Recent research findings point to a declining institutional interest in the necessity of online education. This study, therefore, proposes to examine a number of key pedagogical questions pertaining to the student images and perception about the relevance and usefulness of the e-environments to their study habits; their experiences and expectations; and the effects that the e-environment has on the decision to enroll in online classes. This paper further aims to add to the literature by providing useful information that can aid educators and administrators for enhancing the image and tendencies for a set of desired e-environments in higher education. A random sample of 473 students at a Midwestern liberal arts public university in the United States constituted the working sample in this study. Two additive scales-Structural Motives Scale and Experiential Factors Scale-measure the imagery and the reasons for the student preference for a learning environment-i.e., online, ground, or hybrid courses.Descriptive, bivariate, and regression analysis supported the model that explained the reasons for the popularity of online learning environment.
Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stackbased buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to avoid path explosion. In our method, test units are considered parts of the program's code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints of each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.