Browsing by Author "Computer Information and Graphics Technology, School of Engineering and Technology"

Now showing 1 - 10 of 51
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    A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving
    (IEEE, 2021) Islam, Muhammad Mobaidul; Newaz, Abdullah Al Redwan; Tian, Renran; Homaifar, Abdollah; Karimoddini, Ali; Computer Information and Graphics Technology, School of Engineering and Technology
    This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods.
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    Accelerating complex modeling workflows in CyberWater using on-demand HPC/Cloud resources
    (IEEE, 2021-09) Li, Feng; Chen, Ranran; Fu, Yuankun; Song, Fengguang; Liang, Yao; Ranawaka, Isuru; Pamidighantam, Sudhakar; Luna, Daniel; Liang, Xu; Computer Information and Graphics Technology, School of Engineering and Technology
    Workflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present an on-demand access mechanism to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor and analyze scientific workflows in the CyberWater project. Cyber-Water is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent library, which can be integrated into the local desktop environment to allow on-demand usage of remote resources for hydrology-related workflows. LaunchAgent manages authentication to remote resources, prepares the computationally-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors the quality of services for the users. LaunchAgent interacts seamlessly with other existing components in CyberWater, which is now able to provide advantages of both feature-rich desktop software experience and increased computation power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrology workflow that consists of both local and remote tasks can be constructed and show that the added on-demand HPC/Cloud usage helps speeding up hydrology workflows while allowing intuitive workflow configurations and execution using a desktop graphical user interface.
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    ACTS: Extracting Android App Topological Signature through Graphlet Sampling
    (IEEE, 2016-10) Peng, Wei; Gao, Tianchong; Sisodia, Devkishen; Saha, Tanay Kumar; Li, Feng; Al Hasan, Mohammad; Computer Information and Graphics Technology, School of Engineering and Technology
    Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App Packages (APKs). Specifically, by leveraging recent advances in graphlet sampling, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features.
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    Analyzing the Correlations between the Uninsured and Diabetes Prevalence Rates in Geographic Regions in the United States
    (IEEE, 2017-07) Luo, Xiao; Computer Information and Graphics Technology, School of Engineering and Technology
    The increasing prevalence of diagnosed diabetes has drawn attentions of researchers in recently years. Research has been done in finding the correlations between diabetes prevalence with socioeconomic factors, obesity, social behaviors and so on. Since 2010, diabetes preventive services have been covered under health insurance plans in order to reduce diabetes burden and control the increasing of diabetes prevalence. In this study, a hierarchical clustering model is proposed by using Expectation-Maximization algorithm to investigate the correlations between the uninsured and diabetes prevalence rates in 3142 counties in United States for years from 2009 to 2013. The results identified geographic disparities in the uninsured and diabetes prevalence rates of individual years and over consecutive years.
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    Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data
    (Sage, 2021) Luo, Xiao; Storey, Susan; Gandhi, Priyanka; Zhang, Zuoyi; Metzger, Megan; Huang, Kun; Computer Information and Graphics Technology, School of Engineering and Technology
    This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.
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    Android Malware Detection via Graphlet Sampling
    (IEEE, 2018-11) Gao, Tianchong; Peng, Wei; Sisodia, Devkishen; Saha, Tanay Kumar; Li, Feng; Al Hasan, Mohammad; Computer Information and Graphics Technology, School of Engineering and Technology
    Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App PacKages (APKs). Specifically, by leveraging recent advances on graphlet mining, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features.
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    Applying Rating Systems to Challenge Based Cybersecurity Education
    (IEEE, 2017-01) Samuels, Andrew; Li, Feng; Justice, Connie; Computer Information and Graphics Technology, School of Engineering and Technology
    As technology becomes a larger part of everyday life, it becomes increasingly more important for CS and CIT students to learn about cyber security during their education. While many cyber security oriented courses exist, it is also necessary that students must be able to work and learn in an environment that resembles a real world context. To address this problem it has become common to adapt cyber security challenges into the classroom as a method for students to put their knowledge into practice. One problem is that these challenges can vary considerably in levels of difficulty, which makes it problematic for students to be able to select a challenge that is an appropriate difficulty for their skill level. A potential solution to this problem could be to adapt a rating system to rank both the students and the challenges. This would then allow the students to easily select challenges that are appropriate for them to engage with by comparing their own rating with the rating of available challenges. In this project we propose methods that could be used to adapt a rating system to an existing cyber security education program. Finally we propose a method to survey students that interact with the program so that the effect of the rating system can be measured.
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    Can Early-Assignment Grades Predict Final Grades in IT Courses?: American Society for Engineering Education
    (2017) Ramanathan, Parameswari; Fernandez, Eugenia; Computer Information and Graphics Technology, School of Engineering and Technology
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    Concept embedding-based weighting scheme for biomedical text clustering and visualization
    (BioMed Central, 2018-11-01) Luo, Xiao; Shah, Setu; Computer Information and Graphics Technology, School of Engineering and Technology
    Biomedical text clustering is a text mining technique used to provide better document search, browsing, and retrieval in biomedical and clinical text collections. In this research, the document representation based on the concept embedding along with the proposed weighting scheme is explored. The concept embedding is learned through the neural networks to capture the associations between the concepts. The proposed weighting scheme makes use of the concept associations to build document vectors for clustering. We evaluate two types of concept embedding and new weighting scheme for text clustering and visualization on two different biomedical text collections. The returned results demonstrate that the concept embedding along with the new weighting scheme performs better than the baseline tf–idf for clustering and visualization. Based on the internal clustering evaluation metric-Davies–Bouldin index and the visualization, the concept embedding generated from aggregated word embedding can form well-separated clusters, whereas the intact concept embedding can better identify more clusters of specific diseases and gain better F-measure.
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    Cyber-Informed: Bridging Cybersecurity and Other Disciplines
    (2020) Sample, Char; Loo, Sin Ming; Justice, Connie; Taylor, Eleanor; Hampton, Clay; Computer Information and Graphics Technology, School of Engineering and Technology
    A recent study by Cybersecurity Ventures (Morgan 2018), predicts that 3.5 million cybersecurity jobs around the world will be unfilled by 2021. In the United States, the demand for professionals with cybersecurity expertise is outpacing all other occupations (NIST 2018). These reports, along with many others, underpin the need for increasing workforce development initiatives founded in cybersecurity principles. The workforce shortage is across all cybersecurity domains, yet problems continue to persist, as the lines between combatants and non-combatants are blurred. Combating this persistent threat, which is a 24/7 operation, requires a more aggressive and inclusive approach. Higher education institutions are positioned to fully support cybersecurity workforce development; cybersecurity needs people with different perspectives, approaches, ways of thinking, and methods to solve current and emerging cyber challenges. This need is especially pressing when assessing the digital landscape - a tireless and ever-expanding connectivity supported by societal needs, and economic development yet compromised by the common criminal to nation-state sponsored felonious activity. Educators need to consider augmenting their approaches to educating students to include cybersecurity content. In this technology forward world, one that is expanding more rapidly than society and policy can react, increases the imperative for fundamental cyber defence skills. Accordingly, all students, no matter the major, should, minimally, understand the implications of good versus bad cyber hygiene. STEM graduates will require awareness of cyber issues that impact the security of programs, systems, codes or algorithms that they design. Operationally focused cyber-security graduates require a curriculum for careers dedicated to protecting and defending cyber systems in domain specific environments. In a world of Internet of Things (IoT), the ability for individual disciplines to understand the impact of cyber events in environments outside of traditional cybersecurity networks is critically important. This will provide the next generation defenders with domain specific cybersecurity knowledge that is applicable to specific operating environments.