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.

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.

Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an outdoor image is consistent with its content and geographic location. We consider manipulations done in the hour and/or month of capture of a photograph. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.0% to 81.1%. We perform an ablation study that highlights the importance of various components of the method, showing what types of tampering are detectable using our approach. Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata.

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification.

Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3D point clouds. In this paper, our goal is to enhance the adversarial robustness of PointNet, which is one of the most widely used models for 3D point clouds. We apply the fast gradient sign attack method (FGSM) on 3D point clouds and find that FGSM can be used to generate not only adversarial images but also adversarial point clouds. To minimize the vulnerability of PointNet to adversarial attacks, we propose Defense-PointNet. We compare our model with two baseline approaches and show that Defense-PointNet significantly improves the robustness of the network against adversarial samples.

Looking at the world from above, it is possible to estimate many properties of a given location, including the type of land cover and the expected land use. Historically, such tasks have relied on relatively coarse-grained categories due to the difficulty of obtaining fine-grained annotations. In this work, we propose an easily extensible approach that makes it possible to estimate fine-grained properties from overhead imagery. In particular, we propose a cross-modal distillation strategy to learn to predict the distribution of fine-grained properties from overhead imagery, without requiring any manual annotation of overhead imagery. We show that our learned models can be used directly for applications in mapping and image localization.

We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road meta-data. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along with aerial imagery to directly estimate free-flow speed with a deep convolutional neural network (CNN). We evaluate our approach on a large dataset, and demonstrate that using imagery alone performs nearly as well as the road features and that the combination of imagery with road features leads to the highest accuracy.

We explore the problem of mapping soundscapes, that is, predicting the types of sounds that are likely to be heard at a given geographic location. Using a novel dataset, which includes geo-tagged audio and overhead imagery, we develop an approach for constructing an aural atlas, which captures the geospatial distribution of soundscapes. We build on previous work relating sound to ground-level imagery but incorporate overhead imagery to overcome the limitations of sparsely distributed geo-tagged audio. In the end, all that we require to construct an aural atlas is overhead imagery of the region of interest. We show examples of aural atlases at multiple spatial scales, from block-level to country.

Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken. There are a wide variety of cues that can be used to address this problem. Most previous work has focused on low-level image features, such as color and vignetting. Recent work on image dating has used more semantic cues, such as the appearance of automobiles and buildings. We extend this line of research by focusing on human appearance. Our approach, based on a deep convolutional neural network, allows us to more deeply explore the relationship between human appearance and time. We find that clothing, hair styles, and glasses can all be informative features. To support our analysis, we have collected a new dataset containing images of people from many high school yearbooks, covering the years 1912-2014. While not a complete solution to the problem of image dating, our results show that human appearance is strongly related to time and that semantic information can be a useful cue.