While video communication is becoming quite popular among remote friends and family, recent usage practices have been extending beyond just talking heads to remotely sharing an experience by doing an activity together. However, current video chat tools are aimed at sharing talking heads and need to be reconsidered to support remotely sharing activities. We explore a specific remote shared activity – watching video programs – through a three-phase study. We surveyed people’s interest in watching video...

Near Field Communication (NFC) enables physically proximate devices to communicate over very over short ranges in a peer-to-peer manner, without incurring the overhead of any complex network configuration effort. However, the adoption of NFC-enabled applications has been stymied by the low levels of penetration of NFC hardware. In this paper, we address the challenge of enabling NFClike capability on the existing base of mobile phones. To this end, we develop Dhwani, a novel, acoustics-based NFC system...

We introduce a social media text normalization system that can be deployed as a preprocessing step for Machine Translation and various NLP applications to handle social media text. The proposed system is based on unsupervised learning of the normalization equivalences from unlabeled text. The proposed approach uses Random Walks on a contextual similarity bipartite graph constructed from n-gram sequences on large unlabeled text corpus. We show that the proposed approach has a very high precision of (92.43)...

Recent advances in position localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related content, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data both bridges the gap between the physical and digital worlds and enables a deeper understanding of user preferences and behavior. This addition of vast geospatial datasets has stimulated research into...

Predictive models play a key role for inference and decision making in crowdsourcing. We present methods that can be used to guide the collection of data for enhancing the competency of such predictive models while using the models to provide a base crowdsourcing service. We focus on the challenge of ideally balancing the goals of collecting data over time for learning and for improving task performance with the cost of workers' contributions over the lifetime of the operation of a system. We...

Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing. In this paper, we...

A key decision facing autonomous systems with ac- cess to streams of sensory data is whether to act based on current evidence or to wait for additional information that might enhance the utility of tak- ing an action. Computing the value of informa- tion is particularly difficult with streaming high- dimensional sensory evidence. We describe a belief projection approach to reasoning about information value in these settings, using models for inferring future beliefs over states given streaming...

In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog states. Current approaches based on generative or discriminative models have different but important shortcomings that limit their accuracy. In this paper we discuss these limitations and introduce a new approach for discriminative state tracking that overcomes them by leveraging the problem structure. An offline evaluation with dialog data...

Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suf-fer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individu-als. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical de-pression, based on a standard psychometric instrument. Through their social media postings over a year...

Evaluation of information retrieval (IR) systems has recently been exploring the use of preference judgments over two search result lists. Unlike the traditional method of collecting relevance labels per single result, this method allows to consider the interaction between search results as part of the judging criteria. For example, one result set may be preferred over another if it has a more diverse set of relevant results, covering more diverse user intents. In this paper, we investigate how assessors...

Software engineers often use Q&A forums like Stack Overflow and MSDN to ask and answer technical questions. Through a survey study and web browser log analysis, we find that both askers and answerers of technical forum questions typically conduct extensive online research before composing their posts. The inclusion of links to these research materials is beneficial to the forum participants, though post authors do not always include such citations. Based on these findings, we developed CiteHistory, a...

Query segmentation, like text chunking, is the first step towards query understanding. In this study we explore the effectiveness of crowdsourcing for this task. Through carefully designed control experiments and Inter Annotator Agreement metrics for analysis of experimental data, we show that crowdsourcing may not be a suitable approach for query segmentation because the crowd seems to have a very strong bias towards dividing the query into roughly equal (often only two) parts. Similarly, in the case of...

Symbolic Finite Transducers augment classic transducers with symbolic alphabets represented as parametric theories. Such extension enables succinctness and the use of potentially infinite alphabets while preserving closure and decidability properties. Extended Symbolic Finite Transducers further extend these objects by allowing transitions to read consecutive input elements in a single step. While when the alphabet is finite this extension does not add expressiveness, it does so when the alphabet is...

People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the re-plies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced” answers with crowdsourced answers. By analyzing of thousands of public Twitter questions and an-swers, we build a picture of which questions receive an-swers and the content of their answers....

In statistical relational learning, one is concerned with inferring the most likely explanation (or world) that satisfies a given set of weighted constraints. The weight of a constraint signifies our confidence in the constraint, and the most likely world that explains a set of constraints is simply a satisfying assignment that maximizes the weights of satisfied constraints. The relational learning community has developed specialized solvers (e.g., Alchemy and Tuffy) for such weighted constraints...

We present a new multi-level preconditioning scheme for discrete Poisson equations that arise in various computer graphics applications such as colorization, edge-preserving decomposition for two-dimensional images, and geodesic distances and diffusion on three-dimensional meshes. Our approach interleaves the selection of fine- and coarse-level variables with the removal of weak connections between potential fine-level variables sparsification and the compensation for these changes by strengthening...

Many search engines identify bursts of activity around particular topics and reflect these back to users as Popular Now or Hot Searches. Activity around these topics typically evolves quickly in real-time during the course of a trending event. Users’ informational needs when searching for such topics will vary depending on the stage at which they engage with an event. Through a survey and log study, we observe that interaction with content about trending events varies significantly with prior awareness of...

Consolidation of multiple workloads, encapsulated in virtual machines (VMs), can significantly improve efficiency in cloud infrastructures. But consolidation also introduces contention in shared resources such as the memory hierarchy, leading to degraded VM performance. To avoid such degradation, the current practice is to not pack VMs tightly and leave a large fraction of server resource unused. This is wasteful. We present a system that consolidates VMs such that performance degradation is within a...