loftiness and technical barriers. This research examines the processes of institutionalization of norms virtually routines and work practices in the sharing of data during a humanitarian crisis. The research will generate knowledge of the process of proto-institutionalization, a theory that has heretofore lacked an empirical basis. It will moreover generate findings for practice with important implications for improving data sharing and collaborative analytics in humanitarian crises. Research Tags: Institutional Theory, Collaborative Data Analytics, Humanitarian Relief Recognizing UnexplainedPoliciesin Network EventsSenseResearcher: Peng Liu, John Yen Sponsoring Agency: U.S. Army Research Laboratory This research aims to unzip three goals in up-and-coming cybersituation sensation when cyberoperation centers are doing cyberanalysis to snift intrusions: (1) will-less data triage through data mining of operation traces of analysts, (2) context enlightened wits retrieval, and (3) Intelligent software teachers that can interact with human analysts. Research Tags: cyberattacks, UnexplainedPoliciesin Network Events Refugees and InformationLiaisonTechnologiesSenseResearcher: Carleen Maitland Sponsoring Agency: National Science Foundation and the United NationsUpperCommissioner for Refugees Refugees are among the world’s most vulnerable people. However, for some, the wronging results in an amazingly resourceful and innovative spirit, unshut to change. Our research examines how InformationLiaisonTechnologies (ICT) can modernize the lives of refugees as well as the operations of their service providers. Important questions include: How does refugee use of IT change wideness the refugee life cycle? Can IT-based service provider systems be used to promote both upward and downward accountability? What new, innovative technologies can be ripened for use in camps? With urban refugees? How can data support polity minutiae among refugees? Research Tags: Cognitive Science,Polityand Social Informatics, Human-Computer Interaction Resilience Analytics: A Data-DrivenTidewayfor Enhanced Interdependent Network ResilienceSenseResearcher: Andrea Tapia Sponsoring Agency: National Science Foundation Recent natural disasters have challenged our traditional approaches of planning for and managing disruptive events. Today, social media provides an opportunity to make use of community-driven data to help us understand the resilience, or lack thereof, of polity networks (e.g., friends, neighborhoods) physical infrastructure networks (e.g., transportation, electric power) and networks of service providers (e.g., emergency responders, restoration crews). This research integrates multiple disciplinary perspectives in engineering, computer science, and social science to write how community-driven data can help (i) understand the policies of these interdependent networks before, during, and without disruptions, and (ii) increasingly powerfully reduce their vulnerability to and enhance their recovery without a disruption. Two research components subsume the proposed effort in resilience analytics. The first component creates a network model of the interdependence of infrastructure networks, the polity networks that they serve, and the service networks engaged to respond without a disruption. We will explore the functional relationships between polity resilience and infrastructure network performance. Model results will enable visualization makers to understand the wastefulness of resilience wideness the several networks and regions. The second component integrates the interdependent network model with community-sourced data to develop a framework of data analytics to largest understand and plan for resilience. This component builds on research in the field of socio-technical systems relating to the wringer of social media data monitored without a disruption. The methods will assess the value of information provided by crowd-sourced data with expertise of polity social scientists. This project draws upon multiple methods wideness several disciplines. The multidisciplinary methods explored in this project are essential for a transilience in resilience analytics. This project aims at taking a significant step forward in our understanding of how real-time data from social media and other sources can describe, predict, and prescribe practices to manage interdependent networks in crises. Reverse Engineering Based Software Diversification for Cyber Fault ToleranceSenseResearcher: Dinghao Wu Sponsoring Agency: Office of Naval Research As cyberthreats wilt commoditized, with a wholesale range of tools on the market that are hands wieldy by attackers, there is a hair-trigger need in defense with streamlined tools. While the cyberthreat techniques and landscape are evolving, with latest technologies unexplored quickly, we are falling overdue in the defense side as software systems takes a long time to mature and production systems have fairly long life cycles. It is plush and technically difficult to patch these legacy systems once widespread zero-day cyberthreats are discovered. This research explores reverse engineering bases diversification and transformation methods for defending such widely spread cyberthreats. Current computer systems are highly homogenous in terms of hardware and software, permitting attackers to wade numerous systems once a worldwide vulnerability is revealed. To reverse such unsymmetrical attacks, we propose a set of reverse engineering based diversification techniques, shipped with an iterative platform which can etch and overdraw small and vital diversification techniques. The proposed techniques and platform can defend cyberattacks and render threats not widely exploitable, as we are worldly-wise to generate thousands of heterogeneous variants of the same software. Our aim is to make the reverse engineering lawmaking recompilable or reassembleable, which can help plicate legacy software systems with modern security mechanisms. Research Tags: Reverse Engineering, Software Diversification, Cyber Fault Tolerance, Software Analysis, Software Security, ProgramWringerScholarship for Service in InformationWarrantyprogram at The Pennsylvania State UniversitySenseResearcher: Anna Squicciarini, Peter Forster Sponsoring Agency: National Science Foundation This program allows Penn State to provide grants through the Federal Cyber Corps Scholarship for Service (SFS) program to students studying in the field of information warranty (IA). Each scholarship recipient will well-constructed either a bachelor's or graduate stratum in Security and Risk Analysis, Information Sciences and Technology, or Computer Science. Each scholarship student must be worldly-wise to well-constructed his or her wonk program within a maximum of two years. Secure Lean BinaryLawmakingFaculty Researcher: Dinghao Wu, Peng Liu Sponsoring Agency: Office of Naval Research Modern software engineering practice heavily relies on third party libraries, existing frameworks, upper level programming languages, and wiry minutiae methodologies, which indulge us to build increasingly ramified software and unhook it faster. These practices, however, rationalization some negative consequences, such as bloatware and full-length creep. When such an using is running in the system, inside its write space is unused (library) code, which exposes uneaten wade surface that gives an attacker increasingly choices in launching attacks. The unused yet shared library lawmaking moreover reduces the software diversity among the applications. Removing such unused lawmaking from each write space will not only lead to leaner and increasingly efficient code, but moreover enable the computer systems to unzip largest “vertical” application-application isolation, reduced wade surface, and enhanced diversity. In this project, we aim to build infrastructure and technologies for software customization, expressly for libraries at binary lawmaking level. We aim to have a set of new capabilities to unzip largest isolation, less sharing and less dependencies between code, and to implicitly diversify software. Research Tags: BinaryLawmakingAnalysis, De-bloating, Software Bloat, Software Analysis, Software Security, ProgramWringerSemantic Trajectory Mining with ContextsSenseResearcher: Zhenhui (Jessie) Li Sponsoring Agency: National Science Foundation Rapid advances of sensing and positioning technologies have provided us with an increasing value of trajectory data placid from human movements, unprepossessing traces, and traffic. Understanding such large-scale trajectory data together with their surrounding contexts (e.g., location information, local events, weather and environment) could goody a number of important applications. For example, a semantic understanding of human trajectories can help profiling a person's interest, socioeconomic status and health conditions; mining traffic patterns w.r.t. local events and weather conditions can lead to a increasingly resilient transportation system; and studying how unprepossessing movements respond to environmental changes can whop our understanding of the ecological system. This project investigates data mining algorithms and provides solutions toward semantic trajectory mining with rich spatial-temporal contexts. The results will have broader impacts in other disciplines such as social science, health, transportation, and monitoring through interdisciplinary collaborations. Research Tags: Big Data SoCS: Studying the Computability of Emotions by Harnessing Massive Online Social DataSenseResearcher: James Wang Sponsoring Agency: National Science Foundation The emergence of massive human-rated and commented visual data has opened avenues for exploring fundamental questions in strained intelligence vastitude the horizon. This project tackles the rencontre of automatically inferring visual philosophy and emotions and inventing new systems that squire creative and decision-making activities of the unstipulated public. An interdisciplinary team, with expertise in visual modeling, data mining, psychology, and computational sciences will build tools to slaver information from a combination of visual, textual, and numerical data. Visual features, selected based on published literature and consultation with domain experts, will be extracted for discriminating types of emotions. The resulting systems can select and rank visual information based on philosophy and emotions. Research Tags: Emotion, Affective Computing, Image Analysis, Machine Learning STEM Education in Virtual Worlds Workshop SeriesSenseResearcher: David Fusco Sponsoring Agency: National Science Foundation The significance and importance of this project is the megacosm of two workshops that will focus on the learning of science, technology, engineering, and mathematics (STEM) subjects in Virtual Worlds and how other education technologies can be augmented with Virtual Worlds. At each workshop, STEM educators will share their research and learn from specialists in education and industry. Anticipated outcomes of the workshops include: improved educational activities for higher students in STEM fields, training sense with a largest understanding of how students learn STEM subjects using Virtual Worlds, and stronger collaborations between educators and Virtual World designers in the computer/software industry. Each four-day workshop will offer in-depth research presentations, hands-on experiences, engaging activities, and lively discussions. The principle investigators will invite approximately 30 attendees to each workshop. In addition, experts in curriculum diamond and towage will help STEM educators understand how to develop activities with achievable, measurable learning objectives. Industry experts, such as programmers and representatives from companies developing virtual reality hardware and software, will share their technical knowledge and learn well-nigh the needs of educators who use their services and products. STEM Workforce Training: A Quasi-ExperimentalTidewayUsing the Effects of Research FundingSenseResearcher: C. Lee Giles Sponsoring Agency: National Science Foundation This is a collaborative project involving Ohio State University (lead institution), Pennsylvania State University, American Institutes for Research, University of Illinois-Urbana, and the University of Iowa. The project examines the impact of variegated research funding structures on the training of graduate students and postdoctoral fellows and the impact of their subsequent outcomes. The rationale for the study is the recognition that research teams are organized differently in composition, size, and reliance of graduate students versus postdoctoral fellows. In addition, funding agencies transpiration the structure of science training by creating programs that encourage interdisciplinary groups, multi-university collaborations, or large research centers that focus on specific research questions. However, little research has been washed-up well-nigh how these factors shape the career preparation of STEM professionals. The project will have wholesale implications for the unshortened field of STEM education policy and research. The underlying algorithms and tools will be made misogynist to the wonk research polity and can be leveraged to link internal human resources data sets to external data sets. This new data infrastructure will moreover facilitate the towage of the effects of research investments on research productivity, as well as undergraduate and graduate curriculum development. SupplyUnitingProcess and Technological Implications of Drug Serialization Mandate in the United StatesSenseResearcher: Peng Liu Sponsoring Agency: CVS Health As companies develop their serialization programs, it is imperative that the industry and supply uniting partners are evolving in a harmonized manner. Current approaches to the serialization merchantry process, however, tend to be driven in silos by specific interest groups rather than an industry-wide approach. This research aims to encourage the end-to-end, industry-wide transformation, an understanding of serialization impacts on supply uniting processes and IT systems is a vital first step. Specifically, we will investigate these two aspects to (1) conceptualize a framework representing the pharmaceutical supply uniting in the evolving serialization eta, and (2) evaluate the serialization data tracery required for both regulatory compliance, supply uniting performance improvements, and potential merchantry value to organizations. Research Tags: Pharmaceutical supply chains, serialization, information flows Supporting Communities with Location-Sensitive/Mobile ApplicationsSenseResearcher: John M. Carroll Sponsoring Agency: Intel Corp. Local polity is an important level of social structure in which humans develop their identities; they understand and practice hair-trigger skills and capacities such as participation, joint awareness, and giving/receiving social support. For most of human history communities were mediated through face-to-face interaction. Community information infrastructures are evolving rapidly in recent decades. Our project investigates local-scale mobile computing support for making polity heritage, values, news and opinion, municipal planning, and slipperiness response increasingly visible and participatory. Topically-Enhanced Algorithms for Keyword Extraction in Document NetworksSenseResearcher: C. Lee Giles Sponsoring Agency: National Science Foundation During these big data times, researchers are exposed to large numbers of research papers, which provide the technological understructure for worldwide collection, sharing and dissemination of scientific discoveries. The most important parts or concepts of the text of a paper are not unchangingly readily available, but are subconscious in the multitude of details that trailblaze them. Keyword extraction, specified as the problem of automatically extracting the important words or concepts of a text, is inside to dealing with the overwhelming amounts of information misogynist in these papers. The goal of this project is to explore robust and well-judged approaches that uncover the most important parts of documents to indulge researchers process increasingly information in less time. In particular, this project will investigate models that take into consideration the linkage between citing and cited documents in a document network and will explore various qualitative and quantitative aspects of the questions: What are the key words or concepts in a document? The proposed models will be based on novel unsupervised approaches that combine the complementary strengths of topic models and graph-based algorithms. In the big picture, this research has the potential to help researchers navigate through the large number of research papers that are misogynist in this information age, which in turn may facilitate progress in science. The results of this proposed research will have a uncontrived pipeline to the CiteSeer digital library. In particular, the proposed models will be tested for their robustness and utility in real world settings. Toward Robust and Scalable Discovering of Significant Associations in Massive Genetic DataSenseResearcher: Xiang Zhang Sponsoring Agency: National Science Foundation A fundamental rencontre in life sciences is the label of genetic factors that underlie phenotypic differences. Thanks to the wide sequencing technologies, an enormous value of genetic variants have been identified and cataloged. Such data hold unconfined potential to understand how genes stupefy phenotypes and contribute to the susceptibility to environmental stimulus. However, the existing computational methods for analyzing and interpreting the high-throughput genetic data are still in their infancy. This research will investigate the computational and statistical principles in modeling and discovering genetic understructure of ramified phenotypes. Specifically, we aim to provide answers to understand how (1) to powerfully and efficiently assess statistical significance of the findings, (2) to worth for the relatedness between samples in genetic undertone study, and (3) to virtuously capture possible interactions between multiple genetic factors and their joint contribution to phenotypic variation. Collectively, the theoretic framework and algorithms will provide the research polity much largest tools to dissect ramified relationships between genotypes and phenotypes, and proceeds deeper understanding of the roles of environmental stimuli. Towards a Computational Infrastructure for Comparing Predictive Analytics Methods on Sensitive DataSenseResearcher: Vasant Honavar, John Yen, Yasser El-Manzalawy Sponsoring Agency: National Science Foundation In many applications, our worthiness to realize the full potential of big data to modernize decisions and outcomes is currently limited by the lack of practical frameworks for wringer of sensitive data in a manner that does not violate workable data wangle and use policies. This project aims to explore a framework and software infrastructure for data wangle and use policy compliant wringer and visualization of sensitive data. It aims to develop a novel “Query, Model, Evaluate and Deploy” (QMED) framework for data wangle and use policy compliant wringer and visualizations of sensitive data. This project seeks to test the feasibility of the proposed framework using predictive and causal modeling of data from an online health polity as a test case. This research will yield a prototype unshut source software infrastructure to support wringer and visualization of sensitive data, slide date-driven advances in domains that involve sensitive data through the wholesale engagement of talent in developing largest algorithms and support the incorporation of hands-on wits with such applications into Data Sciences education through hackathons and competitions organized virtually specific sensitive data sets. TowardsWiryand Privacy-PreservingDejectComputingSenseResearcher: Peng Liu Sponsoring Agency: National Science FoundationDejectcomputing offers many benefits to users, including increased availability and flexibility of resources, and efficiency of equipment. However, privacy concerns are rhadamanthine a major windbreak to users transitioning to deject computing. The privilege diamond of existing deject platforms creates unconfined challenges in ensuring the trustworthiness of deject by granting too much power to the deject administrators, who could launch serious insider attacks by abusing their legalistic privileges. This research uses a well-understood philosophy – separation-of-privilege – in the architectural diamond of a deject platform. The architectural diamond and the strong homomorphic cryptographic tideway protect data privacy in deject environments from variegated angles. This project develops an innovative privacy-driven architectural diamond with one focus on the privilege-level diamond of each software component of a deject platform and flipside on defending insider attacks. This project investigates new mechanisms to de-privilege the deject zookeeper and enable increasingly fine-grained wangle tenancy among the software components of a deject platform.Increasinglyspecifically, the new mechanisms enable wiry configuration of the platform; user-configurable privacy protection; and strong isolation in the user space. The techniques ripened under this project are immensely important as users place increasingly of their data into the deject and rely upon deject providers to alimony that data private. Research Tags: secure deject computing, insider threats, privilege separation Towards Locating MemorySelf-indulgenceVulnerability withCadreDumpSenseResearcher: Xinyu Xing Sponsoring Agency: National Science Foundation Despite the weightier efforts of developers, software inevitably contains flaws that may be leveraged as security vulnerabilities. Modern operating systems integrate various security mechanisms to prevent software faults from stuff exploited. To shirk and hijack program execution, an attacker therefore needs to constantly mutate an exploit and make many attempts. While in their attempts, the exploit triggers a security vulnerability and makes the running process terminate abnormally.Withouta program has crashed and terminated abnormally, it typically leaves overdue a snapshot of its crashing state in the form of a personnel dump. While a personnel dump carries a large value of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory self-indulgence vulnerabilities. As such, previous research mainly seeks full reproducible execution tracing to identify software vulnerabilities in crashes. Such techniques, however, are usually impractical for ramified programs. This research aims to explore, diamond and develop lightweight, systematic, and will-less approaches that run a personnel dump to an informative aid in tracking lanugo memory self-indulgence vulnerabilities. The project aims to (1) develop a technical tideway to modernize the quality of information extracted from personnel dumps, (2) explore a set of technical approaches to enhance this readily-available information, and (3) develop a technical tideway to automatically unriddle enhances personnel dumps and pinpoint the root rationalization of software crashes. Towards Obfuscation-Resilient Software Plagiarism DetectionSenseResearcher: Sencun Zhu, Peng Liu, Dinghao Wu Sponsoring Agency: National Science Foundation Software plagiarism is an act of reusing someone else's code, in whole or in part, into one’s own program in a way that violates the terms of original license. Along with the rapid developing software industry and the splash of unshut source projects, software plagiarism has wilt a very serious threat to Intellectual Property Protection and the health of the open-source-embracing software industry. Meanwhile, software plagiarism and “app repackaging” have wilt plane increasingly worldwide phenomena in the mobile app markets for monetary profit or propagation of malware by inserting malicious payloads into the original apps. This project will study the software plagiarism detection problem in a systematic way. The proposed plagiarism detection methods for PC applications leverage program logic and longest semantically-equivalent-basic-block subsequences. They are capable of detecting partial program plagiarism and moreover provide formal guarantee on obfuscation resilience. The proposed method for mobile apps exploits user interface for plagiarism detection, and this unique diamond wile empowers it to defeat various lawmaking obfuscation techniques. Our research will significantly deter the intention or practice of software plagiarism. It will not only serve as a useful tool in collecting strong plagiarism evidences for lawsuits related to intellectual property, but moreover promote a increasingly healthy and trustworthy sharing environment for the unshut source polity and for the mobile app markets. Broader impact will moreover result from the education and dissemination initiatives. Research Tags: Software Plagiarism Detection, obfuscation Towards Privacy Preserving Online Image SharingSenseResearcher: Anna Squicciarini Sponsoring Agency: National Science Foundation Images are now one of the key enablers of users’ connectivity. Every day, increasingly than 4.5 million images are uploaded on Flickr, and nearly a billion images are posted on Facebook. Various types of images are shared to represent users’ interests and show their experiences for social purpose. While extremely convenient, this level of pervasiveness introduces vigilant privacy concerns such as disclosing unwanted information or sharing images with unintended audiences. Malicious attackers can take wholesomeness of these unnecessary leaks to launch content-aware attacks or plane impersonation attacks. This research investigates approaches to protecting users’ online image privacy. We will develop new techniques to tackle image privacy based on the image content as well as image and users’ meta-data, by (1) inferring the sensitivity of a given image based on the visual properties of the images and the users’ image sharing patterns, and then automatically applying the towardly privacy settings for that image; and (2) by using discovered users’ sharing patterns to pinpoint wangle policies equal to the locally enforceable controls on the domain of interest. This work will entail a ramified set of methodologies, including machine learning, wangle control, and information retrieval. TribalNet: Expanding Internet Accessibility and Participation on Native American ReservationsSenseResearcher: Carleen Maitland Sponsoring Agency: National Science Foundation Tribal communities represent the final frontiers of internet wangle in the U.S., with broadband internet wangle misogynist to fewer than 10 percent of Native Americans on tribal reservation lands. The lack of broadband wangle is caused by a hodgepodge of challenges, including remote terrain, inadequate funding, and ramified telecommunication policies. Yet Native Americans need reliable avenues for participation and contribution to internet content to strengthen their communities. This project investigates technologies that will increase internet availability on reservation lands. Further, it will develop new methodologies of disseminating internet content to reservation residents, prioritizing content by relevance during periods of limited connectivity. The goal of the research is to make hair-trigger inroads to write the lack of internet wangle on tribal reservations, to increase the number of Native American reservation residents who are worldly-wise to engage with, create, and disseminate Internet and on-line social network content. Updating the Militarized Dispute Data Through Crowdsourcing: MID5, 2011-2017SenseResearcher: David Reitter Sponsoring Agency: National Science Foundation The Correlates of War Project's Militarized Interstate Dispute (MID) Data is the most prominent and heavily used data hodgepodge in the study of international conflict. The most recent version (MID4) was released in 2014 and brings the period covered to 1816-2010. The MID4 project utilized streamlined text nomenclature procedures to make the process of identifying relevant news stories increasingly efficient. Over the undertow of that project, the principle investigators (PIs) unswayable the primary stickup in the workflow was the coding of those news documents. To write this inefficiency, the PIs completed a pilot project to determine whether crowdsourcing techniques could be used to lawmaking these documents. In the pilot, non-expert workers were paid small sums to read documents and to wordplay sets of questions, the answers to which were used to identify features of possible militarized incidents (the events that subsume MIDs). A systematic comparison of the crowdsourced responses with those of MID4 Project's trained coders revealed that the crowdsourced codings were completely well-judged for 68 percent of the news reports coded; increasingly importantly, upper try-on among prod responses on specific reports was strongly associated with correct coding. This enables the PIs to snift which documents require remoter expert involvement. As a result, the PIs can produce a majority of the MID data in near-realtime and at limited financial cost. These procedures are unromantic on the MID5 Project, which will update the MID data for the period 2011-2017. User-centered MultipartyWangleControl forJointContent ManagementSenseResearcher: Anna Squicciarini Sponsoring Agency: National Science Foundation This project will develop models and techniques to facilitate controlled information sharing of users' data in domains where the data is associated with and co-managed by multiple users, such as bio-repositories, remote teleworking, and social computing. Specifically, we will (1) build on prior work to develop a foundational model describing wangle tenancy in terms of the visualization making process of a single content manager or content owner, laying the groundwork for the second objective; (2) develop new models to support synchronous, asynchronous, and combined joint specification of wangle tenancy policies for shared content for multiple users and site administrators; and (3) wield those solution concepts to two specific applications, group work and a biobank, and self-mastery user studies to test goodness of fit, suitability and feasibility of the resulting wangle setting mechanisms. This project takes an innovative user-centric tideway to ensure that the rigorous models ripened result in enforceable mechanisms that can be used on a variety of existing platforms in and multiple domains. To succeed this, the proposed work draws from multiple disciplines, including wangle control, game theory for security and privacy, and visualization support systems. This research will provide users with the worthiness to express preferred wangle tenancy settings for shared multi-owned data, jointly influencing with that input the final wangle settings, while taking into worth organizational constraints and existing laws. Using Big Data toLargestPredict Severe WeatherSenseResearcher: James Wang Sponsoring Agency: National Science Foundation Severe weather causes an enormous value of damages to life and property virtually the world. One indication of severe weather is a wind pattern known as “bow echoes,” which is currently manually identified by meteorologists. We are analyzing vast historical data placid by the National Oceanic andUndercurrentAdministration (NOAA) to develop an framework that can automatically and virtuously identify bow echoes as they uncork to form, potentially providing largest and older forecasting of severe weather. Research Tags: Big Data, Information Fusion and Visualization, Weather, Satellite Imaging, Radar Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data Intensive ScienceSenseResearcher: Vasant Honavar, Mary Beth Rosson, C. Lee Giles Sponsoring Agency: Rutgers-The State University of New Jersey This project develops a virtual data collaboratory that can be accessed by researchers, educators, and entrepreneurs wideness institutional and geographic boundaries, fostering polity engagement and progressive interdisciplinary research. A federated data system is created, using existing components and towers upon existing cyberinfrastructure and resources in New Jersey and Pennsylvania. The end product is a fully-developed system for collaborative use by the research and education community. A data management and sharing system is constructed, based largely on commercial off-the-shelf technology. The storage system is based on the Hadoop Distributed File System (HDFS), a Java-based file system providing scalable and reliable data storage, designed to span large clusters of thingamabob servers. The Fedora and VIVO object-based storage systems are used, enabling linked data approaches. The system will be integrated with existing research data repositories, such as the Ocean Observatories Initiative and Protein DataWallrepositories. The project moreover develops a custom site federation and data services layer; the data services layer provides services for data linking, search, and sharing; coupling to computation, analytics, and visualization; mechanisms to nail unique Digital Object Identifiers (DOIs), gazetteer data, and will widely publish to internal and wider audiences; and manage the long-term data lifecycle, ensuring immutable and pure data and reproducible research. Virtual Intelligent Tutor for the Andragogy of Military Medicine Integrated Skills (VITAMMINS)SenseResearcher: Frank Ritter Sponsoring Agency: Charles River Analytics, Inc. VITAMINS will create and test tutors on trauma nursing for the Defense Health Program. The tutors will be based on the D2P tutoring tracery and previous work on the Air Force Trauma nursing personnel course. They will include STAT-Guy from our collaborators at Charles River Analytics. The tutors will be tested using PSU nursing students. Visual Cortex on SiliconSenseResearcher: John M. Carroll, Mary Beth Rosson Sponsoring Agency: National Science Foundation Expeditions in Computing are the top-tier of research awards NSF makes in Computer and Information Science; this project is quite wholesale and would-be in many areas. The project leverages low-power, top-down “gist”-based computer vision hardware and algorithms to enable a new generation of smart-camera visual prosthetics. Our focus is on the potential utilization of these new technologies by visually wordless people. We created a permanent participatory research partnership with a dozen local people who have helped us to understand their daily activities in unconfined detail, and who have helped to investigate a series of prototypes. We have moreover worked with them to sieve what assistive technology ways in this context.  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