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ATL Cairo is requesting graduation projects proposal submissions in the area of “Computer Vision”, “Natural Language Processing & Social Analytics” and “Speech”. While encouraging submissions in the general area of all mentioned tracks, we encourage submitting proposals addressing the following project ideas:
Project details
Labs: ATL Cairo
Interspeech 2014 Tutorial Web Page
Project details
Labs: Redmond
an overhead-constraint logging system
Project details
Labs: Asia
We are looking for participants to engage in a personalised online shopping experience. You will receive a £40 shopping voucher for your participation and get the opportunity to purchase a book at 90% discount. The experiment involves a session of online shopping during which we will measure your eye movements and bodily responses. The shopping session is followed by an interview and we will ask you to fill out a final questionnaire to give us feedback on the study.
Project details
Labs: Cambridge
CityNoise is a project led by Dr. Yu Zheng in Microsoft Research. The project aims to diagnose a city's noise pollution with crowdsensing and ubiquitous data. It reveals the fine-grained noise situation throughout a city and analyzes the composition of noises in a particular location, by using 311 complaint data together with road network data, points of interests, and social media.
Project details
Labs: Asia | New York
Animated computer graphics are projected onto the base of a fiber optic tree to create a sparse 3D display within the tree. This was done as an entry into Microsoft Research's MakeFest and demonstrated on 1/10/2014 to the MSRMakeFest community.
Project details
Labs: Redmond
OSLO is a .NET and Silverlight class library for the numerical solution of ordinary differential equations (ODEs). The library enables numerical integration to be performed in C#, F# and Silverlight applications. OSLO implements Runge-Kutta and back differentiation formulae (BDF) for non-stiff and stiff initial value problems.
Project details
Labs: Cambridge
We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of human computer interaction scenarios.
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Project focussed on quantifying the economic and environmental, trade-offs and synergies that arise under land use change.
Project details
Labs: Cambridge
Climatology gives you climate information for anywhere on Earth: temperature, rain and sunniness. Whether finding where are the warm, dry places to go on holiday in December, or avoiding rain for your wedding, to finding out what the climate is like in Kazakhstan in April, Climatology allows you to discover the information you want.
Project details
Labs: Cambridge
Microsoft research developed a tool in collaboration with IUCN, SSC and UAL, to allow the rapid mapping and assessment of species, threats to species and conservation interventions. it is hoped that this tool will improve bottom up conservation monitoring.
Project details
Labs: Cambridge
Microsoft Research is looking for 20 high school students to participate in a study exploring existing and potentially new uses of social media and communication technologies to stay connected with friends and share experiences.
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Embedding professional services in productivity tools
Project details
Labs: FUSE Labs
Website for the CIKM2014 tutorial on Deep Learning for Natural Language Processing: Theory and Practice (more content to be added)
Project details
Labs: Redmond
Fast removal of general multipath for time-of-flight sensors.
Project details
Labs: ATL Israel
Project details
Labs: Redmond
JavaScript cryptographic algorithm implementations suitable for use in all cloud services
Project details
Labs: Redmond
Data compression is essential to large-scale data centers to save both storage and network bandwidth. Current software based method suffers from high computational cost with limited performance. In this project, we are migrating the fundamental workload of the computer system to FPGA accelerator, aiming high throughput performance and high energy efficiency, as well as freeing some CPU resources.
Project details
Labs: Redmond
Using the Internet as an (noisy) knowledgebase to mine semantics for multimedia data.
Project details
Labs: Redmond
Software-defined radios (SDR) have a potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Ziria addresses this problem. It consists of a novel programming language and an optimizing compiler. It is able to synthesize a very efficient SDR code from a high-level PHY description written in Ziria language.
Project details
Labs: Cambridge
An app that lets people check-in to the commuting trips that they take, and communicate with their fellow travelers. The app for the locations we pass through on the way to where we're going.
Project details
Labs: FUSE Labs
Tetris is a cluster scheduler that packs, i.e., matches multi-resource task requirements with resource availabilities of machines. It allows cluster operators to trade-off cluster efficiency (makespan) for job completion time as well as performance for fairness.
Project details
Labs: Redmond
This paper presents a method for acquiring dense nonrigid shape and deformation from a single monocular depth sensor. We focus on modeling the human hand, and assume that a single rough template model is available. We combine and extend existing work on model-based tracking, subdivision surface fitting, and mesh deformation to acquire detailed hand models from as few as 15 frames of depth data.
Project details
Labs: Cambridge
We introduce an efficient camera relocalization approach which can be easily integrated into real-time 3D reconstruction methods, such as KinectFusion. Our approach makes use of compact encoding of whole image frames which enables both online harvesting of keyframes in tracking mode, and fast retrieval of pose proposals when tracking is lost. The encoding scheme is based on randomized ferns and simple binary feature tests.
Project details
Labs: Cambridge
Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure.
Project details
Labs: Cambridge
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