Browsing by Author "Li, Ming"

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    Chinese Students’ Perceptions of the Motivational Climate in College English Courses: Relationships Between Course Perceptions, Engagement, and Achievement
    Li, Ming; Jones, Brett D.; Williams, Thomas O.; Guo, Yingjian (Frontiers Media, 2022-05-23)
    Effective teachers create a motivational climate that engages students in course activities in ways that lead to increased learning and achievement. Although researchers have identified motivational climate variables that are associated with students’ engagement and achievement, less is known about how these variables are related in different courses and cultures. The purpose of the two studies presented in this paper was to contribute to this research literature by examining these associations within the context of college English courses in two Chinese universities. Specifically, we investigated the relationships between students’ perceptions of the motivational climate (i.e., perceptions of empowerment/autonomy, usefulness, success, interest, and caring), cognitive and behavioral engagement, and achievement. This is the first study to examine the connections between all of these variables in one path model in college English courses in China. We administered surveys at two different Chinese universities (n = 332 and 259) and used regression and path analysis to examine the relationships among the variables. We demonstrated that (a) students’ perceptions of the motivational climate were related to their cognitive engagement, (b) cognitive engagement was related to their behavioral engagement, and (c) behavioral engagement predicted their achievement. These findings are consistent with and extend the growing body of literature on motivational climate and engagement, and they highlight the importance of some motivational climate perceptions over others as significant predictors of cognitive engagement. We conclude that effective English language teachers in China do the following: help students to believe that they can be successful, trigger and maintain students’ interest, and empower students by providing them with choices in activities and assignments.
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    A Cross-Cultural Validation of the MUSIC® Model of Academic Motivation Inventory: Evidence from Chinese- and Spanish-Speaking University Students
    Jones, Brett D.; Li, Ming; Cruz, Juan M. (Hipatia Press, 2017-02-24)
    The purpose of this study was to examine the extent to which Chinese and Spanish translations of the College Student version of the MUSIC ® Model of Academic Motivation Inventory (MUSIC Inventory; Jones, 2012 ) demonstrate acceptable psychometric properties. We surveyed 300 students at a university in China and 201 students at a university in Colombia using versions of the MUSIC Inventory that were translated into Chinese and Spanish, respectively. To assess the psychometric properties of the inventory, we examined: (a) the internal consistency reliabilities for all of the scales, (b) the fit indices and factor loadings produced from confirmatory factor analysis, and (c) correlations between the MUSIC Inventory scales and behavioral and cognitive engagement. The results provide evidence that the Chinese and Spanish translations of the MUSIC Inventory demonstrate acceptable psychometric properties for use with undergraduate students. Therefore, instructors and researchers can use the translated inventories to assess students’ perceptions of the five MUSIC ® Model of Motivation components.
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    Extensions to Radio Frequency Fingerprinting
    Andrews, Seth Dixon (Virginia Tech, 2019-12-05)
    Radio frequency fingerprinting, a type of physical layer identification, allows identifying wireless transmitters based on their unique hardware. Every wireless transmitter has slight manufacturing variations and differences due to the layout of components. These are manifested as differences in the signal emitted by the device. A variety of techniques have been proposed for identifying transmitters, at the physical layer, based on these differences. This has been successfully demonstrated on a large variety of transmitters and other devices. However, some situations still pose challenges: Some types of fingerprinting feature are very dependent on the modulated signal, especially features based on the frequency content of a signal. This means that changes in transmitter configuration such as bandwidth or modulation will prevent wireless fingerprinting. Such changes may occur frequently with cognitive radios, and in dynamic spectrum access networks. A method is proposed to transform features to be invariant with respect to changes in transmitter configuration. With the transformed features it is possible to re-identify devices with a high degree of certainty. Next, improving performance with limited data by identifying devices using observations crowdsourced from multiple receivers is examined. Combinations of three types of observations are defined. These are combinations of fingerprinter output, features extracted from multiple signals, and raw observations of multiple signals. Performance is demonstrated, although the best method is dependent on the feature set. Other considerations are considered, including processing power and the amount of data needed. Finally, drift in fingerprinting features caused by changes in temperature is examined. Drift results from gradual changes in the physical layer behavior of transmitters, and can have a substantial negative impact on fingerprinting. Even small changes in temperature are found to cause drift, with the oscillator as the primary source of this drift (and other variation) in the fingerprints used. Various methods are tested to compensate for these changes. It is shown that frequency based features not dependent on the carrier are unaffected by drift, but are not able to distinguish between devices. Several models are examined which can improve performance when drift is present.
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    An Intervention to Increase Students' Engagement and Achievement in College English Classes in China using the MUSIC Model of Motivation
    Li, Ming (Virginia Tech, 2017-06-01)
    Communicative Language Teaching (CLT) is regarded as an effective approach to teaching foreign languages because it focuses on students' engagement and communicative competence. In the realm of educational psychology, researchers have identified many teaching strategies that can have positive effects on students' motivation and engagement. Jones (2009, 2015) synthesized these strategies and created the MUSIC® Model of Motivation. MUSIC is an acronym for the strategies related to eMpowerment, Usefulness, Success, Interest and Caring. The MUSIC model can be used to help instructors to redesign their instruction to motivate and engage their students in learning activities. The purpose of this research was to examine the effectiveness of incorporating the MUSIC model strategies into CLT classes at a university in China. I used a self-report survey comprised of seven subscales (representing five motivation-related variables and two engagement variables) to collect data on students' course perceptions and their engagement in a college English class. The participants were first year college students at a university in central China (n = 259). Independent samples t-tests, regression, and correlation were used to answer the following two research questions: 1. Is there a difference in students' motivation and achievement in traditional lecture classes versus CLT classes that incorporate MUSIC model strategies? 2. To what extent do students' MUSIC model perceptions relate to their engagement and achievement? The results indicated that there was a significant difference between the traditional lecture class and the CLT classes incorporating MUSIC model strategies. Students in CLT classes perceived more control in the class, found the course to be more useful, were more interested, and perceived more caring from their teacher. As a result, students in CLT classes put forth more effort and achieved higher scores on a standardized English test. In addition, the results revealed that students' MUSIC model perceptions predicted their engagement both in CLT classes and the traditional classes. However, the results showed that the MUSIC model components did not significantly predict student achievement. These findings suggest that the MUSIC model and the MUSIC Inventory are ideal tools for Chinese college English teachers to use when they design instruction.
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    Physical Hijacking Attacks against Object Trackers
    Muller, Raymond; Man, Yanmao; Celik, Z. Berkay; Li, Ming; Gerdes, Ryan M. (ACM, 2022-11-07)
    Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector’s accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object’s bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average.
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    POSTER: Passive Drone Localization Using LTE Signals
    Sun, Mingshun; Guo, Zhiwu; Li, Ming; Gerdes, Ryan M. (ACM, 2022-05-16)
    Drones raise significant privacy and security threats, by intruding into the airspace of private properties or unauthorized regions. Being able to detect and localize the encroaching drones is essential to build geofencing systems to prevent drone misuse. While most existing approaches focus on detecting and localizing active drones, passive drones that do not emit signals are particularly challenging to localize, without requiring advanced hardware. In this work, we propose a novel, low-cost passive drone localization approach, by leveraging opportunistic environmental RF signals (e.g., LTE or WiFi) that reflect off the target drone, with only a single wireless receiver.We implement a prototype system on a USRP-device based testbed, with standard LTE signals emitted by multiple distributed transmitters, and conduct experiments on top of a campus building to evaluate its performance. We also perform a drone detection range analysis to extrapolate the real-world applicability of our scheme.
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    Recycling Preconditioners for Sequences of Linear Systems and Matrix Reordering
    Li, Ming (Virginia Tech, 2015-12-09)
    In science and engineering, many applications require the solution of a sequence of linear systems. There are many ways to solve linear systems and we always look for methods that are faster and/or require less storage. In this dissertation, we focus on solving these systems with Krylov subspace methods and how to obtain effective preconditioners inexpensively. We first present an application for electronic structure calculation. A sequence of slowly changing linear systems is produced in the simulation. The linear systems change by rank-one updates. Properties of the system matrix are analyzed. We use Krylov subspace methods to solve these linear systems. Krylov subspace methods need a preconditioner to be efficient and robust. This causes the problem of computing a sequence of preconditioners corresponding to the sequence of linear systems. We use recycling preconditioners, which is to update and reuse existing preconditioner. We investigate and analyze several preconditioners, such as ILU(0), ILUTP, domain decomposition preconditioners, and inexact matrix-vector products with inner-outer iterations. Recycling preconditioners produces cumulative updates to the preconditioner. To reduce the cost of applying the preconditioners, we propose approaches to truncate the cumulative preconditioner updates, which is a low-rank matrix. Two approaches are developed. The first one is to truncate the low-rank matrix using the best approximation given by the singular value decomposition (SVD). This is effective if many singular values are close to zero. If not, based on the ideas underlying GCROT and recycling, we use information from an Arnoldi recurrence to determine which directions to keep. We investigate and analyze their properties. We also prove that both truncation approaches work well under suitable conditions. We apply our truncation approaches on two applications. One is the Quantum Monte Carlo (QMC) method and the other is a nonlinear second order partial differential equation (PDE). For the QMC method, we test both truncation approaches and analyze their results. For the PDE problem, we discretize the equations with finite difference method, solve the nonlinear problem by Newton's method with a line-search, and utilize Krylov subspace methods to solve the linear system in every nonlinear iteration. The preconditioner is updated by Broyden-type rank-one updates, and we truncate the preconditioner updates by using the SVD finally. We demonstrate that the truncation is effective. In the last chapter, we develop a matrix reordering algorithm that improves the diagonal dominance of Slater matrices in the QMC method. If we reorder the entire Slater matrix, we call it global reordering and the cost is O(N^3), which is expensive. As the change is geometrically localized and impacts only one row and a modest number of columns, we propose a local reordering of a submatrix of the Slater matrix. The submatrix has small dimension, which is independent of the size of Slater matrix, and hence the local reordering has constant cost (with respect to the size of Slater matrix).
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    Remote Perception Attacks against Camera-based Object Recognition Systems and Countermeasures
    Man, Yanmao; Li, Ming; Gerdes, Ryan M. (ACM, 2023)
    In vision-based object recognition systems imaging sensors perceive the environment and then objects are detected and classified for decision-making purposes; e.g., to maneuver an automated vehicle around an obstacle or to raise alarms for intruders in surveillance settings. In this work we demonstrate how camera- based perception can be unobtrusively manipulated to enable an attacker to create spurious objects or alter an existing object, by remotely projecting adversarial patterns into cameras, exploiting two common effects in optical imaging systems, viz., lens flare/ghost effects and auto-exposure control. To improve the robustness of the attack, we generate optimal patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We experimentally demonstrate our attacks using a low-cost projector on three different cameras, and under different environments. Results show that, depending on the attack distance, attack success rates can reach as high as 100%, including under targeted conditions. We develop a countermeasure that reduces the problem of detecting ghost-based attacks into verifying whether there is a ghost overlapping with a detected object. We leverage spatiotemporal consistency to eliminate false positives. Evaluation on experimental data provides a worst-case equal error rate of 5%.