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.

We propose a general database-driven framework for coherent synthesis of patient-specific scans of unavailable modality, which adopts and generalizes the patch-based label propagation (LP) strategy. While modality synthesis has received increased attention lately, current methods are mainly tailored to specific applications. On the other hand, the LP framework has been extremely successful for certain segmentation tasks, however, so far it has not been used for estimation of entities other than...

We propose a method for multi-atlas label propagation based on encoding the individual atlases (i.e. image/label-map pairs) by scan-specific classification forests. Most current approaches perform a non-linear registration between all atlases and the target image to be labeled, followed by a sophisticated fusion scheme. While these approaches can achieve high accuracy, in general they do so at high computational cost. This negatively affects the scalability to large databases and experimentation. To...

Accurate localization and identification of vertebrae in spinal imaging is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. The main difficulties for automatic methods arise from the frequent presence of abnormal spine curvature, small field of view, and image artifacts caused by surgical implants. Many previous methods rely on parametric models of appearance and shape whose performance can substantially degrade for pathological cases. We propose a robust...

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...

A challenge in large vocabulary spoken language understanding (SLU) is robustness to automatic speech recognition (ASR) errors. The state of the art approaches for semantic parsing rely on using discriminative sequence classification methods, such as conditional random fields (CRFs). Most dialog systems employ a cascaded approach where the best hypotheses from the ASR system are fed into the following SLU system. In our previous work, we have proposed the use of lattices towards joint recognition and...

Static Analysis Symposium

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.

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...

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)...

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...

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...

While data-driven methods for spoken language understanding (SLU) provide state of the art performances and reduce maintenance and model adaptation costs compared to handcrafted parsers, the collection and annotation of domain-specific natural language utterances for training remains a time-consuming task. A recent line of research has focused on enriching the training data with in-domain utterances by mining search engine query logs to improve the SLU tasks. However genre mismatch is a big obstacle as...

With the explosive growth of social networks, many applications are increasingly harnessing the pulse of online crowds for a variety of tasks such as marketing, advertising, and opinion mining. An important example is the wisdom of crowd effect that has been well studied for such tasks when the crowd is non-interacting. However, these studies don't explicitly address the network effects in social networks. A key difference in this setting is the presence of social influences that arise from these...

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...

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.

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...

The past decade has seen the emergence of web-scale structured and linked semantic knowledge resources (e.g., Freebase, DBPedia). These semantic knowledge graphs provide a scalable “schema for the web”, representing a significant opportunity for the spoken language understanding (SLU) research community. This paper leverages these resources to bootstrap a web-scale semantic parser with no requirement for semantic schema design, no data collection, and no manual annotations. Our approach is based on...

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...