The tenth annual Microsoft eScience Workshop will be held in Beijing in conjunction with the IEEE International Conference on eScience. It will continue the ongoing dialogue centered on applications in broad areas of scientific investigation, such as environmental studies, bioinformatics, and climate understanding; present new results in data modeling; and provide an opportunity for open discourse on developments in urban computing.

In this second workshop on Wearable Systems for Industrial Augmented Reality Applications we will discuss the following topics, related to wearable computing and AR technologies: core technologies, such as hardware, AR development kits or AR-enabled software; software architectures and applications concepts; as well as business ideas and case studies of AR systems within the industrial context.

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...

Static Analysis Symposium

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...

Secure multi-party computation (MPC) allows a set of parties to compute a function of their inputs while preserving input privacy and correctness. MPC has been an active area of research of cryptography for over 30 years. The last decade has witnessed significant interest and advances in the applied aspects of MPC. This workshop will bring together researchers in security and cryptography to discuss recent advances, challenges and research directions related to applied secure computation.

Programming contest associated with the International Conference on Functional Programming (ICFP)

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)...

This workshop aims to bring together researchers and practitioners from industry and academia to discuss health search and discovery. There is a particular focus on ways to make medical information more readily accessible to laypeople (including enhancements to ranking algorithms and search interfaces), and, additionally, how we can discover new medical facts and phenomena from information sought by people, as evidenced in their logged query streams as well as other sources such as social media.

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...

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...

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...

Organized by Microsoft Research in cooperation with Yandex and Lomonosov Moscow State University, the Microsoft School on Algorithms for Massive Data (ALMADA) offers students a unique opportunity to learn about fundamental and state-of-the-art results on algorithms and systems for processing massive datasets. The school offers four courses on technical topics, and additional lectures will provide context for real-life applications and business opportunities.

The fourteenth annual Microsoft Research Faculty Summit brings leading academic researchers and educators together with Microsoft researchers and engineers, to share ideas and results around some of today’s most exciting new directions. Key subjects to be discussed this year include machine and human intelligence, software engineering, computer vision, and quantum computing.

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...

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...

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...

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...

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....