In this poster, we introduce a set of modules designed to enhance security education in introductory programming courses. The modules cover a range of prevalent security concerns, including topics such as integer overflow, buffer overflow, and user input validation. These critical security problems are addressed to equip students with essential knowledge and skills in secure coding. The modules feature code examples within sandbox environments, as well as presented using the Python Tutor code visualizer. The eight modules align seamlessly with chapters found in popular introductory programming courses. Each example has been implemented in C++, Java, and Python, ensuring broad applicability across different programming languages.

In this poster, we introduce LEARNDB (Learning Environment and Resource Network for Databases), a platform designed to meet the unique demands of database education. The platform offers a comprehensive set of tools designed to facilitate the creation and management of tutorials, exercises, quizzes, and laboratory assignments, encompassing topics spanning from database design to SQL (Structured Query Language) proficiency. We have also developed content that encompasses topics spanning from database design to SQL (Structured Query Language) proficiency, providing students with practical skills and knowledge.

Traditional paper-based exams and LMS-provided online exams for introductory programming courses are not aligned with learning objectives that emphasize problem-solving and coding skills. In this poster, we present a cloud-based assessment solution for introductory programming courses. First, we discuss the requirements and challenges of conducting frequent assessments. We then outline the functions in our online exam toolkit that allow instructors to administer versatile assessments. Instead of relying on a traditional lockdown browser, the plagiarism and cheating detection in our toolkit allows instructors to administer exams in any modern browser for face-to-face classes.

In this poster, we present iCAP (In Class Activity Participation), an Audience Response System (ARS) for student engagement during introductory programming lectures. iCAP is a web-based solution, allowing student access from any laptop, tablet, or phone with access to a modern browser. Therefore, iCAP increases the flexibility and convenience of educator-student interaction. This poster introduces iCAP's functionalities and compares student mastery of learning outcomes among students taught with and without question-based learning methodology.

Localization of Drosophila embryos in images is a fundamental step in an automatic computational system for the exploration of gene-gene interaction on Drosophila. In this paper, we introduce a localization framework based on the analysis of connected components in the Gaussian scale space of an embryonic image. We propose three criteria for the selection of the optimal scale. The experiment results show the promise of the proposed methods.

In this paper, we propose a binocular framework for face liveness verification under unconstrained localization. The proposed framework contains two components: the first component localizes imbalanced points in face regions of an input pair of stereo images and the second component detects whether an imaging face is a 2D object or a 3D object. We test the propose framework on a publicly available stereo face database, which demonstrated its potential.

Embryonic images of Drosophila melanogaster were introduced as an important tool for the discovery of gene-gene interaction, which can be characterized as a computational problem of matching expression patterns of embryos. As the first step of the computational system, image standardization aims to build pixel-to-pixel correspondence so that the comparison between embryonic images are biologically meaningful. Standardization of embryonic images is challenging due to substantial variations of imaging conditions and the appearances of embryos. In this paper, we propose a framework for automatic standardization of embryonic images. Experiment results show the significant improvement achieved by the proposed approach.

It has been shown that the cost parameters and kernel parameters are critical in the performance of support vector machines (SVMs). A standard parameter selection method compares parameters among a discrete set of values, called the candidate set, and picks the one which has the best classification accuracy. As a result, the choice of parameters strongly depends on the pre-defined candidate set. In this paper, we formulate the selection of the cost parameter and kernel parameter as a two-level optimization problem, in which the values of parameters vary continuously and thus optimization techniques can be applied to select ideal parameters. Due to the non-smoothness of the objective function in our model, a genetic algorithm has been presented. Numerical results show that the two-level approach can significantly improve the performance of SVM classifier in terms of classification accuracy.

We describe XIS, an XML document integration system. The system is based on an algorithm that computes the top-down edit distance between an XML document and a schema. The complexity of the algorithm is t x s x log s, where t is the size of the document and s is the size of the schema.The system includes a GUI that allows the user to visualize the operations performed on the XML document. Synthesized and real data-sets will be used to show the efficiency and efficacy of the system.

Using a native XML DBMS to efficiently store and retrieve XML data has been well studied. Various native XML DBMSs were developed, and they support most of the features of XQuery. The core topics of these studies are indexing methods for XML. There are two major issues about the existing XML DBMSs: (1) most existing native XML DBMSs use range based indexing methods, resulting in significant extra space; (2) related information in an XML document are isolated in storage. In this paper, we present a method that maps XML data into relational tables. Our method is still range based, which means it can process queries with regular path expression efficiently. But with the introduction of node grouping, the extra space needed for the indexing is reduced significantly. With fewer tables created, we show that query processing can also be significantly expedited.