The goal of the session is to help attendees who are committed to diversity and inclusion learn to talk about different dimensions of identity (e.g., race, class, gender, sex, sexuality, etc.). The landscape of terms is always changing and we want SIGCSE attendees to feel more comfortable using current language to talk about issues related to diversity and inclusion. This special session will include six lightning talks, individual reflection, small-group discussion, and a Q&A with presenters. This is a reprise of a special session held at the NCWIT Summit in 2018; it was well received and we hope to offer it to the larger SIGCSE audience.

The purpose of this project is to provide instructions for teaching the Artificial Intelligence topic of supervised machine learning for the task of Optical Character Recognition (OCR) at various levels of a student’s undergraduate curriculum, such as basic knowledge, novice, and intermediate. The levels vary from beginner with a slight background in computing and computer science to intermediate with a better understanding of computer science fundamentals and algorithms.

The task of object identification occurs when integrating information from multiple websites. The same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. Previous methods of object identification have required manual construction of domain-specific string transformations or manual setting of general transformation parameter weights for recognizing format inconsistencies. This manual process can be time consuming and error-prone. We have developed an object identification system called Active Atlas [18], which applies a set of domain-independent string transformations to compare the objects' shared attributes in order to identify matching objects. In this paper, we discuss extensions to the Active Atlas system, which allow it to learn to tailor the weights of a set of general transformations to a specific application domain through limited user input. The experimental results demonstrate that this approach achieves higher accuracy and requires less user involvement than previous methods across various application domains.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform real-time visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond individual's capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system. To build such integrated robots, we should use different approaches from those employed in separate research disciplines. This paper describes our experience (problems and solutions) in this aspect for building soccer robots. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In RoboCup97, our Dreamteam robots performed well (scored 8 of 9 goals of all teams in the league) and won the world championship in the middle-sized robot league.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.