Papers.

Semantics of query rewriting patterns in search logs

IDEAL

Targeted and scalable information dissemination in a distributed reputation mechanism

Big data benchmarking

Towards energy-proportional datacenter memory with mobile DRAM

A statistical similarity measure for aggregate crowd dynamics

We present an information-theoretic method to measure the similarity between a given set of observed, real-world data and visual simulation technique for aggregate crowd motions of a complex system consisting of many individual agents. This metric uses a two-step process to quantify a simulator's ability to reproduce the collective behaviors of the whole system, as observed in the recorded real-world data. First, Bayesian inference is used to estimate the simulation states which best correspond to the observed data, then a maximum likelihood estimator is used to approximate the prediction errors. This process is iterated using the EM-algorithm to produce a robust, statistical estimate of the magnitude of the prediction error as measured by its entropy (smaller is better). This metric serves as a simulator-to-data similarity measurement. We evaluated the metric in terms of robustness to sensor noise, consistency across different datasets and simulation methods, and correlation to perceptual metrics.

(Big) usage data in web search

Social annotations: utility and prediction modeling

Finding trendsetters in information networks

Random forests for metric learning with implicit pairwise position dependence

Metric learning makes it plausible to learn semantically meaningful distances for complex distributions of data using label or pairwise constraint information. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the feature space have demonstrated superior accuracy, but at a severe cost to computational efficiency. Here, we adopt a new angle on the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of method in terms of accuracy (consistently ranked first) and is an order of magnitude faster than state of the art multi-metric methods (16x faster in the worst case).

Exploring reflection in online communities

Linked open corpus models, leveraging the semantic web for adaptive hypermedia

Evaluation of a domain-aware approach to user model interoperability

A trust-and-risk aware RBAC framework: tackling insider threat

CAM: A Topology Aware Minimum Cost Flow Based Resource Manager for MapReduce Applications in the Cloud

GPU accelerated AES-CBC for database applications.

Entity matching for semistructured data in the Cloud

Finding related tables

Shark: fast data analysis using coarse-grained distributed memory

Google's hybrid approach to research

By closely connecting research and development Google is able to conduct experiments on an unprecedented scale, often resulting in new capabilities for the company.

Layout guidelines for web text and a web service to improve accessibility for dyslexics

Open source column

The effect of links on networked user engagement

Lexical quality as a proxy for web text understandability

Leveraging trust and distrust for sybil-tolerant voting in online social media

On measuring the lexical quality of the web

Identification of top relevant temporal expressions in documents

Your mouse is a database

Web and mobile applications are increasingly composed of asynchronous and real-time streaming services and push notifications.

Idempotence Is Not a Medical Condition

Explains why at-least-once message delivery plus idempotent processing is the practical foundation of reliable distributed applications.

Short message communications

Concurrent tries with efficient non-blocking snapshots

We describe a non-blocking concurrent hash trie based on shared-memory single-word compare-and-swap instructions. The hash trie supports standard mutable lock-free operations such as insertion, removal, lookup and their conditional variants. To ensure space-efficiency, removal operations compress the trie when necessary.

Scala-virtualized

Scala-Virtualized extends the Scala language to better support hosting embedded DSLs. Embedding a DSL in Scala-Virtualized comes with all the benefits of a shallow embedding thanks to Scala's flexible syntax, without giving up analyzing and manipulating the domain program -- typically exclusive to deep embeddings. Through lightweight modular staging, implemented in standard Scala, the benefits of a deep embedding are recovered with little overhead. Scala-Virtualized lifts more of the language's built-in constructs and static information to complete this support and make it more convenient. We illustrate how Scala-Virtualized makes Scala an even better host for embedded DSLs along three axes of customizing the language: syntax, run-time behavior and static semantics.

StagedSAC: a case study in performance-oriented DSL development

Top-10 Data Mining Case Studies

Roles of Performance and Human Capital in College Football Coaches' Compensation.

Despite the escalation of football coaches’ salaries at National Collegiate Athletic Association (NCAA) Football Bowl Subdivision (FBS) institutions, little empirical investigation has been undertaken to identify the determinants of their compensation. As such, the purpose of this study is to explain how the level of coaching compensation is determined based on three theoretical perspectives in managerial compensation: marginal productivity theory, human capital theory, and managerialism. The analysis of compensation data of head football coaches at FBS institutions in 2006–2007 shows that the maximum total compensation of these coaches increases with their past performance. The results further reveal that coaches with greater human capital tend to receive a compensation package where bonuses account for a smaller proportion of the maximum total compensation. Overall, these findings mostly confirm the predictions drawn from managerial productivity theory, human capital theory and managerialism.

The use of novel, direct diode lasers for large area hard-facing and high deposition rate cladding to enhance surface wear and corrosion resistance

Colorless green ideas learn furiously: Chomsky and the two cultures of statistical learning

Mapping XML to a Wide Sparse Table

Scalable Learning of Collective Behavior

Identifying Evolving Groups in Dynamic Multimode Networks

User Taglines: Alternative Presentations of Expertise and Interest in Social Media

Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy-why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for `whom to follow'. Google+ and Facebook also recommend users to follow or add to friend circle. Popular Internet newspaper-The Huffing ton Post shows Twitter experts on the side of an article for authoritative relevant tweets. The state of the art shows user profile bio as summary for Twitter experts, but it has issues with length constraints imposed by the user interface (UI) design, missing bio and sometimes funny profile bio. Alternatively, applications can use human generated user summary, but it will not scale. Therefore, we study the problem of automatic generation of informative expertise summary or taglines for Twitter experts in space constraint imposed by UI design. We propose three methods for expertise summary generation: Occupation-Pattern based, Link-Triangulation based and User-Classification based, with the use of knowledge-enhanced computing approaches. We also propose methods for final summary selection for users with multiple candidates of generated summaries and evaluate results by user-study for both generation and selection tasks. The results of proposed tagline generation methods show 92.8% good summaries with majority agreement in the best case and 70% in the worst case while outperforming the state of the art up to 88%. This study has implications in the area of expert profiling, user presentation and application design for engaging user experience.

Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Network Data

Performance analysis of the Libswift P2P streaming protocol

NeuFlow: Dataflow vision processing system-on-a-chip

Sensing the "Health State" of a Community

Play2: A New Era of Web Application Development

A Visual Tool for Querying and Exploring XML Data

GraphGen: A Tool for Automatic Generation of Multipartite Graphs from Arbitrary Data

Exploiting Distributional Semantic Models in Question Answering

This paper investigates the role of Distributional Semantic Models (DSMs) in Question Answering (QA), and specifically in a QA system called Question Cube. Question Cube is a framework for QA that combines several techniques to retrieve passages containing the exact answers for natural language questions. It exploits Information Retrieval models to seek candidate answers and Natural Language Processing algorithms for the analysis of questions and candidate answers both in English and Italian. The data source for the answer is an unstructured text document collection stored in search indices. In this paper we propose to exploit DSMs in the Question Cube framework. In DSMs words are represented as mathematical points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to compute relatedness between users' questions and candidate answers by exploiting paradigmatic relations between words. Results of an experimental evaluation carried out on CLEF2010 QA dataset, prove the effectiveness of the proposed approach.

Estimating probability of collision for safe motion planning under Gaussian motion and sensing uncertainty

We present a fast, analytical method for estimating the probability of collision of a motion plan for a mobile robot operating under the assumptions of Gaussian motion and sensing uncertainty. Estimating the probability of collision is an integral step in many algorithms for motion planning under uncertainty and is crucial for characterizing the safety of motion plans. Our method is computationally fast, enabling its use in online motion planning, and provides conservative estimates to promote safety. To improve accuracy, we use a novel method to truncate estimated a priori state distributions to account for the fact that the probability of collision at each stage along a plan is conditioned on the previous stages being collision free. Our method can be directly applied within a variety of existing motion planners to improve their performance and the quality of computed plans. We apply our method to a car-like mobile robot with second order dynamics and to a steerable medical needle in 3D and demonstrate that our method for estimating the probability of collision is orders of magnitude faster than naïve Monte Carlo sampling methods and reduces estimation error by more than 25% compared to prior methods.