Computer games are an extremely popular but overlooked workload. Cloud-gaming has been one of the biggest buzzwords in the gaming industry throughout 2020. The rapid growth of the video gaming industry and the diverse set of popular video games available today raises increasing concern to properly understand its I/O characteristics to improve their performance and design better gaming servers and consoles. To the best of our knowledge, this is the first attempt to systematically measure, quantify, and characterize the organization of game data into files, back-end storage access patterns, and the performance of gaming workloads. We explore the I/O behavior of 14 recent and famous games, producing a series of observations coming from measurements done on a real setup.

Mashups are an increasingly popular way to integrate data from multiple web sites to fit a particular need, but it often requires substantial technical expertise to create them. To lower the barrier for creating mashups, we have extended the CoScripter web automation tool with a spreadsheet-like environment called Vegemite. Our system uses direct-manipulation and programming-by-demonstration tech-niques to automatically populate tables with information collected from various web sites. A particular strength of our approach is its ability to augment a data set with new values computed by a web site, such as determining the driving distance from a particular location to each of the addresses in a data set. An informal user study suggests that Vegemite may enable a wider class of users to address their information needs.

There is a tremendous amount of web content available today, but it is not always in a form that supports end-users' needs. In many cases, all of the data and services needed to accomplish a goal already exist, but are not in a form amenable to an end-user. To address this problem, we have developed an end-user programming tool called Marmite, which lets end-users create so-called mashups that re-purpose and combine existing web content and services. In this paper, we present the design, implementation, and evaluation of Marmite. An informal user study found that programmers and some spreadsheet users had little difficulty using the system.

Expertise to assist people on complex tasks is often in short supply. One solution to this problem is to design systems that allow remote experts to help multiple people in simultaneously. As a first step towards building such a system, we studied experts' attention and communication as they assisted two novices at the same time in a co-located setting. We compared simultaneous instruction when the novices are being instructed to do the same task or different tasks. Using machine learning, we attempted to identify speech markers of upcoming attention shifts that could serve as input to a remote assistance system.

Many information tasks on the web require users to make use of multiple websites, for both content as well as information processing or visualization features. Programmers have created "mashups," which customize or combine the functionality of multiple websites by extracting information from web pages or accessing web services APIs. Non-programmers lack the tools or skills create such customizations because of the programming obstacles involved. Marmite is a tool that empowers non-programmers to create functionality similar to those found in mashups.

The increasing interest in supporting multiparty remote collaboration has created both opportunities and challenges for the research community. The research reported here aims to develop tools to support multiparty remote collaborations and to study human behaviors using these tools. In this paper we first introduce an experimental multimedia (video and audio) system with which an expert can collaborate with several novices. We then use this system to study helpers' focus of attention (FOA) during a collaborative circuit assembly task. We investigate the relationship between FOA and language as well as activities using multimodal (audio and video) data, and use learning methods to predict helpers' FOA. We process different modalities separately and fusion the results to make a final decision. We employ a sliding window-based delayed labeling method to automatically predict changes in FOA in real time using only the dialogue among the helper and workers. We apply an adaptive background subtraction method and support vector machine to recognize the worker's activities from the video. To predict the helper's FOA, we make decisions using the information of joint project boundaries and workers' recent activities. The overall prediction accuracies are 79.52% using audio only and 81.79% using audio and video combined.