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An improved P300-based brain-computer interface
TLDR
The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate. Expand
Estimating the query difficulty for information retrieval
TLDR
This tutorial is to expose participants to the current research on query performance prediction (also known as query difficulty estimation), and participants will become familiar with states-of-the-art performance prediction methods, and with common evaluation methodologies for prediction quality. Expand
Microsoft Malware Classification Challenge
TLDR
A high-level comparison of the publications citing the Microsoft Malware Classification Challenge dataset simplifies finding potential research directions in this field and future performance evaluation of the dataset. Expand
Milepost GCC: Machine Learning Enabled Self-tuning Compiler
TLDR
Milepost GCC is described, the first publicly-available open-source machine learning-based compiler that automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Expand
Models of user engagement
TLDR
This paper provides initial insights into engagement patterns, allowing for a better understanding of the important characteristics of how users repeatedly interact with a service or group of services. Expand
Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval
TLDR
Novel learning methods for estimating the quality of results returned by a search engine in response to a query and the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval are presented. Expand
MILEPOST GCC: machine learning based research compiler
TLDR
MILEPOST 1 GCC is described, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Expand
What makes a query difficult?
TLDR
This work addresses a novel model that captures the main components of a topic and the relationship between those components and topic difficulty and demonstrates the applicability of the difficulty model for several uses such as predicting query difficulty, predicting the number of topic aspects expected to be covered by the search results, and analyzing the findability of a specific domain. Expand
Measuring User Engagement
TLDR
This book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve the understanding of this construct, which has become so vital in the authors' wired world. Expand
Predicting Customer Churn in Mobile Networks through Analysis of Social Groups
TLDR
This work proposes a novel framework, termed Group-First Churn Prediction, which eliminates the a priori requirement of knowing who recently churned and exploits the structure of customer interactions to predict which groups of subscribers are most prone to churn, before even a single member in the group has churned. Expand
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