One of the ways to attract a more diverse group of students to computer science is by offering courses that are interdisciplinary. Some of the first examples of this combined multimedia with programming. There are many more possibilities, but as computer scientists, we often do not have the domain knowledge to teach these courses alone. Team teaching offers a solution to this dilemma. The goal of this panel is to present some of the problems we have encountered and to discuss some of the solutions.

Online communities and social networks are the most popular sites on the Internet, and have exploded with multimedia content in the last decade. Most web designers recognize that site images can be saved with lower fidelity to reduce bandwidth consumption and increase capacity, though many are reluctant to do so for aesthetic concerns. However, there are many images that site designers have little direct control over---socially sourced images. Many social networks automatically reduce the fidelity of uploaded images in order to conserve bandwidth. Social networks also contain a vast archive of images with popularity indicators, such as likes and shares, which recent work has correlated with psychovisual features within the images. In this paper, we investigate the trade-off between fidelity reduction and selected psychovisual enhancements. We demonstrate that even simple enhancements can be used to enable more aggressive optimization of socially sourced content, which has implications for static content delivery networks and image servers. Through user testing on real images, we validate the efficacy of our proposed approach.

Web bots such as crawlers are widely used to automate various online tasks over the Internet. In addition to the conventional approach of human interactive proofs such as CAPTCHAs, a more recent approach of human observational proofs (HOP) has been developed to automatically distinguish web bots from human users. Its design rationale is that web bots behave intrinsically differently from human beings, allowing them to be detected. This paper escalates the battle against web bots by exploring the limits of current HOP-based bot detection systems. We develop an evasive web bot system based on human behavioral patterns. Then we prototype a general web bot framework and a set of flexible de-classifier plugins, primarily based on application-level event evasion. We further abstract and define a set of benchmarks for measuring our system's evasion performance on contemporary web applications, including social network sites. Our results show that the proposed evasive system can effectively mimic human behaviors and evade detectors by achieving high similarities between human users and evasive bots.

Search engines are an essential component of the web, but their web crawling agents can impose a significant burden on heavily loaded web servers. Unfortunately, blocking or deferring web crawler requests is not a viable solution due to economic consequences. We conduct a quantitative measurement study on the impact and cost of web crawling agents, seeking optimization points for this class of request. Based on our measurements, we present a practical caching approach for mitigating search engine overload, and implement the two-level cache scheme on a very busy web server. Our experimental results show that the proposed caching framework can effectively reduce the impact of search engine overload on service quality.