Smartphone App Security: Vulnerabilities and Implementations

Due to the high occupancy volume of smartphones in mode society, more and more developers join the smartphone app market and develop various mobile applications that could benefit out life in many ways. However, smartphone apps are often blamed for insecurities due to smartphone technologies as well as inexperienced app developers. In this thesis work, we study smartphone app security vulnerabilities due to either improper implementations or improper use of smartphone technologies. More specifically, we study potential security vulnerabilities in three categories of apps: apps which use the secure socket layer(SSL) protocol for secure communication, apps which use the WebView technology, and apps which are HTML5-based. For each category of apps, we analyze the underlying technologies to show the cause of vulnerabilities, and develop instruction materials for each of the three validation attacks we have implemented and turn them into security teaching labs. These security teaching labs aim to help students to understand the theoretical attack concepts in and accurate and understandable way and cultivate the hacking mindset.Master of Science (MS)Computer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143522/1/Linxi-thesis-submission.pdfDescription of Linxi-thesis-submission.pdf : Thesi

Development of the Life After Sports Transition (LAST) Online Course for Collegiate Student-Athletes: Pretest-Posttest Study

Transitioning into athletic retirement can have negative impacts on college student-athletes’ psychological, social, emotional, and physical well-being, yet few educational programs exist to help augment college student-athlete preparation for embracing life after sports. The objective of this study was to develop and evaluate a new Life After Sports Transition (LAST) online course for college student athletes. A single group pretest-post-test study evaluated effects of the LAST course among a convenience sample of college student-athletes (n=10) attending a NCAA Division I university. Paired sample t-tests examined changes in athletic identity, psychological well-being, hope, and self-reflection/insight. Propensity score matching (PSM) of pretest scores and age was used to reduce effects of the pretest differences in the small sample. At posttest, participants were also asked to assess the overall quality of the online LAST course. There was a decrease observed in athletic identity scores from pretest to posttest which approached statistical significance (P=.06). PSM analyses indicated that participants with higher GPA scores had significantly higher environmental mastery (b=2.28, SE=0.49, Pb=2.78, SE=1.20, P=.02, 95% CI: 0.42 to 5.14) scores at post-test than participants with lower GPA scores. However, contrary to our hypotheses, participants also reported lower scores on self-reflection/insight (P=.004, Hedges g = 1.65) and self-acceptance (P=.042, Hedges’ g = 0.93) at post-test. Despite these counter intuitive findings, participants rated the LAST course highly on most distance education quality dimensions. While student-athlete participation in the LAST course was associated with a decline in athletic identity, findings suggest that future life after sports programs focus more on introspective mediators of lifestyle change (i.e., self-reflection and self-acceptance) in order to foster more positive life transitions for college student-athletes

MimicPlay: Long-Horizon Imitation Learning by Watching Human Play

Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data - video sequences of people freely interacting with the environment using their hands. Even with different morphologies, we hypothesize that human play data contain rich and salient information about physical interactions that can readily facilitate robot policy learning. Motivated by this, we introduce a hierarchical learning framework named MimicPlay that learns latent plans from human play data to guide low-level visuomotor control trained on a small number of teleoperated demonstrations. With systematic evaluations of 14 long-horizon manipulation tasks in the real world, we show that MimicPlay outperforms state-of-the-art imitation learning methods in task success rate, generalization ability, and robustness to disturbances. Code and videos are available at https://mimic-play.github.ioComment: 7th Conference on Robot Learning (CoRL 2023 oral presentation

Oral Administration of Alkylglycerols Differentially Modulates High-Fat Diet-Induced Obesity and Insulin Resistance in Mice

Alkylglycerols (AKGs) from shark liver oil (SLO) were demonstrated to have strong potency to stimulate immune response. However, no study has been conducted on the effects of AKGs on diet-induced obesity and metabolic inflammatory disorder. The purpose of the present study was to investigate the effect of two AKGs isoforms on obesity and insulin resistance in mice fed high-fat (HF) diet. Forty-eight C57BL/6 mice were divided into normal, HF, HF+20 mg/kg selachyl alcohol (SA), HF+200 mg/kg SA, HF+20 mg/kg batyl alcohol (BA), and HF+200 mg/kg BA groups. Body weight, fasting glucose, lipids, insulin and leptin levels, serum IL-1β, and TNF-α levels were compared among different groups. Our results showed that high-dose SA decreased body weight, serum triglyceride, cholesterol, fasting glucose level, insulin level, and serum leptin level of the HF fed mice, while high-dose BA increased fasting insulin level of the HF fed mice. Pretreatment of primary adipocytes with 10 μM SA or BA differentially modulates LPS-mediated MAPK and NF-κB signaling. Our study demonstrated that oral administration of AKGs has differential effects on HF-induced obesity and metabolic inflammatory disorder in mice

The evolution and morphodynamic characteristics of shoals and troughs in Lingdingyang Bay of the Pearl River Estuary

Shoals and troughs are the fundamental geomorphological units of estuarine systems. However, their definition and morphodynamic characteristics, influenced by the complex dynamic environment, remain a critical challenge. This work introduces a depth–area spatial function as a quantitative criterion for the definition of shoals and troughs, while simultaneously elucidating their geodynamic implications. The Lingdingyang Bay (LDB) of the Pearl River Estuary serves as a case study. From 1901 to 2018, the LDB consisted of the West Shoal, Middle Shoal, and East Shoal and the West Trough and East Trough. The threshold depth of the LDB shifted from −5.75 m in 1901 to −4.75 m between 1964 and 2018. The depth–area distribution curve of the LDB exhibits two dominant peak depths (approximately 0 m and −2 m) within the shoal stable state, which categorizes shallow areas into high, medium and low tidal flats. The shoal–trough area ratio in the LDB, relative to the threshold depths, increased from 1901 to 1998, followed by a decline between 2008 and 2018, and culminated in a restoration to the level seen in 1901 (65% shoals and 35% troughs). Regional variations in dominant forces influencing shoal formation and evolution were observed by the vertical classification of the shoal state. The West Shoal is river dominated, the East Shoal is tide dominated, and the Middle Shoal reflects an interaction between riverine inflows and tides. Stabilized curves observed between 2008 and 2018 indicate that this estuary is progressively achieving new equilibrium states. The depth–area spatial function is useful for identifying shoals and troughs within various estuaries, which also provides a geomorphological framework for understanding the estuarine evolution and sediment dynamics

VIMA: General Robot Manipulation with Multimodal Prompts

Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/Comment: ICML 2023 Camera-ready version. Project website: https://vimalabs.github.io