Recent Submissions

  • Two Tier Analysis of Social Media Collaboration for Student Migration 

    Razavisousan, Ronak; Joshi, Karuna P. (IEEE, 2019-12-14)
    Global adoption of Social Media as the preferred medium for collaboration and information exchange is increasingly reshaping social realities and facilitating new research methodologies in various disciplines. Social Media ...
  • Differential Fairness 

    Foulds, James R.; Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei (2019)
    We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by the framework of intersectionality, a critical lens arising from the humanities literature, leveraging ...
  • Demo Abstract: ByzGame, a Visualized and Understandable BFT Consensus 

    Clavin, James R.; Duan, Sisi (Association for Computing Machinery, 2019-12-09)
    Byzantine Fault Tolerance (BFT) is the only generic technique that tolerates arbitrary failures in distributed systems, and can be used as a core primitive in building consensus in blockchains. Numerous BFT protocols have ...
  • Performance Benchmarking of Data Augmentation and Deep Learning for Tornado Prediction 

    Barajas, Carlos A.; Gobbert, Matthias K.; Wang, Jianwu (2019)
    Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic ...
  • A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data 

    Parameshwarappa, Pooja; Chen, Zhiyuan; Koru, Güneṣ (2019-08-21)
    Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, ...
  • Towards Effective Technical Debt Decision Making in Software Startups: Early-Stage 

    Aldaeej, Abdullah (Association for Computing Machinery, 2019-11)
    Technical Debt (TD) is a metaphor used to describe outstanding software maintenance tasks or shortcuts made in the software development to achieve short-term benefits (i.e. time to market), but negatively impact the software ...
  • Communication Cost of Single-user Gesturing Tool in Laparoscopic Surgical Training 

    Feng, Yuanyuan; Li, Katie; Semsar, Azin; McGowan, Hannah; Mun, Jacqueline; Zahiri, H. Reza; George, Ivan; Park, Adrian; Kleinsmith, Andrea; Mentis, Helena M. (Association for Computing Machinery, 2019-05-04)
    Multi-user input over a shared display has been shown to support group process and improve performance. However, current gesturing systems for instructional collaborative tasks limit the input to experts and overlook the ...
  • Integrating Artificial Intelligence into Weapon Systems 

    Feldman, Philip; Dant, Aaron; Massey, Aaron (2019-05-10)
    The integration of Artificial Intelligence (AI) into weapon systems is one of the most consequential tactical and strategic decisions in the history of warfare. Current AI development is a remarkable combination of ...
  • Anomaly Detection Models for Smart Home Security 

    Ramapatruni, Sowmya; Narayanan, Sandeep Nair; Mittal, Sudip; Joshi, Anupam; Joshi, Karuna (IEEE, 2019-08-29)
    Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized ...
  • Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes 

    Hussung, Steve; Mahmud, Suhail; Sampath, Akila; Wu, Mengxi; Guo, Pei (HPCF UMBC, 2019)
    Identification of causal networks in atmospheric teleconnection patterns has applications in many climate studies. We evaluate and compare three data-driven causal discovery methods in locating and linking causation of ...
  • Dust Detection in Satellite Data using Convolutional Neural Networks 

    Cai, Changjie; Lee, Jangho; Shi, Yingxi Rona; Zerfas, Camille; Guo, Pei (HPCF UMBC, 2019)
    Atmospheric dust is known to cause health ailments and impacts earth’s climate and weather patterns. Due to the many issues atmospheric dust contributes to, it is important to study dust patterns and how it enters the ...
  • Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data 

    Barajas, Carlos; Guo, Pei; Mukherjee, Lipi; Hoban, Susan; Wang, Jianwu; Jin, Daeho; Gangopadhyay, Aryya; Gobbert, Matthias K. (Springer, Cham, 2019-10-08)
    The study of clouds, i.e., where they occur and what are their characteristics, plays a key role in the understanding of climate change. Clustering is a common machine learning technique used in atmospheric science to ...
  • Anomaly Detection: Under the [data] hood in Smart Cars 

    Quader, Faisal; Janeja, Vandana P. (IEEE, 2019-08-01)
    This research focuses on discovering baseline models for driving behavior and vehicle functioning from data in smart cars. This facilitates detection of anomalous behaviors that deviate from such baselines. Human behavioral ...
  • An overview of online trust: Concepts, elements, and implications 

    Wang, Ye Diana; Emurian, Henry H. (Elsevier Ltd., 2004-02-18)
    Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the ...
  • Mitigating Demographic Biases in Social Media-Based Recommender Systems 

    Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei; Foulds, James (Association for Computing Machinery, 2019-08-04)
    As a growing proportion of our daily human interactions are digitized and subjected to algorithmic decision-making on social media platforms, it has become increasingly important to ensure that these algorithms behave in ...
  • A Bayesian Data Analytics Approach to Buildings’ Thermal Parameter Estimation 

    Pathak, Nilavra; Foulds, James; Roy, Nirmalya; Banerjee, Nilanjan; Robucci, Ryan (Association for Computing Machinery, 2019-06-28)
    Modeling buildings’ heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents’ behavior. Gray-box models offer an explanation ...
  • Social Media-based User Embedding: A Literature Review 

    Pan, Shimei; Ding, Tao (2019-06)
    Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number ...
  • Developing Machine Learning Based Predictive Models for Smart Policing 

    Elluri, Lavanya; Mandalapu, Varun; Roy, Nirmalya (IEEE, 2019-08-01)
    Crimes are problematic where normal social issues are confronted and influence personal satisfaction, financial development, and quality-of-life of a region. There has been a surge in the crime rate over the past couple ...
  • Causes and Effects of the Presence of Technical Debt in Agile Software Projects 

    Rios, Nicolli; Mendonça, Manoel; Seaman, Carolyn; Spínola, Rodrigo Oliveira (2019)
    The current software development scenario is characterized by a wide adoption of agile methodologies. Despite its benefits, agile software development (ASD) is also vulnerable to technical debt (TD). In fact, due to its ...
  • Understanding automated and human-based technical debt identification approaches-a two-phase study 

    Spínola, Rodrigo O.; Zazworka, Nico; Vetro, Antonio; Shull, Forrest; Seaman, Carolyn (Springer London, 2019-06-08)
    Context The technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools ...

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