Collaborative games, played across multiple co-located mobile devices are gaining popularity because of their location flexibility, low hardware requirements, players’ close proximity, and their promise of generating excitement and collaborative social experience. Even though the integration of spatial device rearrangement (i.e., physically picking up a device and positioning it in another location) in cross-device applications in other domains has demonstrated higher potential for collaboration, this form of interaction remains relatively unexplored in ad hoc cross-device gaming applications. That is because these interactions typically require additional hardware support, which can hinder the ad hoc nature of the cross-device social games. In this paper, we present the iterative user-centred design process of creating an ad hoc co-located cross-device maze exploration game named Snap-To-Eat that supports both on-screen cross-device interactions (e.g., moving elements across devices) and spatial device rearrangement interactions without requiring any additional hardware. We refined the game design by conducting two rounds of workshops, the first with a low-fidelity paper prototype and the second with a high-fidelity digital prototype, with 36 undergraduate and graduate students in total. Findings from both workshops demonstrated that integrating spatial device rearrangement interactions in an ad hoc co-located cross-device game created opportunities for collaboration and engagement among the players, which elevated their overall social experience. These findings also suggested new design opportunities and future research directions for ad hoc collocated cross-device games, e.g., introducing new interactions, as well as exploring the game mechanics in a 3D space to elevate the gaming experience.

Computer-aided simulation-based platforms have been shown to be effective tools for teaching STEM concepts. At the same time, Computer Supported Collaborative Learning (CSCL) platforms encourage different viewpoints and approaches from the learners which can enrich the learning experience in STEM classrooms. The deployment in recent years of networked personal devices such as Chromebooks in classrooms has motivated educators to design collaborative learning tools for these devices. However, prior work has shown that using one-on-one devices may discourage students from talking among each other, which hinders collaboration. To understand the affordances of personal devices for CSCL tools within Biology curricula, we designed a collaborative plant growth simulation application that provides mirrored plant growth simulation views for every group member to facilitate a common visualization. In this paper, we present our findings from an in-the-wild study that evaluated the affordance and usability of the plant growth simulation application and investigated the nature of collaboration and engagement aided through the simulation mirroring feature. Our study results showed that the plant simulation application had high usability and acceptance. Moreover, mirroring the plant growth simulation improved collaboration, generated excitement, and stimulated conversation. We also identified episodes where collaboration was hindered due to off-task activities, troubleshooting, group dynamics, and lack of understanding that led us to outline some potential guidelines to improve the collaborative learning experience for the students in Biology classroom.

Creating effective middle school STEM curricula requires a combination of individual and collaborative learning. Prior studies showed that finding a proper balance and providing uninterrupted knowledge transmission between different learning modes can be challenging in such mixed pedagogical approaches. In this paper, we present a multi-device interactive educational platform named SimSnap to teach biology curriculum to middle school children. SimSnap facilitates interactions among touchscreen Chromebooks to perform in-class individual and group activities. We present a usability analysis study with eight middle school children where they learn about the influence of temperature on tomato plant growth. Our study demonstrated that SimSnap facilitates group discussions to complete collaborative tasks. It also creates seamless knowledge propagation between prior to current tasks to learn about more complex concepts from previous simpler activities. Middle school children gave overall high usability ratings and positive feedback on SimSnap. This study also helped to outline some design recommendations for future improvements of SimSnap.

We study the problem of continuous object dissemination---given a large number of users and continuously arriving new objects, deliver an object to all users who prefer the object. Many real world applications analyze users' preferences for effective object dissemination. For continuously arriving objects, timely finding users who prefer a new object is challenging. In this paper, we consider an append-only table of objects with multiple attributes and users' preferences on individual attributes are modeled as strict partial orders. An object is preferred by a user if it belongs to the Pareto frontier with respect to the user's partial orders. Users' preferences can be similar. Exploiting shared computation across similar preferences of different users, we design algorithms to find target users of a new object. In order to find users of similar preferences, we study the novel problem of clustering users' preferences that are represented as partial orders. We also present an approximate solution of the problem of finding target users which is more efficient than the exact one while ensuring sufficient accuracy. Furthermore, we extend the algorithms to operate under the semantics of sliding window. We present the results from comprehensive experiments for evaluating the efficiency and effectiveness of the proposed techniques.

We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy-e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a “contextual” skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas-tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques.

Towards computational journalism, we present FactWatcher, a system that helps journalists identify data-backed, attention-seizing facts which serve as leads to news stories. FactWatcher discovers three types of facts, including situational facts, one-of-the-few facts, and prominent streaks, through a unified suite of data model, algorithm framework, and fact ranking measure. Given an append-only database, upon the arrival of a new tuple, FactWatcher monitors if the tuple triggers any new facts. Its algorithms efficiently search for facts without exhaustively testing all possible ones. Furthermore, FactWatcher provides multiple features in striving for an end-to-end system, including fact ranking, fact-to-statement translation and keyword-based fact search.

Given the sheer amount of work and expertise required in authoring Wikipedia articles, automatic tools that help Wikipedia contributors in generating and improving content are valuable. This paper presents our initial step towards building a full-fledged author assistant, particularly for suggesting infobox templates for articles. We build SVM classifiers to suggest infobox template types, among a large number of possible types, to Wikipedia articles without infoboxes. Different from prior works on Wikipedia article classification which deal with only a few label classes for named entity recognition, the much larger 337-class setup in our study is geared towards realistic deployment of infobox suggestion tool. We also emphasize testing on articles without infoboxes, due to that labeled and unlabeled data exhibit different distributions of features, which departs from the typical assumption that they are drawn from the same underlying population.

Host-based intrusion detection techniques are needed to ensure the safety and security of software systems, especially, if these systems handle sensitive data. Most host-based intrusion detection systems involve building some sort of reference models offline, usually from execution traces (in the absence of the source code), to characterize the system healthy behavior. The models can later be used as a baseline for online detection of abnormal behavior. Perhaps the most popular techniques are the ones based on the use of Hidden Markov Models (HMM). These techniques, however, require long training time of the models, which makes them computationally infeasible, the main reason being the large size of typical traces. In this paper, we propose an improved HMM using the concept of frequent common patterns. In other words, we build models based on extracting the largest n-grams (patterns) in the traces instead of taking each trace event on its own. We show through a case study that our approach can reduce the training time by 31.96%-48.44% compared to the original HMM algorithms while keeping almost the same accuracy rate.